US20220381143A1

US20220381143A1 - Event detection using dts features

Info

Publication number: US20220381143A1
Application number: US17/767,133
Authority: US
Inventors: Cagri CERRAHOGLU; Pradyumna` THIRUVENKATANATHAN; James Crofton RAMSAY
Original assignee: Lytt Ltd
Current assignee: Lytt Ltd
Priority date: 2019-10-17
Filing date: 2019-12-16
Publication date: 2022-12-01
Also published as: CA3154435A1; WO2021073740A1; US20210115785A1; US11098576B2; WO2021073763A1; CA3154435C; EP4045764A1

Abstract

A method of detecting one or more events comprises determining a plurality of temperature features from a temperature sensing signal, using the plurality of temperature features in an event detection model, and determining the presence or absence of the one or more events at one or more locations based on an output from the event detection model.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 35 U.S.C. § 371 national stage application of PCT/EP2019/085454 filed Dec. 16, 2019, entitled “Event Detection Using DTS Features,” which claims priority to PCT/EP2019/078195 filed Oct. 17, 2019, entitled “Inflow Detection Using DTS Features,” each of which is hereby incorporated herein by reference in its entirety for all purposes..

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

It can be desirable to detect events that provide a temperature signature. For example, to obtain hydrocarbons from subterranean formations, wellbores are drilled from the surface to access the hydrocarbon-bearing formation. After drilling a wellbore to the desired depth, a production string is installed in the wellbore to produce the hydrocarbons from one or more production zones of the formation to the surface. The production of the fluids can be detected at the wellhead based on total flow of fluid. However, it can be difficult to determine a fluid inflow event, for example, where the fluid is inflowing into the wellbore when multiple production zones are present.

BRIEF SUMMARY

In some embodiments, a method of detecting one or more events comprises determining a plurality of temperature features from a distributed temperature sensing signal, using the plurality of temperature features in an event model, and determining the presence or absence of the one or more events based on an output from the event detection model.
In some embodiments, a method of detecting one or more events comprises determining a plurality of temperature features from a distributed temperature sensing signal, and determining the presence or absence of the one or more events using the plurality of temperature features, The plurality of temperature features can comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, a peak-to-peak value, an FFT of the distributed temperature sensing signal, a Laplace transform of the distributed temperature sensing signal, a wavelet transform of the distributed temperature sensing signal or of the derivative of the distributed temperature sensing signal with respect to length (e.g., depth), a derivative of the distributed temperature sensing signal with respect to length (e.g., depth), a heat loss parameter, an autocorrelation of the distributed temperature sensing signal, or a combination thereof.
In some embodiments, a system of detecting one or more events comprises a processor, a memory, and an analysis program stored in the memory. The analysis program is configured, when executed on the processor, to receive a distributed temperature sensing signal, determine a plurality of temperature features from the distributed temperature sensing signal, use the plurality of temperature features in an event detection model, and determine the presence or absence of the one or more events based on an output from the event detection model.
Embodiments described herein comprise a combination of features and characteristics intended to address various shortcomings associated with certain prior devices, systems, and methods. The foregoing has outlined rather broadly the features and technical characteristics of the disclosed embodiments in order that the detailed description that follows may be better understood. The various characteristics and features described above, as well as others, will be readily apparent to those skilled in the art upon reading the following detailed description, and by referring to the accompanying drawings. It should be appreciated that the conception and the specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes as the disclosed embodiments, It should also be realized that such equivalent constructions do not depart from the spirit and scope of the principles disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various exemplary embodiments, reference will now be made to the accompanying drawings in which:

FIG. 1 is a schematic, cross-sectional illustration of a downhole wellbore environment according to some embodiments;

FIG. 2 is a schematic, cross-sectional views of embodiments of a well with a wellbore tubular having an optical fiber inserted therein according to some embodiments;

FIG. 3 is a schematic, cross-sectional views of embodiments of a well with a wellbore tubular having an optical fiber inserted therein according to some embodiments;

FIG. 4 is a flow chart of a method for detecting one or more events, according to some embodiments; and

FIG. 5 is a flow diagram of a method of determining temperature features, according to some embodiments; and

FIG. 6 schematically illustrates a computer that may be used to carry out various methods according to some embodiments.

DETAILED DESCRIPTION

The following discussion is directed to various exemplary embodiments, However, one of ordinary skill in the art will understand that the examples disclosed herein have broad application, and that the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment.
The drawing figures are not necessarily to scale. Certain features and components herein may be shown exaggerated in scale or in somewhat schematic form and some details of conventional elements may not be shown in interest of clarity and conciseness.
Unless otherwise specified, any use of any form of the terms “connect,” “engage,” “couple,” “attach,” or any other term describing an interaction between elements is not meant to limit the interaction to direct interaction between the elements and may also include indirect interaction between the elements described. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . ”. Reference to up or down will be made for purposes of description with “up,” “upper,” “upward,” “upstream,” or “above” meaning toward the surface of the wellbore and with “down,” “lower,” “downward,” “downstream,” or “below” meaning toward the terminal end of the well, regardless of the wellbore orientation. Reference to inner or outer will be made for purposes of description with “in,” “inner,” or “inward” meaning towards the central longitudinal axis of the wellbore and/or wellbore tubular, and “out,” “outer,” or “outward” meaning towards the wellbore wall. As used herein, the term “longitudinal” or “longitudinally” refers to an axis substantially aligned with the central axis of the wellbore tubular, and “radial” or “radially” refer to a direction perpendicular to the longitudinal axis. The various characteristics mentioned above, as well as other features and characteristics described in more detail below, will be readily apparent to those skilled in the art with the aid of this disclosure upon reading the following detailed description of the embodiments, and by referring to the accompanying drawings.
As utilized herein, a ‘fluid inflow event’ includes fluid inflow (e.g., any fluid inflow regardless of composition thereof), gas phase inflow, aqueous phase inflow, and/or hydrocarbon phase inflow. The fluid can comprise other components such as solid particulate matter (e.g., sand, etc.) in some embodiments, as discussed in more detail herein.

Overview

Disclosed herein are systems and methods for detecting events and/or characterizing event locations, for example, within a subterranean wellbore, so that a wellbore operator may more effectively control the fluid production from the wellbore. The systems allow for an identification of the events at the event location(s) using temperature features derived from temperature measurements.
The new signal processing architecture disclosed herein allows for the identification of various events. In some embodiments, the events can occur within a wellbore such as fluid inflow event (e.g., including fluid inflow detection, fluid inflow location determination, fluid inflow quantification, fluid inflow discrimination, etc.), fluid outflow detection (e.g., fluid outflow detection, fluid outflow quantification), fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, including in-well leak detection (e.g., downhole casing and tubing leak detection, leaking fluid phase identification, etc.), flow assurance (e.g., wax deposition), annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in the overburden (e.g., micro-seismic events, etc.), sand detection (e.g., sand ingress, sand flows, etc.), and the like, each in real time or near real time in some embodiments. Other events can also be detected such as security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, dam monitoring events, and the like. As utilized herein, “fluid flow discrimination” indicates an identification and/or assignment of the detected fluid flow (e.g., single phase flow, mixed phase flows, time-based slugging, altering fluid flows, etc.), gas inflow, hydrocarbon liquid (e.g., ‘oil’) inflow, and/or aqueous phase (e.g., water) inflow, including any combined or multiphase flows or inflows. The methods of this disclosure can thus be utilized, in embodiments, to provide information on various events such as a fluid ingress and/or a fluid ingress point or location as well as flow regimes within a conduit rather than simply a location at which gas, water, or hydrocarbon liquid is present in the wellbore tubular (e.g., present in a flowing fluid), which can occur at any point above the ingress location as the fluid flows to the surface of the wellbore. In some embodiments, the system allows for a quantitative measurement of various fluid flows such as a relative concentration of in-well hydrocarbon liquid, water, and gas.
In some instances, the systems and methods can provide information in real time or near real time. As used herein, the term “real time” refers to a time that takes into account various communication and latency delays within a system, and can include actions taken within about ten seconds, within about thirty seconds, within about a minute, within about five minutes, or within about ten minutes of the action occurring. Various sensors (e.g., distributed temperature sensing sensors, etc.) can be used to obtain a distributed temperature signal at various points along a length, for example, along a wellbore. The distributed temperature sensing signal can then be processed using signal processing architecture with various feature extraction techniques (e.g., temperature feature extraction techniques) to obtain a measure of one or more temperature features and/or combinations thereof that enable selectively extracting the distributed temperature sensing signals of interest from background noise and consequently aiding in improving the accuracy of the identification of events, including, for example, the movement of fluids (e.g., gas inflow locations, water inflow locations, hydrocarbon liquid inflow locations, etc.) in real time. While discussed in terms of being real time in some instances, the data can also be analyzed at a later time at the same location and/or a displaced location.
The signal processing techniques described herein can also help to address the big-data problem through intelligent extraction of data (rather than crude decimation techniques) to considerably reduce real time data volumes at the collection and processing site (e.g., by over 100 times, over 500 times, or over 1000 times, or over 10,000 times reduction, in some embodiments).
In some embodiments, the distributed temperature sensing signal(s) can be obtained in a manner that allows for a signal to be obtained along a length, for example, an entire wellbore or a portion of interest (e.g., a depth) thereof. Production logging systems utilize a production logging system (PLS) to determine flow profile in wells. However, since the PLS can be 10-20 meters long and the sensors are distributed along the length, sensors that are not at the front of the PLS are not actually taking measurements at the depth for which the measurements are recorded, and, thus, the data can be incorrect or incomplete over time. Furthermore, the flow can be altered by the mere presence of the PLS within the wellbore, so what is measured at the downstream end of the PLS is not an accurate reflection of what the profile/regime was before the tool disturbed the flow. Furthermore, as a PLS is typically run through a well once or a few times (down and then up once or a few times and out), and the sensors are exposed to the conditions at a given depth for only a very brief period of time (e.g., 4-5 seconds). Accordingly, while PLSs can provide an indication that certain events, such as downhole water inflow, may be occurring, they do not provide continuous measurements over prolonged durations of time that would be needed to study dynamic variabilities in production profiles over time.
Fiber optic distributed temperature sensors (DTS) can capture distributed temperature sensing signals resulting from downhole events, such as wellbore events (e.g., gas inflow/flow, hydrocarbon liquid inflow/flow, water inflow/flow, mixed flow, leaks, overburden movement, and the like), as well as other background events. DTS can also be used to capture distributed temperature sensing signals from events, such as security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, dam monitoring events, and the like. This allows for signal processing procedures that distinguish events and flow signals from other sources to properly identify each type of event. This in turn results in a need for a clearer understanding of the fingerprint of in-well event of interest (e.g., fluid inflow, water inflow, gas inflow, hydrocarbon liquid inflow, fluid flow along the tubulars, etc.) in order to be able to segregate and identify a signal resulting from an event of interest from other ambient background signals. As used herein, the resulting fingerprint of a particular event can also be referred to as an event signature, as described in more detail herein. In some embodiments, the temperature features can be used with a model (e.g., a machine learning model, multivariate model, etc.) to provide for detection, identification, and determination of the various events. A number of different models can be developed and used to determine when certain events have occurred, for example, within a wellbore.
The ability to identify various events (e.g., in a wellbore) may allow for various actions or processes to be taken in response to the events. For example, reducing deferrals resulting from one or more events such as water ingress and facilitating effective remediation relies upon accurate and timely decision support to inform the operator of the events. As another example, with respect to events within a wellbore, a well can be shut in, production can be increased or decreased, and/or remedial measures can be taken in the wellbore, as appropriate based on the identified event(s). An effective response, when needed, benefits not just from a binary yes/no output of an identification/detection of in-well events but also from a measure of relative amount of fluids (e.g., amount of gas inflow, amount of hydrocarbon liquid inflow, amount of water inflow, etc.) from each of the identified zones of events so that zones contributing the greatest fluid amount(s) can be acted upon first to improve or optimize production. The systems and methods described herein can be used, in applications, to identify the source of an event or problem, as well as additional information about the event, such as a direction and amount of flow, and/or an identification of the type of problem being faced. For example, when an event comprising water inflow and a location thereof are detected, a relative flow rate of the hydrocarbon liquid at the water inflow location may allow for a determination of whether or not to remediate, the type or method of remediation, the timing for remediation, and/or deciding to alter (e.g., reduce) a production rate from the well. For example, production zones can be isolated, production assemblies can be open, closed, or choked at various levels, side wells can be drilled or isolated, and the like. Such determinations can be used to improve on the drawdown of the well while reducing the production expenses associated with various factors such as produced water.
The same signal processing described herein can be used to identify various events across a variety of industries. The systems can comprise similar real time signal processing architecture that allows for the identification of events using various signatures or models. Within these systems, various sensors (e.g., distributed temperature sensors, and/or point temperature sensors, etc.) can be used to obtain a sampling at various points along a path or length. The distributed temperature sensing signal can then be processed using signal processing architecture with temperature feature extraction techniques to obtain temperature features that enable selectively extracting the signals of interest from background noise.
Once obtained, the temperature features can be used in various models in order to be able to segregate a noise resulting from an event of interest from other ambient background noise. Specific models can be determined for each event by considering one or more temperature features for known events, From these known events, the temperature features specific to each event can be developed and signatures (e.g., having ranges or thresholds) and/or models can be established to determine a presence (or absence) of each event. Based on the specifics of each temperature feature, the resulting signatures or models can be used to sufficiently distinguish between events to allow for a relatively fast identification of such events. The resulting signatures or models can then be used along with processed distributed temperature sensing signal data to determine if an event is occurring at a point of interest along the path of the temperature sensor(s). Any of the processing techniques disclosed herein can be used to initially determine a signature or model(s), and then process and compare the temperature features in a sampled temperature sensing signal with the resulting signatures or model(s).
Thus, temperature signals in industries such as security (e.g., security, pipeline monitoring, etc.), energy (e.g., geothermal, etc.), transportation (e.g., railway monitoring, roadway monitoring, etc.), and facilities monitoring (e.g., monitoring equipment such as electric submersible pumps, wind turbines, compressors, dams, etc.) can benefit from the use of the systems and methods disclosed herein. For example, a pipeline can be monitored to detect temperature signals along the length of a pipeline, using for example, a fiber attached to the pipeline, along with a DTS unit. The length of the fiber along the pipeline can be considered a path of the fiber as it passes from the receiver/generator (e.g., the DTS unit) along the pipeline. Various temperature signatures can be detected based on temperature sensing signals originating along the length of the pipeline and/or fiber. These signals can be processed to extract one or more temperature features, and signatures and/or model(s) of such events can be determined or developed. Once obtained, the signatures and/or model(s) can be used to process distributed temperature sensing signals at various lengths along the path of the fiber and determine the presence or absence of the various events using the temperature features and the signatures and/or model(s).
Similarly, the temperature monitoring techniques described herein can be used with one or more point sources, which can be individual or connected along a path. For example, a facility having industrial equipment can be monitored using the temperature monitoring techniques described herein. For example, a facility having pumps, turbines, compressors, or other equipment can have a temperature sensor(s) monitoring the piece of equipment. Signatures and/or model(s) of various events can be determined for each type of equipment and used to monitor and identify the state of the equipment. For example, a pump can be monitored to determine if the pump is active or inactive through the use of a temperature signal and the characteristics and/or model(s) determining the presence of an event as described herein. When multiple piece of equipment are present, a single distributed temperature sensor, such as a fiber, can be coupled to each piece of equipment. This configuration may allow a single interrogation unit to monitor multiple pieces of equipment using the analysis by resolving a length along the fiber for each piece of equipment. Thus, a distributed temperature monitoring system may not require multiple processors correlating to individual pieces of equipment.
Similarly, pipelines can be monitored in a manner similar to the way the wellbores are monitored as disclosed herein. In this embodiment, the fiber may detect various events such as leaks, flow over a blockage or corrosion, and the like. This may allow for remote monitoring along the length of a pipeline.
Other types of industries can also benefit from the use of temperature sensing to obtain temperature signals that can be analyzed and matched to events using temperature feature extraction as described hereinbelow. Any industry that experiences events that create temperature signals can be monitored using the systems and methods as described herein. Further, when the signals are distributed across space, a single temperature sensor, such as an optical fiber, can be used with a receiver unit to detect temperature signals across the length or path of the sensor element, thereby enabling a single sensor to detect temperature sensing signals across a wide area or path. In some embodiments, a point sensor such as a temperature thermocouple can be used to obtain a temperature from a location and used with the processes described herein to detect an event. In these embodiments, the signal may not be obtained from a wellbore. For example, the temperature signal may be obtained from a non-wellbore source or from outside of a subterranean formation. Thus, the systems and processing techniques described herein can be used to identify events using temperature features obtained from temperature sensing signals across a variety of industries and locations.
Herein described are methods and systems for detecting (e.g., identifying) events (e.g., wellbore events, security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, dam monitoring events, etc.). In some embodiments, the wellbore events can comprise fluid inflow locations and/or fluid flow regimes within a conduit in the wellbore. In some embodiments, other wellbore events such as fluid outflow detection, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, sand detection (e.g., sand ingress, sand flows, etc.), and the like can be detected. In some embodiments, events such as security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, dam monitoring events, and the like can be detected. For example, temperature signals can be used in security monitoring to detect open doorways, gaps or holes in a perimeter, or the like. Similarly, temperature signals can be detected on railways to determine the presence of trains through friction based heating, and ice can be detected on railways or roadways through the use of the temperature signals. Geothermal events, carbon capture events, and CO₂injection events can be similar to wellbore events with respect to geothermal fluid inflow, outflows, leaks, and the like. Pipelines can also be monitored for flow, leaks, blockages, and the like.
As described herein, temperature features can be used with DTS temperature data processing to provide for event detection. For example, the temperature features can be used with wellbore event detection (e.g., fluid profiling, fluid inflow location detection, fluid phase discrimination such as the determination that the fluid at one or more locations such as the detected fluid inflow location comprises gas inflow, hydrocarbon liquid inflow, aqueous phase inflow, a combined fluid flow, and/or a time varying fluid flow such as slugging single or multiphase flow, and the like). In some embodiments, an event detection model can be used for event identification. The event detection model can comprise one or more individual models, which can be the same or different as described in more detail herein. In some embodiments, the event detection model can comprise a plurality of sub-models such as a fluid flow model used for inflow fluid phase discrimination, which can allow for the determination of at least one of a gas phase inflow, an aqueous phase inflow, a hydrocarbon liquid phase inflow, and various combinational flow regimes in a wellbore. In some embodiments, the same or a different event detection models can be used to identify other events such as fluid flow phase discrimination to determine the composition of fluid flowing in a conduit. Suitable event detection model(s) can be developed for any of the events described herein.
Application of the signal processing techniques and one or more event detection models with DTS for wellbore events such as downhole surveillance can provide a number of benefits including improving reservoir recovery by monitoring efficient drainage of reserves through downhole fluid surveillance (e.g., production flow monitoring), improving well operating envelopes through identification of drawdown levels (e.g., gas, water, etc.), facilitating targeted remedial action for efficient well management and well integrity, reducing operational risk through the clear identification of anomalies and/or failures in well barrier elements. Similar advantages are also possible with other non-wellbore events.
In some embodiments, use of the systems and methods described herein may provide knowledge of the events and the locations experiencing various events, thereby potentially allowing for improved actions (e.g., remediation actions for wellbore events, security actions for security events, etc.) based on the processing results. The methods and systems disclosed herein can also provide information on the events, For example, for wellbore events, information about a variability of the amount of fluid inflow being produced by the different fluid influx zones as a function of different production rates, different production chokes, and downhole pressure conditions can be determined, thereby enabling control of fluid inflow. For fluid inflow events, embodiments of the systems and methods disclosed herein also allow fora computation of the relative concentrations of fluid ingress (e.g., relative amounts of gas, hydrocarbon liquid, and water in the inflow fluid) into the wellbore, thereby offering the potential for more targeted and effective remediation.
As disclosed herein, embodiments of the data processing techniques can use various sequences of real time digital signal processing steps to identify the temperature signal resulting from various events from background noise, and allow real time detection of the events and their locations using distributed fiber optic temperature sensor data as the input data feed.
One or more models can be developed using test data to provide a labeled data set used as input into the event detection model. The resulting trained models can then be used to identify one or more signatures based on features of the test data and one or more machine learning techniques to develop correlations for the presence of various events. In the event detection model development, specific events can be created in a test set-up, and the temperature signals can be obtained and recorded to develop test data. The test data can be used to train one or more models defining the various events. The resulting model can then be used to determine one or more events. In some embodiments, actual field data can be used and correlated to actual events using inputs from other temperature sensors. The data can be labeled to create a training data set based on actual production situations. The data can then be used alone or in combination with the test data to develop the model(s).
As described herein, wellbore events are used as an example. However, as noted above, other events and event detection model(s) for the other events are also within the scope of this disclosure. As described herein, the methods and systems can be used to identify one or more events. Various events can be determined using the method, such wellbore events including, without limitation, fluid outflow detection, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in well leak detection, annular fluid flow diagnosis, overburden monitoring, fluid flow detection behind a casing, wax deposition events, sand detection (e.g., sand ingress, sand flows, etc.), security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, darn monitoring events, and the like. Fluid flow can comprise fluid flow along or within a tubular within a wellbore such as fluid flow within a production tubular. Fluid flow can also comprise fluid flow from the reservoir or formation into a wellbore tubular. Such flow into the wellbore and/or a wellbore tubular can be referred to as fluid inflow. While fluid inflow may be separately identified at times in this disclosure, such fluid inflow is considered a part of fluid flow within the wellbore.
In some embodiments, temperature features can be determined from temperature measurements taken along a length, for example, a length of a wellbore. In some embodiments, the temperature measurements can be used with one or more temperature signatures to determine the presence of absence of an event. The signatures can comprise a number of thresholds or ranges for comparison with various temperature features. When the detected temperature features fall within the signatures, the event may be determined to be present. In some embodiments, the temperature measurements can be used in an event detection model that can provide an output indicative of the presence or absence of one or more events (and optionally also one or more event locations) along the length (e.g., along the length of the wellbore). This can allow events to be identified using temperature based measurements (e.g., from the wellbore). When combined with a distributed temperature sensing system that can provide distributed and continuous temperature measurements, the systems can allow for fluid inflow locations to be tracked through time.

DTS System

A DTS system of this disclosure will now be described with reference to a wellbore. As noted above, a DTS system of this disclosure can be applied in non-wellbore applications, and the following wellbore description should not be limiting. Referring now to FIG. 1 , a schematic, cross-sectional illustration of a downhole wellbore operating environment 101 according to some embodiments is shown. More specifically, environment 101 includes a wellbore 114 traversing a subterranean formation 102, casing 112 lining at least a portion of wellbore 114, and a tubular 120 extending through wellbore 114 and casing 112. A plurality of completion assemblies such as spaced screen elements or assemblies 118 may be provided along tubular 120 at one or more production zones 104 a, 104 b within the subterranean formation 102. In particular, two production zones 104 a, 104 b are depicted within subterranean formation 102 of FIG. 1 ; however, the precise number and spacing of the production zones 104 a, 104 b may be varied in different embodiments. The completion assemblies can comprise flow control devices such as sliding sleeves, adjustable chokes, and/or inflow control devices to allow for control of the flow from each production zone. The production zones 104 a, 104 b may be layers, zones, or strata of formation 102 that contain hydrocarbon fluids (e.g., oil, gas, condensate, etc.) therein.
In addition, a plurality of spaced zonal isolation devices 117 and gravel packs 122 may be provided between tubular 120 and the sidewall of wellbore 114 at or along the interface of the wellbore 114 with the production zones 104 a, 104 b. In some embodiments, the operating environment 101 includes a workover and/or drilling rig positioned at the surface and extending over the wellbore 114. While FIG. 1 shows an example completion configuration in FIG. 1 , it should be appreciated that other configurations and equipment may be present in place of or in addition to the illustrated configurations and equipment. For example, sections of the wellbore 114 can be completed as open hole completions or with gravel packs without completion assemblies.
In general, the wellbore 114 can be formed in the subterranean formation 102 using any suitable technique (e.g., drilling). The wellbore 114 can extend substantially vertically from the earth's surface over a vertical wellbore portion, deviate from vertical relative to the earth's surface over a deviated wellbore portion, and/or transition to a horizontal wellbore portion. In general, all or portions of a wellbore may be vertical, deviated at any suitable angle, horizontal, and/or curved. In addition, the wellbore 114 can be a new wellbore, an existing wellbore, a straight wellbore, an extended reach wellbore, a sidetracked wellbore, a multi-lateral wellbore, and other types of wellbores for drilling and completing one or more production zones. As illustrated, the wellbore 114 includes a substantially vertical producing section 150 which includes the production zones 104 a, 104 b. In this embodiment, producing section 150 is an open-hole completion (i.e., casing 112 does not extend through producing section 150). Although section 150 is illustrated as a vertical and open-hole portion of wellbore 114 in FIG. 1 , embodiments disclosed herein can be employed in sections of wellbores having any orientation, and in open or cased sections of wellbores. The casing 112 extends into the wellbore 114 from the surface and can be secured within the wellbore 114 with cement 111.
The tubular 120 may comprise any suitable downhole tubular or tubular string (e.g., drill string, casing, liner, jointed tubing, and/or coiled tubing, etc.), and may be inserted within wellbore 114 for any suitable operation(s) (e.g., drilling, completion, intervention, workover, treatment, production, etc.). In the embodiment shown in FIG. 1 the tubular 120 is a completion assembly string, In addition, the tubular 120 may be disposed within any or all portions of the wellbore 114 (e.g., vertical, deviated, horizontal, and/or curved section of wellbore 114).
In this embodiment, the tubular 120 extends from the surface to the production zones 104 a, 104 b and generally provides a conduit for fluids to travel from the formation 102 (particularly from production zones 104 a, 104 b) to the surface. A completion assembly including the tubular 120 can include a variety of other equipment or downhole tools to facilitate the production of the formation fluids from the production zones. For example, zonal isolation devices 117 can be used to isolate the production zones 104 a, 104 b within the wellbore 114. In this embodiment, each zonal isolation device 117 comprises a packer (e.g., production packer, gravel pack packer, frac-pac packer, etc.). The zonal isolation devices 117 can be positioned between the screen assemblies 118, for example, to isolate different gravel pack zones or intervals along the wellbore 114 from each other. In general, the space between each pair of adjacent zonal isolation devices 117 defines a production interval, and each production interval may correspond with one of the production zones 104 a, 104 b of subterranean formation 102.
The screen assemblies 118 provide sand control capability. In particular, the sand control screen elements 118, or other filter media associated with wellbore tubular 120, can be designed to allow fluids to flow therethrough but restrict and/or prevent particulate matter of sufficient size from flowing therethrough. The screen assemblies 118 can be of any suitable type such as the type known as “wire-wrapped”, which are made up of a wire closely wrapped helically about a wellbore tubular, with a spacing between the wire wraps being chosen to allow fluid flow through the filter media while keeping particulates that are greater than a selected size from passing between the wire wraps. Other types of filter media can also be provided along the tubular 120 and can include any type of structures commonly used in gravel pack well completions, which permit the flow of fluids through the filter or screen while restricting and/or blocking the flow of particulates (e.g. other commercially-available screens, slotted or perforated liners or pipes; sintered-metal screens; sintered-sized, mesh screens; screened pipes; prepacked screens and/or liners; or combinations thereof). A protective outer shroud having a plurality of perforations therethrough may be positioned around the exterior of any such filter medium.
The gravel packs 122 can be formed in the annulus 119 between the screen elements 118 (or tubular 120) and the sidewall of the wellbore 114 in an open hole completion. In general, the gravel packs 122 comprise relatively coarse granular material placed in the annulus 119 to form a rough screen against the ingress of sand into the wellbore 114 while also supporting the wellbore wall. The gravel pack 122 is optional and may not be present in all completions.
In some embodiments, one or more of the completion assemblies can comprise flow control elements such as sliding sleeves, chokes, valves, or other types of flow control devices that can control the flow of a fluid from an individual production zone or a group of production zones (e.g., production zones 104 a/104 b ). The force on the production face can then vary based on the type of completion within the wellbore 114 and/or each production zone 104 a/104 b (e.g., in a sliding sleeve completion, open hole completion, gravel pack completion, etc.). In some embodiments, a sliding sleeve or other flow controlled production zone can experience a force on the production face that is relatively uniform within the production zone (e.g., production zone 104 a/104 b ), and the force on the production face can be different between each production zone (e.g., production zone 104 a/104 b). For example, a first production zone (e.g., production zone 104 a) can have a specific flow control setting that allows the production rate from the first production zone (e.g., production zone 104) to be different than the production rate from a second production zone (e.g., production zone 104 b). Thus, the choice of completion type (e.g., which can be specified in a completion plan) can have an affect on the need for or the ability to provide a different production rate within different production zones (e.g., production zones 104 a/104 b).
Referring still to FIG. 1 , a monitoring system 110 can comprise a temperature monitoring system. The monitoring system 110 can be positioned in the wellbore 114. As described herein, the monitoring system 110 may be utilized to detect various events in the wellbore 114. The monitoring system (e.g., the temperature monitoring systems) may be referred to herein as an “event detection system,” an “event monitoring system, or simply a “monitoring system”.
In some embodiments, the monitoring system 110 can comprise an optical fiber 162 that is coupled to and extends along tubular 120. In cased completions, the optical fiber 162 can be installed between the casing 112 and the wellbore wall within a cement layer 111 and/or installed within the casing 112 or production tubing 120. Referring briefly to FIGS. 2 and 3 , optical fiber 162 of the monitoring system 110 may be coupled to an exterior of tubular 120 (e.g., such as shown in FIG. 3 ) or an interior of tubular 120 (e.g., such as shown in FIG. 2 ). When the optical fiber 162 is coupled to the exterior of the tubular 120, as depicted in the embodiment of FIG. 3 , the optical fiber 162 can be positioned within a control line, control channel, or recess in the tubular 120. In some embodiments an outer shroud contains the tubular 120 and protects the optical fiber 162 during installation. A control line or channel can be formed in the shroud and the optical fiber 162 can be placed in the control line or channel (not specifically shown in FIGS. 2 and 3 ).
Referring again to FIG. 1 , generally speaking, during operation of a the monitoring system 110, an optical backscatter component of light injected into the optical fiber 162 may be used to detect various conditions incident on the optical fiber 162 such as acoustic perturbations (e.g., dynamic strain), temperature, static strain, and the like along the length of the optical fiber 162. The light can be generated by a light generator or source 166 such as a laser, which can generate light pulses. The light used in the monitoring system 110 is not limited to the visible spectrum, and light of any frequency can be used with the monitoring systems 110 described herein. Accordingly, the optical fiber 162 acts as the sensor element with no additional transducers in the optical path, and measurements can be taken along the length of the entire optical fiber 162. The measurements can then be detected by an optical receiver such as sensor 164 and selectively filtered to obtain measurements from a given depth point or range, thereby providing for a distributed measurement that has selective data for a plurality of zones (e.g., production zones 104 a, 104 b) along the optical fiber 162 at any given time. For example, time of flight measurements of the backscattered light can be used to identify individual zones or measurement lengths of the optical fiber 162. In this manner, the optical fiber 162 effectively functions as a distributed array of sensors spread over the entire length of the optical fiber 162, which typically extends across at least production zones 104 a, 104 b within the wellbore 114.
The light backscattered up the optical fiber 162 as a result of the optical backscatter can travel back to the light generator or source 166, where the signal can be collected by a sensor 164 and processed (e.g., using a processor 168). In general, the time the light takes to return to the collection point is proportional to the distance traveled along the optical fiber 162, thereby allowing time of flight measurements of distance along the optical fiber 162. The resulting backscattered light arising along the length of the optical fiber 162 can be used to characterize the environment around the optical fiber 162. The use of a controlled light generator or source 166 (e.g., having a controlled spectral width and frequency) may allow the backscatter to be collected and any parameters and/or disturbances along the length of the optical fiber 162 to be analyzed. In general, the various parameters and/or disturbances along the length of the optical fiber 162 can result in a change in the properties of the backscattered light.
An acquisition device 160 may be coupled to one end of the optical fiber 162 that comprises the sensor 164, light generator or source 166, a processor 168, and a memory 170. As discussed herein, the light generator or source 166 can generate the light (e.g., one or more light pulses), and the sensor 164 can collect and analyze the backscattered light returning up the optical fiber 162. In some contexts, the acquisition device 160 (which can comprise the light generator or source 166 and the sensor 164 as noted above), can be referred to as an interrogator. The processor 168 may be in signal communication with the sensor 164 and may perform various analysis steps described in more detail herein. While shown as being within the acquisition device 160, the processor 168 can also be located outside of the acquisition device 160 including being located remotely from the acquisition device 160. The sensor 164 can be used to obtain data at various rates and may obtain data at a sufficient rate to detect the distributed temperature sensing signals of interest with sufficient bandwidth. While described as a sensor 164 in a singular sense, the sensor 164 can comprise one or more photodetectors or other sensors that can allow one or more light beams and/or backscattered light to be detected for further processing. In an embodiment, depth resolution ranges in a range of from about 1 meter to about 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved. Depending on the resolution needed, larger averages or ranges can be used for computing purposes. When a high depth resolution is not needed, a system may have a wider resolution (e.g., which may be less expensive) can also be used in some embodiments. Data acquired by the monitoring system 110 (e.g., via fiber 162, sensor 164, etc.) may be stored on memory 170.
The monitoring system 110 can be used for detecting a variety of parameters and/or disturbances in the wellbore, in addition to detecting temperatures along the wellbore, for example, the monitoring system can also be operable to detect acoustic signals along the wellbore, static strain and/or pressure along the wellbore, or any combination thereof.
The event monitoring system 110 can be used to detect temperatures within the wellbore 114. The temperature monitoring system 110 can include a distributed temperature sensing (DTS) system. A DTS system can rely on light injected into the optical fiber 162 along with the reflected signals to determine a temperature and/or strain based on optical time-domain reflectometry. In order to obtain DTS measurements, a pulsed laser from the light generator or source 166 can be coupled to the optical fiber 162 that serves as the sensing element. The injected light can be backscattered as the pulse propagates through the optical fiber 162 owing to density and composition as well as to molecular and bulk vibrations. A portion of the backscattered light can be guided back to the acquisition device 160 and split by a directional coupler to sensor 164. It is expected that the intensity of the backscattered light decays exponentially with time, As the speed of light within the optical fiber 162 is known, the distance that the light has passed through the optical fiber 162 can be derived using time of flight measurements.
In both distributed acoustic sensing (DAS) and DTS systems, the backscattered light includes different spectral components which contain peaks that are known as Rayleigh and Brillouin peaks and Raman bands. The Rayleigh peaks are independent of temperature and can be used to determine the DAS components of the backscattered light. The Raman spectral bands are caused by thermally influenced molecular vibrations. The Raman spectral bands can then be used to obtain information about distribution of temperature along the length of the optical fiber 162 disposed in the wellbore.
The Raman backscattered light has two components, Stokes and Anti-Stokes, one being only weakly dependent on temperature and the other being greatly influenced by temperature. The relative intensities between the Stokes and Anti-Stokes components are a function of temperature at which the backscattering occurred. Therefore, temperature can be determined at any point along the length of the optical fiber 162 by comparing at each point the Stokes and Anti-stokes components of the light backscattered from the particular point. The Brillouin peaks may be used to monitor strain along the length of the optical fiber 162.
The DTS system can then be used to provide a temperature measurement along the length of the wellbore 114 during the production of fluids, including various events such as fluid inflow events. The DTS system can represent a separate system from the DAS system or a single common system, which can comprise one or more acquisition devices in some embodiments. In some embodiments, a plurality of optical fibers 162 are present within the wellbore 114, and a DAS system can be coupled to a first optical fiber and the DTS monitoring system 110 can be coupled to a second, different, optical fiber 162. Alternatively, a single optical fiber 162 can be used with both monitoring systems 110, and a time division multiplexing or other process can be used to measure both DAS and DTS on the same optical fiber 162.
In an embodiment, depth resolution for the DTS system can range from about 1 meter to about 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved. Depending on the resolution needed, larger averages or ranges can be used for computing purposes. When a high depth resolution is not needed, a system having a wider resolution (e.g., which may be less expensive) can also be used, in some embodiments. Data acquired by the DTS system 110 (e.g., via fiber 162, sensor 164, etc.) may be stored on memory 170.
While the temperature monitoring system described herein can use a DTS system to acquire the temperature measurements for a location or depth range in the wellbore 114, in general, any suitable temperature monitoring system can be used. For example, various point sensors, thermocouples, resistive temperature sensors, or other sensors can be used to provide temperature measurements at a given location based on the temperature measurement processing described herein. Further, an optical fiber 162 comprising a plurality of point sensors such as Bragg gratings can also be used. As described herein, a benefit of the use of the DTS system is that temperature measurements can be obtained across a plurality of locations and/or across a continuous length of the wellbore 114 rather than at discrete locations.
The monitoring system 110 can be used to generate temperature measurements along a length, e.g., of optical fiber 162, such as along a length of a wellbore 114. The resulting measurements can be processed to obtain various temperature features that can then be used to identify events, such as, without limitation, fluid inflow locations, identify inflowing fluid phases, and/or quantify the rate of fluid inflow. Each of the specific types of temperature features obtained from the monitoring system 110 are described in more detail hereinbelow.
For events including fluid inflow and/or fluid flow, fluid can be produced into the wellbore 114 and into the completion assembly string. During operations, the fluid flowing into the wellbore 114 may comprise hydrocarbon fluids, such as, for instance hydrocarbon liquids (e.g., oil), gases (e.g., natural gas such as methane, ethane, etc.), and/or water, any of which can also comprise particulates such as sand. However, the fluid flowing into the tubular 120 may also comprise other components, such as, for instance steam, carbon dioxide, and/or various multiphase mixed flows, The fluid flow can further be time varying such as including slugging, bubbling, or time altering flow rates of different phases. The amounts or flow rates of these components can vary over time based on conditions within the formation 102 and the wellbore 114. Likewise, the composition of the fluid flowing into the tubular 120 sections throughout the length of the entire production string (e.g., including the amount of sand contained within the fluid flow) can vary significantly from section to section at any given time.
As the fluid enters the wellbore 114, the fluid can create temperature changes that can be detected by the event monitoring system 110, such as the DTS monitoring system 110, as described herein. With respect to the temperature variations, the temperature changes can result from various fluid effects within the wellbore 114 such as cooling based on gas entering the wellbore 114, temperature changes resulting from liquids entering the wellbore 114, and various flow related temperature changes as a result of the fluids passing through the wellbore 114. For example, as fluids enter the wellbore 114, the fluids can experience a sudden pressure drop, which can result in a change in the temperature. The magnitude of the temperature change depends on the phase and composition of the inflowing fluid, the pressure drop, and the pressure and temperature conditions. The other major thermodynamic process that takes place as the fluid enters the well is thermal mixing which results from the heat exchange between the fluid body that flows into the wellbore 114 and the fluid that is already flowing in the wellbore 114. As a result, inflow of fluids from the reservoir into the wellbore 114 can cause a deviation in the flowing well temperature profile.
By obtaining the temperature in the wellbore 114, a number of temperature features can be obtained from the temperature measurements. The temperature features can provide an indication of one or more temperature trends at a given location in the wellbore 114 during a measurement period. The resulting features can form a distribution of temperature results that can then be used with various models to identify one or more events within the wellbore 114 at the location.
The temperature measurements can represent output values from the DTS monitoring system 110, which can be used with or without various types of pre-processing such as noise reduction, smoothing, and the like. When background temperature measurements are used, the background measurement can represent a temperature measurement at a location within the wellbore 114 taken in the absence of the flow of a fluid. For example, a temperature profile along the wellbore 114 can be taken when the well is initially formed and/or the wellbore 114 can be shut in and allowed to equilibrate to some degree before measuring the temperatures at various points in the wellbore 114. The resulting background temperature measurements or temperature profile can then be used in determining the temperature features in some embodiments.
In general, the temperature features represent statistical variations of the temperature measurements through time and/or depth. For example, the temperature features can represent statistical measurements or functions of the temperature along the length (e.g., within the wellbore 114) that can be used with various models to determine whether or not an event (e.g., a fluid inflow event) has occurred. The temperature features can be determined using various functions and transformations, and in some embodiments can represent a distribution of results. In some embodiments, the temperature features can represent a normal or Gaussian distribution. The resulting distributions can then be used with models, such as, without limitation, multivariate models to determine the presence or absence of the event (e.g., fluid inflow event).
In some embodiments, the temperature features can include various features including, but not limited to, a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, a peak-to-peak value, a Fast Fourier transform (FFT), a Laplace transform, a wavelet transform, a derivative of temperature with respect to depth, a heat loss parameter, an autocorrelation, and combinations thereof.
In some embodiments, the temperature features can comprise a depth derivative of temperature with respect to depth. This feature can be determined by taking the temperature measurements along the wellbore and smoothing the measurements. Smoothing can comprise a variety of steps including filtering the results, de-noising the results, or the like. In some embodiments, the temperature measurements can be median filtered within a given window to smooth the measurements. Once smoothed, the change in the temperature with depth can be determined. In some embodiments, this can include taking a derivative of the temperature measurements with respect to depth along the longitudinal axis of the wellbore 114. The depth derivative of temperature values can then be processed, and the measurement with a zero value (e.g., representing a point of no change in temperature with depth) that have preceding and proceeding values that are non-zero and have opposite signs in depth (e.g., zero below which the value is negative and above positive or vice versa) can have the values assign to the nearest value. This can then result in a set of measurements representing the depth derivative of temperature with respect to depth.
In some embodiments, the temperature features can comprise a temperature excursion measurement. The temperature excursion measurement can comprise a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, where the first depth is within the depth range. In some embodiments, the temperature excursion measurement can represent a difference between de-trended temperature measurements over an interval and the actual temperature measurements within the interval. For example, a depth range can be selected within the wellbore 114. The temperature readings within a time window can be obtained within the depth range and de-trended or smoothed. In some embodiments, the de-trending or smoothing can include any of those processes described above, such as using median filtering of the data within a window within the depth range. For median filtering, the larger the window of values used, the greater the smoothing effect can be on the measurements. For the temperature excursion measurement, a range of windows from about 10 to about 100 values, or between about 20-60 values (e.g., measurements of temperature within the depth range) can be used to median filter the temperature measurements. A difference can then be taken between the temperature measurement at a location and the de-trended (e.g., median filtered) temperature values. The temperature measurements at a location can be within the depth range and the values being used for the median filtering. This temperature feature then represents a temperature excursion at a location along the wellbore 114 from a smoothed temperature measurement over a larger range of depths around the location in the wellbore 114.
In some embodiments, the temperature features can comprise a baseline temperature excursion. The baseline temperature excursion represents a difference between a de-trended baseline temperature profile and the current temperature at a given depth. In some embodiments, the baseline temperature excursion can rely on a baseline temperature profile that can contain or define the baseline temperatures along the length of the wellbore 114. As described herein, the baseline temperatures represent the temperature as measured when the wellbore 114 is shut in. This can represent a temperature profile of the formation in the absence of fluid flow. While the wellbore 114 may affect the baseline temperature readings, the baseline temperature profile can approximate a formation temperature profile. The baseline temperature profile can be determined when the wellbore 114 is shut in and/or during formation of the wellbore 114, and the resulting baseline temperature profile can be used over time. If the condition of the wellbore 114 changes over time, the wellbore 114 can be shut in and a new baseline temperature profile can be measured or determined. It is not expected that the baseline temperature profile is re-determined at specific intervals, and rather it would be determined at discrete times in the life of the wellbore 114. In some embodiments, the baseline temperature profile can be re-determined and used to determine one or more temperature features such as the baseline temperature excursion.
Once the baseline temperature profile is obtained, the baseline temperature measurements at a location in the wellbore 114 can be subtracted from the temperature measurement detected by the temperature monitoring system 110 at that location to provide baseline subtracted values. The results can then be obtained and smoothed or de-trended. For example, the resulting baseline subtracted values can be median filtered within a window to smooth the data. In some embodiments, a window between 10 and 500 temperature values, between 50 and 400 temperature values, or between 100 and 300 temperature values can be used to median filter the resulting baseline subtracted values. The resulting smoothed baseline subtracted values can then be processed to determine a change in the smoothed baseline subtracted values with depth. In some embodiments, this can include taking a derivative of the smoothed baseline subtracted values with respect to depth along the longitudinal axis of the wellbore. The resulting values can represent the baseline temperature excursion feature.
In some embodiments, the temperature features can comprise a peak-to-peak temperature value. This feature can represent the difference between the maximum and minimum values (e.g., the range, etc.) within the temperature profile along the wellbore 114. In some embodiments, the peak-to-peak temperature values can be determined by detecting the maximum temperature readings (e.g., the peaks) and the minimum temperature values (e.g., the dips) within the temperature profile along the wellbore 114. The difference can then be determined within the temperature profile to determine peak-to-peak values along the length of the wellbore 114. The resulting peak-to-peak values can then be processed to determine a change in the peak-to-peak values with respect to depth. In some embodiments, this can include taking a derivative of the peak-to-peak values with respect to depth along the longitudinal axis of the wellbore 114. The resulting values can represent the peak-to-peak temperature values.
Other temperature features can also be determined from the temperature measurements. In some embodiments, various statistical measurements can be obtained from the temperature measurements along the wellbore 114 to determine one or more temperature features. For example, a cross-correlation of the temperature measurements with respect to time can be used to determine a cross-correlated temperature feature. The temperature measurements can be smoothed as described herein prior to determining the cross-correlation with respect to time. As another example, an autocorrelation measurement of the temperature measurements can be obtained with respect to depth. Autocorrelation is defined as the cross-correlation of a signal with itself. An autocorrelation temperature feature can thus measure the similarity of the signal with itself as a function of the displacement. An autocorrelation temperature feature can be used, in applications, as a means of anomaly detection for event (e.g., fluid inflow) detection. The temperature measurements can be smoothed and/or the resulting autocorrelation measurements can be smoothed as described herein to determine the autocorrelation temperature features.
In some embodiments, the temperature features can comprise a Fast Fourier transform (FFT) of the distributed temperature sensing (e.g., DTS) signal. This algorithm can transform the distributed temperature sensing signal from the time domain into the frequency domain, thus allowing detection of the deviation in DTS along length (e.g., depth). This temperature feature can be utilized, for example, for anomaly detection for event (e.g., fluid inflow) detection purposes.
In some embodiments, the temperature features can comprise the Laplace transform of DTS. This algorithm can transform the DTS signal from the time domain into Laplace domain allows us to detect the deviation in the DTS along length (e.g., depth of wellbore 114). This temperature feature can be utilized, for example, for anomaly detection for event (e.g., fluid inflow) detection. This feature can be utilized, for example, in addition to (e.g., in combination with) the FFT temperature feature.
In some embodiments, the temperature features can comprise a wavelet transform of the distributed temperature sensing (e.g., DTS) signal and/or of the derivative of DTS with respect to depth, dT/dz. The wavelet transform can be used to represent the abrupt changes in the signal data. This feature can be utilized, for example, in inflow detection. A wavelet is described as an oscillation that has zero mean, which can thus make the derivative of DTS in depth more suitable for this application. In embodiments and without limitation, the wavelet can comprise a Morse wavelet, an Analytical wavelet, a Bump wavelet, or a combination thereof.
In some embodiments, the temperature features can comprise a derivative of DTS with respect to depth, or dT/dz. The relationship between the derivative of flowing temperature T_fwith respect to depth (L) (i.e., dT_fdL) has been described by several models. For example, and without limitation, the model described by Sagar (Sagar, R., Doty, D. & Schmidt, (1991, Nov. 1). Predicting Temperature Profiles in a Flowing Well. Society of Petroleum Engineers doi: 10.2118/19702-PA) which accounts for radial heat loss due to conduction and describes a relationship (Equation (1) below) between temperature change in depth and mass rate. The mass rate w_tis conversely proportional to the relaxation parameter A and, as the relaxation parameter A increases, the change in temperature in depth increases. Hence this temperature feature can be designed to be used, for example, in events comprising inflow quantification.
$\begin{matrix} \frac{{dT}_{f}}{dL} = - A [(T_{f} - T_{e}) + \frac{g}{g_{c}} \frac{\sin θ}{{JC}_{pm} A} - \frac{F_{c}}{A}] & (1) \end{matrix}$
The formula for the relaxation parameter, A, is provided in Equation (2):
$\begin{matrix} A = (\frac{2 π}{w_{i} C_{pl}}) (\frac{r_{d} {Uk}_{e}}{k_{e} + r_{d} Uf / 12}) (\frac{1}{86, 400 \times 12}) & (2) \end{matrix}$
A=coefficient, ft⁻¹
C_pL=specific had of liquid, Btu/lbm-° F.
C_pm=specific heat mixture, Btu/lbm-° F.
C_po=specific heat of oil Btu/lbm-° F.
C_pw=specific heat of water, Btu/lbm-° F.
d_c=casing diameter, in.
d_t=tubing diameter, in.
d_wb=wellbore diameter, in.
D=depth, ft
D_inj=injection depth, ft
f=modified dimensionless heat conduction time function for long times for earth
f(t)=dimensionless transient heat conduction time function for earth
F_c=correction factor
F_c =average correction factor for one length interval
g=acceleration of gravity, 32.2 ft/sec²
g_c=conversion factor, 32.2 ft-lbm/sec²-lbf
g_G=geothermal gradient, ° F./ft
h=specific enthalpy, Btu/lbm
J=mechanical equivalent of heat, 778 ft-lbf/Btu
k_an=thermal conductivity of material in annulus, Btu/D-ft-° F.
k_ang=thermal conductivity of gas in annulus, Btu/D-ft-° F.
k_anw=thermal conductivity of water in annulus, Btu/D-ft-° F.
k_cem=thermal conductivity of cement, Btu/D-ft-° F.
k_e=thermal conductivity of earth, Btu/D-ft-° F.
L=length of well from perforations, ft
L_in=length from perforation to inlet, ft
p=pressure, psi
p_wh=wellhead pressure, psig
q_fg=formation gas flow rale, scf/D
q_ginj=injection gas flow rate, scf/D
q_o=oil flow rate, STB/D
q_w=water flow rate, STB/D
Q=heat transfer between fluid and surrounding area, Btu/lbm
r_ci=inside casing radius, in.
r_co=outside casing radius, in.
r_ti=inside tubing radius, in.
r_to=outside tubing radius, in.
r_wb=wellbore radius, in.
R_gL=gas/liquid ratio, scf/STB
T=temperature, ° F.
T_bh=bottomhole temperature, ° F.
T_c=casing temperature, ° F.
T_e=surrounding earth temperature, ° F.
T_ein=earth temperature at inlet, ° F.
T_f=flowing fluid temperature, ° F.
T_fin=flowing fluid temperature at inlet, ° F.
T_h=cement/earth interface temperature, ° F.
U=overall heat transfer coefficient. Btu/D-ft²-° F.
v=fluid velocity, ft/sec
V=volume
w_t=total mass flow rate, lbm/sec
Z height from bottom of hole, ft
Z_in=height from bottom of hole at inlet, ft
α=thermal diffusivity of earth, 0.04 ft²/hr
γAPI=oil gravity, ° API
γ_g=gas specific gravity (air=1)
γ_o=oil specific gravity
γ_w=water specific gravity
θ=angle of inclination, degrees
μ=Joule-Thomson coefficient
In some embodiments, the temperature features can comprise a heat loss parameter. As described hereinabove, Sagar's model describes the relationship between various input parameters, including the mass rate w_tand temperature change in depth dT_f/d_L. These parameters can be utilized as temperature features in a machine learning model which uses features from known cases (production logging results) as learning data sets, when available. These features can include geothermal temperature, deviation, dimensions of the tubulars 120 that are in the well (casing 112, tubing 120, gravel pack 122 components, etc.), as well as the wellbore 114, well head pressure, individual separator rates, downhole pressure, gas/liquid ratio, and/or a combination thereof. Such heat loss parameters can, for example, be utilized as inputs in a machine learning model for events comprising inflow quantification of the mass flow rate w_t.
In some embodiments, the temperature features can be based on dynamic temperature measurements rather than steady state or flowing temperature measurements. In order to obtain dynamic temperature measurements, a change in the operation of the wellbore 114 can be introduced, and the temperature monitored using the temperature monitoring system. The change in conditions can be introduced by shutting in the wellbore 114, opening one or more sections of the wellbore 114 to flow, introducing a fluid to the wellbore 114 (e.g., injecting a fluid), and the like. When the wellbore 114 is shut in from a flowing state, the temperature profile along the wellbore 114 may be expected to change from the flowing profile to the baseline profile over time. Similarly, when a wellbore 114 that is shut in is opened for flow, the temperature profile may change from a baseline profile to a flowing profile. Based on the change in the condition of the wellbore 114, the temperature measurements can change dynamically over time. In some embodiments, this approach can allow for a contrast in thermal conductivity to be determined between a location or interval having radial flow (e.g., into or out of the wellbore) to a location or interval without radial flow. One or more temperature features can then be determined using the dynamic temperature measurements. Once the temperature features are determined from the temperature measurements obtained from the temperature monitoring system, one or more of the temperature features can be used to identify events (e.g., fluid inflow events within a wellbore), as described in more detail herein.
Any of these temperature features, or any combination of these temperature features (including transformations of any of the temperature features and combinations thereof), can be used to detect one or more events. In an embodiment, a selected set of characteristics can be used to identify the presence or absence for each event, and/or all of the temperature features that are calculated can be used as a group in characterizing the presence or absence of an event. The specific values for the temperature features that are calculated can vary depending on the specific attributes of the temperature signal acquisition system, such that the absolute value of each temperature feature can change between systems. In some embodiments, the temperature features can be calculated for each event based on the system being used to capture the temperature signal and/or the differences between systems can be taken into account in determining the temperature feature values for each event between or among the systems used to determine the values and the systems used to capture the temperature signal being evaluated.
One or a plurality of temperature features can be used to identify events. In an embodiment, one, or at least two, three, four, five, six, seven, eight, etc. different temperature features can be used to detect events. The temperature features can be combined or transformed in order to define the event signatures for one or more events, such as, for instance, a fluid inflow event location or flowrate. The actual numerical results for any temperature feature may vary depending on the data acquisition system and/or the values can be normalized or otherwise processed to provide different results.

DTS Method

The systems described herein can be used with the temperature features to determine the presence of one or more events (e.g. fluid inflow), and/or a location(s) of the event(s) (e.g., a fluid inflow event at one or more locations along the wellbore). FIG. 4 illustrates a method 400 for detecting one or more events. The method can start at step 402 with a determination of one or a plurality of temperature features, for example and without limitation, temperature originating within the wellbore 114. As described herein, one or more fluids that can include gas, a liquid aqueous phase, a liquid hydrocarbon phase, and potentially other fluids as well as various combinations thereof can enter a wellbore 114 at one or more locations along the wellbore 114. The temperature features can then be used, in embodiments, to identify events comprising these inflow locations.
As depicted in FIG. 5 , which illustrates a method 402′ for determining temperature features, determining temperature features 402′ can comprise obtaining a distributed temperature sensing signal at 402A and determining one or a plurality of temperature features from the distributed temperature sensing signal at 402B. For example, the temperature features can be determined using the temperature monitoring system 110 to obtain temperature measurements along the monitored length (e.g., a monitored length along the optical fiber 162, such as along a length of the wellbore 114). In some embodiments, a DTS system 110 can be used to receive distributed temperature measurement signals from a sensor disposed along the length (e.g., of a wellbore 114), such as an optical fiber 162. The resulting signals from the temperature monitoring system 110 can be used to determine one or more temperature features as described herein. In some embodiments, a baseline or background temperature profile can be used to determine the temperature features, and the baseline temperature profile can be obtained prior to obtaining the temperature measurements.
In some embodiments, a plurality of temperature features can be determined from the temperature measurements, and the plurality of temperature features can comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, a peak-to-peak value, a fast Fourier transform, a Laplace transform, a wavelet transform, a derivative of temperature with respect to length (e.g., depth), a heat loss parameter, an autocorrelation, as detailed hereinabove, and/or the like. Other temperature features can also be used in some embodiments. The temperature excursion measurement can comprise a difference between a temperature reading at a first depth, and a smoothed temperature reading over a depth range, where the first depth is within the depth range. The baseline temperature excursion can comprise a derivative of a baseline excursion with depth, where the baseline excursion can comprise a difference between a baseline temperature profile and a smoothed temperature profile. The peak-to-peak value can comprise a derivative of a peak-to-peak difference with depth, where the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval. The fast Fourier Transform can comprise an FFT of the distributed temperature sensing signal. The Laplace transform can comprise a Laplace transform of the distributed temperature sensing signal. The wavelet transform can comprise a wavelet transform of the distributed temperature sensing signal or of the derivative of the distributed temperature sensing signal with respect to length (e.g., depth). The derivative of the distributed temperature sensing signal with respect to length (e.g., depth) can comprise the derivative of the flowing temperature with respect to depth. The heat loss parameter can comprise one or more of the geothermal temperature, a deviation, dimensions of the tubulars that are in the well, well head pressure, individual separator rates, downhole pressure, gas/liquid ratio, or the like. The autocorrelation can comprise a cross-correlation of the distributed temperature sensing signal with itself.
Once the temperature features are obtained, the temperature features can be used with an event detection model to detect the event. In some embodiments, the event detection model can accept a plurality of temperature features as inputs. In general, the temperature features are representative of feature at a particular location (e.g., a depth resolution portion of the optical fiber along a length (e.g., a length of the wellbore)) along the wellbore 114. The event detection model can comprise one or more models configured to accept the temperature features as input(s) and provide an indication of whether or not there is an event at the particular location along the length (e.g., along the optical fiber 162 and/or wellbore 114). The output of the event detection model can be in the form of a binary yes/no result, and/or a likelihood of an event (e.g., a percentage likelihood, etc.). Other outputs providing an indication of an event are also possible. In some embodiments, the event detection model can comprise a multivariate model, a machine learning model using supervised or unsupervised learning algorithms, or the like.
In some embodiments, the event detection model can comprise a multivariate model. A multivariate model allows for the use of a plurality of variables in a model to determine or predict an outcome. A multivariate model can be developed using known data on events (e.g., inflow events, outflow events, etc.) along with temperature features for those events to develop a relationship between the temperature features and the presence of the event at the locations within the available data. One or more multivariate models can be developed using data, where each multivariate model uses a plurality of temperature features as inputs to determine the likelihood of an event occurring at the particular location along the length (e.g., a length of the wellbore 114).
In some embodiments, the event detection model can comprise one or more multivariate models. The multivariate model can use multivariate equations, and the multivariate model equations can use the temperature features or combinations or transformations thereof to determine when an event is (or is not) present. The multivariate mod& can define a threshold, decision point, and/or decision boundary having any type of shapes such as a point, line, surface, or envelope between the presence and absence of the specific event. In some embodiments, the multivariate model can be in the form of a polynomial, though other representations are also possible. The model can include coefficients that can be calibrated based on known event data. While there can be variability or uncertainty in the resulting values used in the model, the uncertainty can be taken into account in the output of the model. Once calibrated or tuned, the mod& can then be used with the corresponding temperature features to provide an output that is indicative of the occurrence (or lack of occurrence) of an event.
The multivariate model is not limited to two dimensions (e.g., two temperature features or two variables representing transformed values from two or more temperature features), and rather can have any number of variables or dimensions in defining the threshold between the presence or absence of the (e.g., fluid inflow) event. When used, the detected values can be used in the multivariate model, and the calculated value can be compared to the model values. The presence of the (e.g., fluid inflow) event can be indicated when the calculated value is on one side of the threshold and the absence of the fluid or flow regime can be indicated when the calculated value is on the other side of the threshold. In some embodiments, the output of the multivariate model can be based on a value from the model relative to a normal distribution for the model. Thus, the model can represent a distribution or envelope and the resulting temperature features can be used to define where the output of the model lies along the distribution at the location in the wellbore 114. Thus, each multivariate model can, in some embodiments, represent a specific determination between the presence of absence of an event at a specific location along a length (e.g., in the wellbore 114). Different multivariate models, and therefore thresholds, can be used for different (e.g., fluid inflow) events, and each multivariate model can rely on different temperature features or combinations or transformations of temperature features. Since the multivariate models define thresholds for the determination and/or identification of (e.g., fluid inflow) events, the multivariate models and event detection model using such multivariate models can be considered to be temperature based event signatures for each type of (e.g., fluid inflow) event.
In some embodiments, the event detection model can comprise a plurality of models. Each of the models can use one or more of the temperature features as inputs. The models can comprise any suitable model that can relate one or more temperature features to an occurrence of an event (e.g., a likelihood of the event, a binary yes/no output, etc.). The output of each model can then be combined to form a composite or combined output. The combined output can then be used to determine if an event has occurred, for example, by comparing the combined output with a threshold value (e.g., a fluid inflow threshold). The determination of the occurrence of an event can then be based on the comparison of the combined output with the threshold value.
As an example, the event detection model can comprise a plurality of multivariate models, each using a plurality of temperature features as described above. The output of the multivariate models can include a percentage likelihood of the occurrence of an (e.g., a fluid inflow) event at the particular location at which each model is applied. The resulting output values can then be used in a function such as a simple multiplication, a weighted average, a voting scheme, or the like to provide a combined output. The resulting output can then be compared to a threshold to determine if an event has (or has not) occurred. For example, a combined output indicating that there is greater than a fifty percent likelihood an event at the particular location can be taken as an indication that an event has occurred at the location of interest.
In some embodiments, the event detection model can also comprise other types of models. In some embodiments, a machine learning approach comprises a logistic regression model. In some such embodiments, one or more temperature features can be used to determine if an event is present at one or more locations of interest. The machine learning approach can rely on a training data set that can be obtained from a test set-up (e.g., a flow loop) or obtained based on actual temperature data from known events. The one or more temperature features in the training data set can then be used to train the event detection model using machine learning, including any supervised or unsupervised learning approach. For example, the event detection model can be a neural network, a Bayesian network, a decision tree, a logistical regression model, a normalized logistical regression model, or the like. In some embodiments, the event detection model can comprise a model developed using unsupervised learning techniques such a k-means clustering and the like.
In some embodiments when a plurality of models are used, the models can be different, The use of different models for one or more types of events can allow for a more accurate determination of each event. The models can differ in a number of ways. For example, the models can have different parameters, different mathematical determinations, be different types of models, and/or use different temperature features. In some embodiments, a plurality of models can be used for different events, and at least one of the models can have different parameters. In general, parameters refer to constants or values used within the models to determine the output of the model. In multivariate models as an example, the parameters can be coefficients of one or more terms in the equations in the models. In neural network models as an example, the parameters can be the weightings applied to one or more nodes. Other constants, offsets, and coefficients in various types of models can also represent parameters. The use of different parameters can provide a different output amongst the models when the models are used to identify different types of events.
The models can also differ in their mathematical determinations. In multivariate models, the models can comprise one or more terms that can represent linear, non-linear, power, or other functions of the input variables (e.g., one or more temperature features, etc.). The functions can then change between the models. As another example, a neural network may have different numbers of layers and nodes, thereby creating a different network used with the input variables. Thus, even when the same temperature features are used in two more models, the outputs can vary based on the different functions and/or structures of the models.
The models can also be different on the basis of being different types of models. For example, the plurality of models can use regression models to identify one or more events and neural networks for different events. Other types of models are also possible and can be used to identify different types of events. Similarly, the models can be different by using different input variables. The use of different variables can provide different outputs between the models. The use of different models can allow for the same or different training data to be used to produce more accurate results for different types of events. Any of the models described herein can rely on the use of different models for different types of events, as described in more detail herein.
A test set-up can be used to obtain training data. For example, one or more fluids of a plurality of fluids can be introduced into a conduit at predetermined locations spanning a length of the conduit, and the temperature signal can be obtained across a portion of the conduit, The one or more fluids of a plurality of fluids can be introduced into a flowing fluid to determine the inflow signatures for fluid(s) entering flow fluids. In some embodiments, the one or more fluids can be introduced in a relatively stagnant fluid. This may help to model the lower or lowest producing portion of the well where no bulk fluid flow may be passing through the wellbore at the point at which the fluid enters the well. This may be tested to obtain the signature of fluid inflow into a fluid within the wellbore that may not be flowing. Other events such as leaks, casing flows, and the like can also be recreated in the test apparatus to allow for the corresponding temperature signals to be determined along with the corresponding temperature features. The temperature signal can be obtained by any means known to those of skill in the art. In some embodiments, the temperature data can be from field data where the data is verified by other test instruments. In some embodiments, the temperature signal is obtained from a sensor within or coupled to the conduit for each event test of the plurality of event tests. The sensor can be disposed along the length of the conduit, and the temperature signal that is obtained can be indicative of an temperature signature along a length of the conduit. The sensor can comprise a fiber optic cable disposed within the conduit, or in some embodiments, coupled to the conduit (e.g., on an outside of the conduit). The conduit can be a continuous section of a tubular, and in some embodiments, the can be disposed in a loop. While described as being a loop in some circumstances, a single section of pipe or tubular can also be used with additional piping used to return a portion of the fluid to the entrance of the conduit.
Other events such as casing vent leaks, annular flow, and various other flow type events can also be recreated using a flow loop configuration. For these events, models of wellbore configurations such as pipe-in-pipe configurations can be constructed, and annular flow, leaks, and the like can be recreated. The temperature sensors can then be used to capture the temperature signals for such events based on the test recreations. The ability to use a test apparatus to recreate such events can allow for variable yet controlled leak rates and fluid flow rates to be provided with the corresponding temperature signals being captured.
Other test data for other events such as facility monitoring events, pipeline monitoring events, and the like can also be developed using testing arrangements. Facility monitoring events can have test apparatus including one or more types of equipment with known operating issues (e.g., a pump with a worn bearing, an out of balance compressor, etc.). Pipeline monitoring can be tested with a test pipeline arrangement similar to a flow loop. The resulting data can be used to form a labelled data set that can be used to train one or more event detection models. Using the test data obtained from the flow apparatus, the method of developing the event detection model can include determining one or more temperature features from the temperature signal for at least a portion of the data from the plurality of event tests. The one or more temperature features can be obtained across the portion of the conduit including the predetermined locations, and training the event detection model can use the one or more temperature features for a plurality of the tests and the predetermined locations. The training of the event detection model can use machine learning, including any supervised or unsupervised learning approach. For example, the event detection model can be a neural network, a Bayesian network, a decision tree, a logistical regression model, a normalized logistical regression model, k-means clustering or the like.
In some embodiments, the model(s) can be developed and trained using a logistic regression model. As an example for training of a model used to determine the presence or absence of an event, the training of the model can begin with providing the one or more temperature features to the logistic regression model corresponding to one or more reference data sets in which events are present. Additional reference data sets can be provided in which events are not present. The one or more temperature features can be provided to the logistic regression model, and a first multivariate model can be determined using the one or more temperature features as inputs. The first multivariate model can define a relationship between a presence and an absence of the events in the one or more fluids.
Once the model is trained, the event detection model can be used to determine the presence or absence of an event at one or more locations along the length (e.g., a length of wellbore 114) in step 406. The temperature features determined for each location (e.g., along the wellbore 114) can be used with the event detection model. The output of the event detection model can provide an indication of the presence (or absence) of an event at each location for which the temperature features are obtained. When the output indicates that an event has occurred at a given location, an output can be generated indicating the presence of the event. The process can be repeated along the length (e.g., of the wellbore 114) to provide an event profile, which can comprise an indication of the events at one or more locations along sensor, for example along the optical fiber 162 (e.g., along the wellbore 114).
In some embodiments, the event outputs from the event detection model can be presented as a profile along a length (e.g., a length of a wellbore 114) on an output device. The outputs can be presented in the form of an event (e.g., an inflow) profile depicted along an axis with or without a (e.g., well) schematic. The event profile can then be used to visualize the event locations, which can allow for various processes to be carried out. For example, the event (e.g., fluid inflow) locations can be compared to the producing zones within a completion to understand where fluid is entering the wellbore. In some embodiments, an event (e.g., fluid inflow) can be detected at locations other than a producing zone, which may provide an indication that a remediation procedure is needed within the wellbore 114.
In the event detection model, the multivariate model equations can use the temperature features or combinations or transformations thereof to determine when a specific event is present. The multivariate model can define a threshold, decision point, and/or decision boundary having any type of shapes such as a point, line, surface, or envelope between the presence and absence of the event. In some embodiments, the multivariate model can be in the form of a polynomial, though other representations are also possible. When models such a neural networks are used, the thresholds can be based on node weights or thresholds within the model. As noted herein, the multivariate model is not limited to two dimensions (e.g., two temperature features or two variables representing transformed values from two or more temperature features), and rather can have any number of variables or dimensions in defining the threshold between the presence or absence of the event. When used, the detected values can be used in the multivariate model, and the calculated value can be compared to the model values. The presence of the event can be indicated when the calculated value is on one side of the threshold and the absence of the event can be indicated when the calculated value is on the other side of the threshold. Thus, each multivariate model can, in some embodiments, represent a specific determination between the presence of absence of an event. Different multivariate models, and therefore thresholds, can be used for each type of event, and each multivariate model can rely on different temperature features or combinations or transformations of temperature features. Since the multivariate models define thresholds for the determination and/or identification of specific events, the multivariate models and event detection model using such multivariate models can be considered to be event signatures for each type of fluid flow and/or inflow (including flow regimes, etc.).
Once the model is trained or developed, the event detection model can be verified or validated. In some embodiments, the plurality of the tests used for training the event detection model can be a subset of the plurality of flow tests, and the tests used to validate the models can be another subset of the plurality of flow tests. A method of developing an event detection model according to this disclosure can further include the validation of the trained event detection model using the temperature signals from one or more tests and the predetermined locations of the one or more tests.
The validation process can include providing the temperature signals from one or more of the plurality of event tests and the predetermined locations of the one or more of the plurality of event tests to each of the first multivariate model, the second multivariate model, and one or more additional multivariate models. A presence or absence of at least one of the first event, the second event, or one or more additional events based on an output of each of the first multivariate model, the second multivariate model, and the one or more additional multivariate models can then be determined. The event detection model can be validated by comparing the predicted presence or absence of the first event, the second event, or the one or more additional events to the actual presence as known from the test data. Should the accuracy of the event detection model be sufficient (e.g., meeting a confidence threshold), then the event detection model can be used to detect and/or identify events. If the accuracy is not sufficient, then additional data and training or development can be carried out to either find new temperature feature relationships to define the multivariate models or improve the derived multivariate models to more accurately predict the presence and identification of the events. In this process, the development, validation, and accuracy checking can be iteratively carried out until a suitable event detection model is determined. Using the validation process, a confidence level can be determined based on the validating. In some embodiments, an optional remediation procedure can be performed based on the confidence level. The optional remediation procedure can serve to correct an issue within the wellbore identified using the event detection. For example, a leak can be fixed when the event comprises a leak in the wellbore, an annulus, or around a zonal isolation device.
In some embodiments, the event(s) (e.g., fluid inflow rates) can be refined by using a measure of the event (e.g., fluid flow rate) from the wellbore 114 as measured at a logging tool above the producing zones 104 a/104 b, a wellhead, surface flow line, or the like. For example, the fluid production rate can be measured by a standard fluid flowrate measurement tool that is not associated with the temperature monitoring system 110 within the wellbore 114. For example, the fluid production rate can be measured with various flow meters. The fluid production rate can comprise an indication of the fluid flow rates of one or more fluids and/or one or more fluid phases.
Any of the systems and methods disclosed herein can be carried out on a computer or other device comprising a processor (e.g., a desktop computer, a laptop computer, a tablet, a server, a smartphone, or some combination thereof), such as the acquisition device 160 of FIG. 1 . FIG. 6 illustrates a computer system 680 suitable for implementing one or more embodiments disclosed herein such as the acquisition device or any portion thereof. The computer system 680 includes a processor 682 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 684, read only memory (ROM) 686, random access memory (RAM) 688, input/output (I/O) devices 690, and network connectivity devices 692. The processor 682 may be implemented as one or more CPU chips.
It is understood that by programming and/or loading executable instructions onto the computer system 680, at least one of the CPU 682, the RAM 688, and the ROM 686 are changed, transforming the computer system 680 in part into a particular machine or apparatus having the novel functionality taught by the present disclosure. It is fundamental to the electrical engineering and software engineering arts that functionality that can be implemented by loading executable software into a computer can be converted to a hardware implementation by well-known design rules. Decisions between implementing a concept in software versus hardware typically hinge on considerations of stability of the design and numbers of units to be produced rather than any issues involved in translating from the software domain to the hardware domain. Generally, a design that is still subject to frequent change may be preferred to be implemented in software, because re-spinning a hardware implementation is more expensive than re-spinning a software design. Generally, a design that is stable that will be produced in large volume may be preferred to be implemented in hardware, for example in an application specific integrated circuit (ASIC), because for large production runs the hardware implementation may be less expensive than the software implementation. Often a design may be developed and tested in a software form and later transformed, by well-known design rules, to an equivalent hardware implementation in an application specific integrated circuit that hardwires the instructions of the software. In the same manner as a machine controlled by a new ASIC is a particular machine or apparatus, likewise a computer that has been programmed and/or loaded with executable instructions may be viewed as a particular machine or apparatus.
Additionally, after the system 680 is turned on or booted, the CPU 682 may execute a computer program or application. For example, the CPU 682 may execute software or firmware stored in the ROM 686 or stored in the RAM 688. In some cases, on boot and/or when the application is initiated, the CPU 682 may copy the application or portions of the application from the secondary storage 684 to the RAM 688 or to memory space within the CPU 682 itself, and the CPU 682 may then execute instructions of which the application is comprised. In some cases, the CPU 682 may copy the application or portions of the application from memory accessed via the network connectivity devices 692 or via the I/O devices 690 to the RAM 688 or to memory space within the CPU 682, and the CPU 682 may then execute instructions of which the application is comprised. During execution, an application may load instructions into the CPU 682, for example load some of the instructions of the application into a cache of the CPU 682. In some contexts, an application that is executed may be said to configure the CPU 682 to do something, e.g., to configure the CPU 682 to perform the function or functions promoted by the subject application. When the CPU 682 is configured in this way by the application, the CPU 682 becomes a specific purpose computer or a specific purpose machine.
The secondary storage 684 is typically comprised of one or more disk drives or tape drives and is used for non-volatile storage of data and as an over-flow data storage device if RAM 688 is not large enough to hold all working data. Secondary storage 684 may be used to store programs which are loaded into RAM 688 when such programs are selected for execution. The ROM 686 is used to store instructions and perhaps data which are read during program execution. ROM 686 is a non-volatile memory device which typically has a small memory capacity relative to the larger memory capacity of secondary storage 684. The RAM 688 is used to store volatile data and perhaps to store instructions. Access to both ROM 686 and RAM 688 is typically faster than to secondary storage 684. The secondary storage 684, the RAM 688, and/or the ROM 686 may be referred to in some contexts as computer readable storage media and/or non-transitory computer readable media.
I/O devices 690 may include printers, video monitors, electronic displays (e.g., liquid crystal displays (LCDs), plasma displays, organic light emitting diode displays (OLED), touch sensitive displays, etc.), keyboards, keypads, switches, dials, mice, track balls, voice recognizers, card readers, paper tape readers, or other well-known input devices.
The network connectivity devices 692 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, fiber distributed data interface (FDDI) cards, wireless local area network (WLAN) cards, radio transceiver cards that promote radio communications using protocols such as code division multiple access (CDMA), global system for mobile communications (GSM), long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX), near field communications (NFC), radio frequency identity (RFID), and/or other air interface protocol radio transceiver cards, and other well-known network devices. These network connectivity devices 692 may enable the processor 682 to communicate with the Internet or one or more intranets. With such a network connection, it is contemplated that the processor 682 might receive information from the network, or might output information to the network (e.g., to an event database) in the course of performing the above-described method steps. Such information, which is often represented as a sequence of instructions to be executed using processor 682, may be received from and outputted to the network, for example, in the form of a computer data signal embodied in a carrier wave.
Such information, which may include data or instructions to be executed using processor 682 for example, may be received from and outputted to the network, for example, in the form of a computer data baseband signal or signal embodied in a carrier wave. The baseband signal or signal embedded in the carrier wave, or other types of signals currently used or hereafter developed, may be generated according to several known methods. The baseband signal and/or signal embedded in the carrier wave may be referred to in some contexts as a transitory signal.
The processor 682 executes instructions, codes, computer programs, scripts which it accesses from hard disk, floppy disk, optical disk (these various disk based systems may all be considered secondary storage 684), flash drive, ROM 686, RAM 688, or the network connectivity devices 692. While only one processor 682 is shown, multiple processors may be present. Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors. Instructions, codes, computer programs, scripts, and/or data that may be accessed from the secondary storage 684, for example, hard drives, floppy disks, optical disks, and/or other device, the ROM 686, and/or the RAM 688 may be referred to in some contexts as non-transitory instructions and/or non-transitory information.
In an embodiment, the computer system 680 may comprise two or more computers in communication with each other that collaborate to perform a task. For example, but not by way of limitation, an application may be partitioned in such a way as to permit concurrent and/or parallel processing of the instructions of the application. Alternatively, the data processed by the application may be partitioned in such a way as to permit concurrent and/or parallel processing of different portions of a data set by the two or more computers. In an embodiment, virtualization software may be employed by the computer system 680 to provide the functionality of a number of servers that is not directly bound to the number of computers in the computer system 680. For example, virtualization software may provide twenty virtual servers on four physical computers. In an embodiment, the functionality disclosed above may be provided by executing the application and/or applications in a cloud computing environment. Cloud computing may comprise providing computing services via a network connection using dynamically scalable computing resources. Cloud computing may be supported, at least in part, by virtualization software. A cloud computing environment may be established by an enterprise and/or may be hired on an as-needed basis from a third party provider. Some cloud computing environments may comprise cloud computing resources owned and operated by the enterprise as well as cloud computing resources hired and/or leased from a third party provider.
In an embodiment, some or all of the functionality disclosed above may be provided as a computer program product. The computer program product may comprise one or more computer readable storage medium having computer usable program code embodied therein to implement the functionality disclosed above. The computer program product may comprise data structures, executable instructions, and other computer usable program code. The computer program product may be embodied in removable computer storage media and/or non-removable computer storage media. The removable computer readable storage medium may comprise, without limitation, a paper tape, a magnetic tape, magnetic disk, an optical disk, a solid state memory chip, for example analog magnetic tape, compact disk read only memory (CD-ROM) disks, floppy disks, jump drives, digital cards, multimedia cards, and others. The computer program product may be suitable for loading, by the computer system 680, at least portions of the contents of the computer program product to the secondary storage 684, to the ROM 686, to the RAM 688, and/or to other non-volatile memory and volatile memory of the computer system 680. The processor 682 may process the executable instructions and/or data structures in part by directly accessing the computer program product, for example by reading from a CD-ROM disk inserted into a disk drive peripheral of the computer system 680. Alternatively, the processor 682 may process the executable instructions and/or data structures by remotely accessing the computer program product, for example by downloading the executable instructions and/or data structures from a remote server through the network connectivity devices 692. The computer program product may comprise instructions that promote the loading and/or copying of data, data structures, files, and/or executable instructions to the secondary storage 684, to the ROM 686, to the RAM 688, and/or to other non-volatile memory and volatile memory of the computer system 680.
In some contexts, the secondary storage 684, the ROM 686, and the RAM 688 may be referred to as a non-transitory computer readable medium or a computer readable storage media. A dynamic RAM embodiment of the RAM 688, likewise, may be referred to as a non-transitory computer readable medium in that while the dynamic RAM receives electrical power and is operated in accordance with its design, for example during a period of time during which the computer system 680 is turned on and operational, the dynamic RAM stores information that is written to it, Similarly, the processor 682 may comprise an internal RAM, an internal ROM, a cache memory, and/or other internal non-transitory storage blocks, sections, or components that may be referred to in some contexts as non-transitory computer readable media or computer readable storage media.
Having described various systems and methods, certain embodiments can include, but are not limited to systems and methods for detecting events. Certain embodiments for detecting events can include, but are not limited to:
In a first embodiment, a method of detecting one or more events comprises: obtaining a temperature sensing signal; determining a plurality of temperature features from the temperature sensing signal; and determining the presence of the one or more events using the plurality of temperature features.
A second embodiment can include the method of the first embodiment, further comprising: using the plurality of temperature features in an event detection model, wherein determining the presence of the one or more events comprises: determining the presence of the one or more events based on an output from the event detection model.
A third embodiment can include the method of any one of the first or second embodiments, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-wed leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, sand ingress, wax deposition, or sand flow along a wellbore.
A fourth embodiment can include the method of any one of the first to third embodiments, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.
A fifth embodiment can include the method of any one of the first to fourth embodiments, wherein the one or more events comprises a fluid inflow at one or more locations.
A sixth embodiment can include the method of the fifth embodiment, wherein the fluid inflow comprises a liquid inflow at the one or more locations.
A seventh embodiment can include the method of the sixth embodiment, wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both.
An eighth embodiment can include the method of any one of the first to seventh embodiments, wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth.
A ninth embodiment can include the method of any one of the first to eighth embodiments, wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
A tenth embodiment can include the method of any one of the first to ninth embodiments, wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
An eleventh embodiment can include the method of any one of the first to tenth embodiments, wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
A twelfth embodiment can include the method of any one of the first to eleventh embodiments, wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself.
A thirteenth embodiment can include the method of any one of the first to twelfth embodiments, wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal.
A fourteenth embodiment can include the method of any one of the first to thirteenth embodiments, wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal.
A fifteenth embodiment can include the method of any one of the first to fourteenth embodiments, wherein the plurality of temperature features comprises a wavelet transform of the temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth).
A sixteenth embodiment can include the method of the fifteenth embodiment, wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof.
A seventeenth embodiment can include the method of any one of the first to sixteenth embodiments, wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein.
An eighteenth embodiment can include the method of any one of the first to seventeenth embodiments, wherein the plurality of temperature features comprises a heat loss parameter.
A nineteenth embodiment can include the method of any one of the second to eighteenth embodiments, wherein the event detection model comprises a plurality of models, wherein each model of the plurality of model uses one or more temperature features of the plurality of temperature features, and wherein determining the presence of the event comprises: combining an output from each model to determine combined output; comparing the combined output with an event threshold; and determining that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.
A twentieth embodiment can include the method of the nineteenth embodiment, wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model.
A twenty first embodiment can include the method of any one of the second to nineteenth embodiments, wherein the event detection model uses an unsupervised learning algorithm.
A twenty second embodiment can include the method of any one of the second to nineteenth embodiments, wherein the event detection model uses a supervised learning algorithm.
A twenty third embodiment can include the method of any one of the first to twenty second embodiments, further comprising: receiving the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.
A twenty fourth embodiment can include the method of the twenty third embodiment, wherein the sensor is disposed in a wellbore.
A twenty fifth embodiment can include the method of any one of the first to twenty fourth embodiments further comprising: denoising the temperature sensing signal prior to determining the one or more temperature features.
A twenty sixth embodiment can include the method of the twenty fifth embodiment, wherein denoising the temperature sensing signal comprises median filtering the temperature sensing signal.
A twenty seventh embodiment can include the method of any one of the first to twenty sixth embodiments, further comprising: calibrating the temperature sensing signal.
A twenty eighth embodiment can include the method of any one of the first to twenty seventh embodiments, further comprising: normalizing the one or more temperature features prior to determining the presence of the one or more events.
A twenty ninth embodiment can include the method of any one of the first to twenty eighth embodiments, wherein determining the presence or absence of the one or more events comprises: identifying one or more anomalies in the temperature sensing signal using the one or more temperature features; and/or selecting depth intervals of the one or more anomalies as event locations.
A thirtieth embodiment can include the method of any one of the first to twenty ninth embodiments, further comprising: determining a response or remediation procedure based on the presence of the one or more events; and performing the response or remediation procedure.
In a thirty first embodiment, a method of detecting one or more events comprises: determining a plurality of temperature features from a temperature sensing signal, wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal and/or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), or a heat loss parameter; and determining the presence or absence of the one or more events using the plurality of temperature features.
A thirty second embodiment can include the method of the thirty first embodiment, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, sand ingress, wax deposition, or sand flow along a wellbore.
A thirty third embodiment can include the method of any one of the thirty first to thirty second embodiments, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.
A thirty fourth embodiment can include the method of any one of the thirty first to thirty third embodiments, wherein the one or more events comprises a fluid inflow at one or more locations.
A thirty fifth embodiment can include the method of the thirty fourth embodiment, wherein the fluid inflow is a liquid inflow at the one or more locations.
A thirty sixth embodiment can include the method of the thirty fifth embodiment, wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both an aqueous liquid and a hydrocarbon liquid.
A thirty seventh embodiment can include the method of any one of the thirty first to thirty sixth embodiments, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
A thirty eighth embodiment can include the method of any one of the thirty first to thirty seventh embodiments, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
A thirty ninth embodiment can include the method of any one of the thirty first to thirty eighth embodiments, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
In a fortieth embodiment, a system of determining one or more events comprises: a processor; a memory; and an analysis program stored in the memory, wherein the analysis program is configured, when executed on the processor, to: receive a temperature sensing signal, wherein the temperature sensing signal; determine a plurality of temperature features from the temperature sensing signal; and determine the presence of the one or more events using the plurality of temperature features.
A forty first embodiment can include the system of the fortieth embodiment, wherein the analysis program is further configured to: use the plurality of temperature features in an event detection model; and determine the presence of the one or more events based on an output from the event detection model.
A forty second embodiment can include the system of any one of the fortieth to forty first embodiments, wherein the processor is further configured to: identify one or more event locations using the one or more temperature features.
A forty third embodiment can include the system of any one of the fortieth to forty second embodiments, wherein the one or more events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, wax deposition, sand ingress, or sand flow along a wellbore.
A forty fourth embodiment can include the system of any one of the fortieth to forty third embodiments, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or darn monitoring events.
A forty fifth embodiment can include the system of any one the fortieth to forty fourth embodiments, wherein the one or more events comprise a fluid inflow at one or more locations.
A forty sixth embodiment can include the system of the forty fifth embodiment, wherein the fluid inflow is a liquid inflow at the one or more locations.
A forty seventh embodiment can include the system of the forty sixth embodiment, wherein the liquid inflow comprises an inflow rate for an aqueous liquid, a hydrocarbon liquid, or a combination of both.
A forty eighth embodiment can include the system of any one of the fortieth to forty seventh embodiments, wherein the processor is further configured to: calibrate the temperature sensing signal.
A forty ninth embodiment can include the system of any one of the fortieth to forty eighth embodiments, wherein the processor is further configured to: normalize the one or more temperature features prior to determining the presence or absence of the one or more events.
A fiftieth embodiment can include the system of any one of the fortieth to forty ninth embodiments, wherein the processor is further configured to: identify a background event signature using the temperature sensing signal; and remove the background event signature from the temperature sensing signal prior to determining the plurality of temperature features.
A fifty first embodiment can include the system of any one of the fortieth to fiftieth embodiments, wherein the processor is further configured to: identify one or more anomalies in the temperature sensing signal using the one or more temperature features; and/or select depth intervals of the one or more anomalies as event locations.
A fifty second embodiment can include the system of any one of the fortieth to fifty first embodiments, wherein the processor is further configured to: determine a response or remediation procedure based on the presence or absence of the one or more events; and optionally perform the response or remediation procedure.
A fifty third embodiment can include the system of any one of the fortieth to fifty second embodiments, wherein the processor is further configured to: determine a confidence level for the determination of the presence of the one or more events; and optionally perform a remediation procedure based on the confidence level.
A fifty fourth embodiment can include the system of any one of the fortieth to fifty third embodiments, wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth.
A fifty fifth embodiment can include the system of any one of the fortieth to fifty fourth embodiments, wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.
A fifty sixth embodiment can include the system of any one of the fortieth to fifty fifth embodiments, wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.
A fifty seventh embodiment can include the system of any one of the fortieth to fifty sixth embodiments, wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.
A fifty eighth embodiment can include the system of any one of the fortieth to fifty seventh embodiments, wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself.
A fifty ninth embodiment can include the system of any one of the fortieth to fifty eighth embodiments, wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal.
A sixtieth embodiment can include the system of any one of the fortieth to fifty ninth embodiments, wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal.
A sixty first embodiment can include the system of any one of the fortieth to sixtieth embodiments, wherein the plurality of temperature features comprises a wavelet transform of the distributed temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth).
A sixty second embodiment can include the system of the sixty first embodiment, wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof.
A sixty third embodiment can include the system of ay one of the fortieth to sixty second embodiments, wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein.
A sixty fourth embodiment can include the system of any one of the fortieth to sixty third embodiments, wherein the plurality of temperature features comprises a heat loss parameter.
A sixty fifth embodiment can include the system of any one of the forty first to sixty fourth embodiments, wherein the event detection model comprises a plurality of models, wherein each model of the plurality of models uses one or more temperature features of the plurality of temperature features, and wherein the analysis program is further configured to: combine an output from each model to determine combined output; compare the combined output with an event threshold; and determine that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.
A sixty sixth embodiment can include the system of the sixty fifth embodiment, wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model.
A sixty seventh embodiment can include the system of any one of the forty first to sixty sixth embodiments, wherein the event detection model uses an unsupervised learning algorithm.
A sixty eighth embodiment can include the system of any one of the forty first to sixty sixth embodiments, wherein the event detection model uses a supervised learning algorithm.
A sixty ninth embodiment can include the system of any one of the fortieth to sixty eighth embodiments, wherein the analysis program is further configured to: receive the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.
A seventieth embodiment can include the embodiment of the sixty ninth embodiment, wherein the sensor is disposed in a wellbore.
The embodiments disclosed herein have included systems and methods for detecting and/or characterizing sand ingress and/or sand transport within a subterranean wellbore, or a plurality of such wellbores. Thus, through use of the systems and methods described herein, one may more effectively limit or avoid sand ingress and accumulation with a wellbore so as to enhance the economic production therefrom.
While exemplary embodiments have been shown and described, modifications thereof can be made by one skilled in the art without departing from the scope or teachings herein. The embodiments described herein are exemplary only and are not limiting. Many variations and modifications of the systems, apparatus, and processes described herein are possible and are within the scope of the disclosure. Accordingly, the scope of protection is not limited to the embodiments described herein, but is only limited by the claims that follow, the scope of which shall include all equivalents of the subject matter of the claims. Unless expressly stated otherwise, the steps in a method claim may be performed in any order. The recitation of identifiers such as (a), (b), (c) or (1), (2), (3) before steps in a method claim are not intended to and do not specify a particular order to the steps, but rather are used to simplify subsequent reference to such steps.

Claims

1. A method of detecting one or more events, the method comprising:

obtaining a temperature sensing signal;

determining a plurality of temperature features from the temperature sensing signal; and

determining the presence of the one or more events using the plurality of temperature features.

2. The method of claim 1, further comprising:

using the plurality of temperature features in an event detection model, wherein determining the presence of the one or more events comprises: determining the presence of the one or more events based on an output from the event detection model.

3. The method of claim 1, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, sand ingress, wax deposition, or sand flow along a wellbore.

4. The method of claim 1, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or darn monitoring events.

5. The method of claim 1, wherein the one or more events comprises a fluid inflow at one or more locations.

6. The method of claim 5, wherein the fluid inflow comprises a liquid inflow at the one or more locations.

7. The method of claim 6, wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both.

8. The method of 1, wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth.

9. The method of claim 1, wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.

10. The method of claim 1, wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.

11. The method of claim 1, wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.

12. The method of claim 1, wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself.

13. The method of claim 1, wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal.

14. The method of claim 1, wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal.

15. The method of claim 1, wherein the plurality of temperature features comprises a wavelet transform of the temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth).

16. The method of claim 15, wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof.

17. The method of claim 1, wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein.

18. The method of claim 1, wherein the plurality of temperature features comprises a heat loss parameter.

19. The method of claim 2, wherein the event detection model comprises a plurality of models, wherein each model of the plurality of model uses one or more temperature features of the plurality of temperature features, and wherein determining the presence of the event comprises:

combining an output from each model to determine combined output;

comparing the combined output with an event threshold; and

determining that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.

20. The method of claim 19, wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model.

21. The method of claim 2, wherein the event detection model uses an unsupervised learning algorithm.

22. The method of claim 2, wherein the event detection model uses a supervised learning algorithm.

23. The method of claim 1, further comprising:

receiving the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.

24. The method of claim 23, wherein the sensor is disposed in a wellbore.

25. The method of claim 1, further comprising:

denoising the temperature sensing signal prior to determining the one or more temperature features.

26. The method of claim 25, wherein denoising the temperature sensing signal comprises median filtering the temperature sensing signal

27. The method of claim 1, further comprising:

calibrating the temperature sensing signal.

28. The method of claim 1, further comprising:

normalizing the one or more temperature features prior to determining the presence of the one or more events.

29. The method of claim 1, wherein determining the presence or absence of the one or more events comprises:

identifying one or more anomalies in the temperature sensing signal using the one or more temperature features; and/or

selecting depth intervals of the one or more anomalies as event locations.

30. The method of claim 1, further comprising:

determining a response or remediation procedure based on the presence of the one or more events; and

performing the response or remediation procedure.

31. A method of detecting one or more events, the method comprising:

determining a plurality of temperature features from a temperature sensing signal, wherein the plurality of temperature features comprise at least two of: a depth derivative of temperature with respect to depth, a temperature excursion measurement, a baseline temperature excursion, a peak-to-peak value, an autocorrelation, a Fast Fourier Transform (FFT) of the temperature sensing signal, a Laplace transform of the temperature sensing signal, a wavelet transform of the temperature sensing signal and/or of a derivative of the temperature sensing signal with respect to length (e.g., depth), or a derivative of flowing temperature with respect to length (depth), as described by Equation (1), or a heat loss parameter; and

determining the presence or absence of the one or more events using the plurality of temperature features.

32. The method of claim 31, wherein the one or more events comprise one or more wellbore events, and wherein the one or more wellbore events comprise one or more of fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, sand ingress, wax deposition, or sand flow along a wellbore.

33. The method of claim 31, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.

34. The method of claim 31, wherein the one or more events comprises a fluid inflow at one or more locations.

35. The method of claim 34, wherein the fluid inflow is a liquid inflow at the one or more locations.

36. The method of claim 35, wherein the liquid inflow comprises an aqueous liquid, a hydrocarbon liquid, or a combination of both an aqueous liquid and a hydrocarbon liquid.

37. The method of claim 31, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.

38. The method of a claim 31, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.

39. The method of claim 31, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.

40. A system of determining one or more events, the system comprising:

a processor;

a memory; and

an analysis program stored in the memory, wherein the analysis program is configured, when executed on the processor, to:

receive a temperature sensing signal;

determine a plurality of temperature features from the temperature sensing signal; and

determine the presence of the one or more events using the plurality of temperature features.

41. The system of claim 40, wherein the analysis program is further configured to:

use the plurality of temperature features in an event detection model; and

determine the presence of the one or more events based on an output from the event detection model.

42. The system of claim 40, wherein the processor is further configured to:

identify one or more event locations using the one or more temperature features.

43. The system of claim 40, wherein the one or more events comprise one or more of: fluid inflow, fluid outflow, fluid phase segregation, fluid flow discrimination within a conduit, well integrity monitoring, in-well leak detection, annular fluid flow, overburden monitoring, fluid flow detection behind a casing, fluid induced hydraulic fracture detection in an overburden, wax deposition, sand ingress, or sand flow along a wellbore.

44. The system of claim 40, wherein the one or more events comprise one or more security events, transportation events, geothermal events, carbon capture and CO₂injection events, facility monitoring events, pipeline monitoring events, or dam monitoring events.

45. The system of claim 40, wherein the one or more events comprise a fluid inflow at one or more locations.

46. The system of claim 45, wherein the fluid inflow is a liquid inflow at the one or more locations.

47. The system of claim 46, wherein the liquid inflow comprises an inflow rate for an aqueous liquid, a hydrocarbon liquid, or a combination of both.

48. The system of claim 40, wherein the processor is further configured to: calibrate the temperature sensing signal.

49. The system of claim 40, wherein the processor is further configured to:

normalize the one or more temperature features prior to determining the presence or absence of the one or more events.

50. The system of claim 40, wherein the processor is further configured to:

identify a background event signature using the temperature sensing signal: and

remove the background event signature from the temperature sensing signal prior to determining the plurality of temperature features.

51. The system of claim 40, wherein the processor is further configured to:

identify one or more anomalies in the temperature sensing signal using the one or more temperature features: and/or

select depth intervals of the one or more anomalies as event locations.

52. The system of claim 40, wherein the processor is further configured to:

determine a response or remediation procedure based on the presence or absence of the one or more events; and

optionally perform the response or remediation procedure.

53. The system of claim 40, wherein the processor is further configured to:

determine a confidence level for the determination of the presence of the one or more events; and

optionally perform a remediation procedure based on the confidence level.

54. The system of claim 40, wherein the plurality of temperature features comprises a depth derivative of temperature with respect to depth.

55. The system of claim 40, wherein the plurality of temperature features comprises a temperature excursion measurement, wherein the temperature excursion measurement comprises a difference between a temperature reading at a first depth and a smoothed temperature reading over a depth range, wherein the first depth is within the depth range.

56. The system of claim 40, wherein the plurality of temperature features comprises a baseline temperature excursion, wherein the baseline temperature excursion comprises a derivative of a baseline excursion with depth, wherein the baseline excursion comprises a difference between a baseline temperature profile and a smoothed temperature profile.

57. The system of claim 40, wherein the plurality of temperature features comprises a peak-to-peak value, wherein the peak-to-peak value comprises a derivative of a peak-to-peak difference with depth, wherein the peak-to-peak difference comprises a difference between a peak high temperature reading and a peak low temperature reading with an interval.

58. The system of claim 40, wherein the plurality of temperature features comprises an autocorrelation, wherein the autocorrelation is a cross-correlation of the temperature sensing signal with itself.

59. The system of claim 40, wherein the plurality of temperature features comprises a Fast Fourier Transform (FFT) of the temperature sensing signal.

60. The system of claim 40, wherein the plurality of temperature features comprises a Laplace transform of the temperature sensing signal.

61. The system of claim 40, wherein the plurality of temperature features comprises a wavelet transform of the distributed temperature sensing signal or a wavelet transform of the derivative of the temperature sensing signal with length (e.g., depth).

62. The system of claim 61, wherein the wavelet comprises a Morse wavelet, an analytical wavelet, a Bump wavelet, or a combination thereof.

63. The system of claim 40, wherein the plurality of temperature features comprises a derivative of flowing temperature with respect to depth, as defined by Equation (1) herein

64. The system of claim 40, wherein the plurality of temperature features comprises a heat loss parameter.

65. The system of claim 41, wherein the event detection model comprises a plurality of models, wherein each model of the plurality of models uses one or more temperature features of the plurality of temperature features, and wherein the analysis program is further configured to: combine an output from each model to determine combined output,

compare the combined output with an event threshold; and

determine that the combined output meets or exceeds the event threshold, wherein the determination of the presence of the event is based on the determination that the combined output meets or exceeds the event threshold.

66. The system of claim 65, wherein one or more of the plurality of models comprise multivariate models, and wherein the output from each multivariate model comprises an indication of a status of each temperature feature with respect to a multivariate normal distribution for the corresponding multivariate model.

67. The system of claim 41, wherein the event detection model uses an unsupervised learning algorithm.

68. The system of claim 41, wherein the event detection model uses a supervised learning algorithm.

69. The system of claim 40, wherein the analysis program is further configured to:

receive the temperature sensing signal from a sensor comprising a fiber optic based temperature sensor.

70. The system of claim 69, wherein the sensor is disposed in a wellbore.