EP2893378B1 - Surveillance et diagnostics pilotés par des modèles - Google Patents

Surveillance et diagnostics pilotés par des modèles Download PDF

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Publication number
EP2893378B1
EP2893378B1 EP13835507.8A EP13835507A EP2893378B1 EP 2893378 B1 EP2893378 B1 EP 2893378B1 EP 13835507 A EP13835507 A EP 13835507A EP 2893378 B1 EP2893378 B1 EP 2893378B1
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Prior art keywords
downhole pump
root causes
root
classifier
hydrocarbon production
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EP2893378A4 (fr
EP2893378A1 (fr
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David John ROSSI
Richard Torrens
Zaki ALI
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Services Petroliers Schlumberger SA
Schlumberger Technology BV
Schlumberger Holdings Ltd
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Services Petroliers Schlumberger SA
Schlumberger Technology BV
Schlumberger Holdings Ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells

Definitions

  • Operations such as geophysical surveying, drilling, logging, well completion, and production, may be performed to locate and gather valuable downhole fluids.
  • the subterranean assets are not limited to hydrocarbons such as oil, throughout this document, the terms “oilfield” and “oilfield operation” may be used interchangeably with the terms “field” and “field operation” to refer to a site where any type of valuable fluids or minerals can be found and the activities required to extract them. The terms may also refer to sites where substances are deposited or stored by injecting the substances into the surface using boreholes and the operations associated with this process.
  • field operation refers to a field operation associated with a field, including activities related to field planning, wellbore drilling, wellbore completion, and/or production using the wellbore.
  • US 2008/0202763 A1 describes a method for determining and reporting a general production state of a gas-lift system for an oil well, the oil well having associated tubing, casing, and gas-lift valves, and wherein sensor signals from the well and its associated tubing and casing are input into mathematical models.
  • the method comprises the steps of: a) extracting values from the mathematical models that indicate instantaneous states of production; b) supplying the sensor signals and the values to an associative memory agent; and c) using the associative memory agent to associate the sensor signals and the values to generate the general production state.
  • KBS knowledge system
  • WPA Well Problem Analysis
  • US 2008/0270328 A1 describes a system and method for monitoring processes in the production of oil and gas uses intelligent software agents employing associative memory techniques that receive data from sensors in the production environment and from other sources and perform pattern matching operations to identify normal and abnormal behavior of the well production.
  • the agents report the behaviors to human operators or other software systems.
  • the abnormal behavior may consist of any behavior of the production processes that is other than the desired behavior of the well.
  • the intelligent software agents are trained to identify both specific behaviors and behaviors that have never before been observed and recognized in the well.
  • model-driven surveillance and diagnostics illustrate several embodiments of model-driven surveillance and diagnostics and are not to be considered limiting of its scope, for model-driven surveillance and diagnostics may admit to other equally effective embodiments.
  • Embodiments of model-driven surveillance and diagnostics provide an algorithmic method that continuously monitors a large number of online measurement signals to detect and automatically classify or diagnose the root cause of an underlying oil and gas production problem.
  • a thermal-hydraulic production system model is created for each root cause problem; in this manner a catalog of scenarios of some production system root cause problems is pre-defined and stored. Each scenario in the catalog is continually re-simulated using the thermal-hydraulic production system model (e.g., PIPESIM®, a registered trademark of Schlumberger Technology Corporation, Houston, TX) that predicts the surface and subsurface measurements expected under each scenario.
  • PIPESIM® a registered trademark of Schlumberger Technology Corporation, Houston, TX
  • the measurements predicted for each scenario are compared to the actual measurements in order to identify scenarios that are consistent with the measurements, within the accuracies and uncertainties of the measurements and models.
  • the rate of model re-calculation is determined by several factors such as the sample rate of the incoming measurement data, the speed at which the underlying production system changes, the speed of the computing equipment, user specified speed requirements, etc.
  • FIG. 1.1 depicts a schematic view, partially in cross section, of a field (100) in which one or more embodiments of model-driven surveillance and diagnostics may be implemented.
  • one or more of the modules and elements shown in FIG. 1.1 may be omitted, repeated, and/or substituted. Accordingly, embodiments of model-driven surveillance and diagnostics should not be considered limited to the specific arrangements of modules shown in FIG. 1.1 .
  • oil and gas production in the field (100) is performed using a wellsite system (204), a flow line (106), and surface facilities (202), collectively referred to as a production system.
  • the wellsite system (204) includes a wellbore (103) extending from a subsurface reservoir (104) to the surface wellhead (101), with hydrocarbon fluids flowing from the reservoir (104), through perforations (105) in the well casing and up to the wellhead (101).
  • the wellbore operations may be controlled by a surface unit (201).
  • the fluids proceed through a surface flow line (106) to the facilities equipment such as oil, gas and water fluid separators in the surface facilities (202), which may be situated miles to tens of miles away.
  • the terms "wellbore” and "well” may be used interchangeably.
  • measurements may be made using one or more data acquisition devices (102). From time to time (thus referred to as a "point" measurement), using the fluid separator equipment in the surface facilities (202), each individual well and flow line may be channeled into a dedicated fluid separator to perform a well test, where the individual flow rates of the oil, water and gas are measured and recorded. Additionally, instruments may measure pressure (P) and temperature (T) at the wellhead (101) and at the bottom of the well.
  • P pressure
  • T temperature
  • the surface unit (201) and the surface facilities (202) may be located at the wellsite system (204) and/or remote locations.
  • the surface unit (201) and the surface facilities (202) may be provided with computer facilities for receiving, storing, processing, and/or analyzing data from the data acquisition devices (102), or other part of the field (100).
  • the surface unit (201) and the surface facilities (202) may also be provided with functionality for actuating mechanisms at the field (100).
  • the surface unit (201) and the surface facilities (202) may then send command signals to the field (100) in response to data received, for example to control and/or optimize various field operations described above.
  • the engineer when managing an oil or gas field, the engineer will monitor the well hydrocarbon flow rate. If it falls more quickly than expected, the engineer will evaluate the available pressure and temperature data, and combine this with additional knowledge and possibly analytical or mathematical models of the system to determine the cause for the decline.
  • Thermal-hydraulic models for the well and flow line allow the engineer to relate measured pressures, temperature and flow rates. System performance can be analyzed to determine, for example, whether the root cause of an observed well performance problem lies with the reservoir (e.g., reservoir (104)), the perforation (inflow) sub-system (e.g., perforations (105)), or the well (tubing and/or annulus) subsystem (e.g., well bore (103)).
  • the thermal-hydraulic model for the well and flow line is described in reference to FIG. 3.1 below.
  • the wellsite system (204) and the surface facilities (202) are operatively coupled to a model-driven surveillance and diagnostics computer system (208).
  • the surface unit (201) and the surface facilities (202) are configured to communicate with the model-driven surveillance and diagnostics computer system (208) to send commands to the model-driven surveillance and diagnostics computer system (208) and to receive data therefrom.
  • the data received by the wellsite system (204) and the surface facilities (202) may be sent to the model-driven surveillance and diagnostics computer system (208) for further analysis.
  • the model-driven surveillance and diagnostics computer system (208) is configured to analyze, model, control, optimize, or perform other management tasks of the aforementioned field operations based on the data provided from the wellsite system (204) and the surface facilities (202).
  • the model-driven surveillance and diagnostics computer system (208) is provided with functionality for manipulating and analyzing the data, such as pressure, temperature, and other well test data, or performing simulation, planning, and optimization of production operations of the wellsite system (204) and the surface facilities (202).
  • the result generated by the model-driven surveillance and diagnostics computer system (208) may be displayed for user viewing using a 2 dimensional (2D) display, 3 dimensional (3D) display, or other suitable display.
  • the surface unit (201), the surface facilities (202), and the model-driven surveillance and diagnostics computer system (208) are shown as separate from each other in FIG. 1.1 , in other examples, two or more of the surface unit (201), the surface facilities (202), and the model-driven surveillance and diagnostics computer system (208) may also be combined.
  • FIG. 1.2 shows more details of the model-driven surveillance and diagnostics computer system (208) in which one or more embodiments of model-driven surveillance and diagnostics may be implemented.
  • one or more of the modules and elements shown in FIG. 1.2 may be omitted, repeated, and/or substituted. Accordingly, embodiments of model-driven surveillance and diagnostics should not be considered limited to the specific arrangements of modules shown in FIG. 1.2 .
  • model-driven surveillance and diagnostics computer system (208) includes model-driven surveillance and diagnostics tool (230) having model generator (231), analysis engine (232), and statistical classifier (233), data repository (234), and display (237). Each of these elements is described below.
  • the model-driven surveillance and diagnostics computer system (208) includes the model-driven surveillance and diagnostics tool (230) having software instructions stored in a memory and executing on a processor to communicate with the surface facilities (202) and/or the wellsite system (204) for receiving surveillance data (235) therefrom and to manage ( e.g., analyze, model, control, optimize, or perform other management tasks) the aforementioned field operations based on the received surveillance data (235).
  • the received surveillance data (235) is stored in the data repository (234) to be processed by the model-driven surveillance and diagnostics tool (230).
  • One or more field operation management tasks may be performed based on results of the model-driven surveillance and diagnostics tool (230).
  • the surveillance data (235) includes information that represents one or more of pressure data, temperature data, and/or flow rate data.
  • the wellbore (103) may be equipped with a downhole pump, and the surveillance data (235) may also include one or more of electrical current to a downhole pump, electrical voltage at the downhole pump, frequency of the electrical current, well head tubing fluid temperature, well head tubing fluid pressure, downhole pump intake pressure, downhole pump discharge pressure, downhole pump intake fluid temperature, downhole pump motor windings temperature, well head annulus fluid pressure, etc.
  • the wellbore (103) is not be equipped with a pump.
  • the model-driven surveillance and diagnostics tool (230) includes the model generator (231) that is configured to generate a thermal-hydraulic production system model (236) that represents the hydrocarbon production of the surface facilities (202) and the wellsite system (204).
  • the thermal-hydraulic production system model (236) is a physics-based mathematical model that has been tuned or calibrated so that the computed signals associated with the surface facilities (202) and the wellsite system (204) match the corresponding measured signals. In one or more embodiments, these computed signals and measured signals correspond to at least a portion of the surveillance data (235). As shown in FIG. 1.2 , the thermal-hydraulic production system model (236) may be stored in the repository (234).
  • the received surveillance data (235) is manipulated by the analysis engine (232) based on the thermal-hydraulic production system model (236) to generate, continuously or intermittently, preliminary results that are rendered and displayed to the user using the display (237). Examples of the thermal-hydraulic production system model (236) are described in reference to FIGS. 3.1-3.7 below.
  • the model-driven surveillance and diagnostics tool (230) includes the analysis engine (232) that is configured to simulate, using the thermal-hydraulic production system model (236) and based on a number of pre-determined root causes, a hydrocarbon production problem to generate a set of feature vectors (238) corresponding to the root causes.
  • each of feature vectors (238) includes multiple parameter values corresponding to physical parameters associated with the hydrocarbon production system.
  • the analysis engine (232) configures a statistical classifier (233) to classify the hydrocarbon production problem according to the root causes. Examples of the feature vectors (238) and corresponding root causes are described in reference to FIGS. 3.1-3.7 below.
  • the model-driven surveillance and diagnostics tool (230) includes the statistical classifier (233) that is configured to detect the hydrocarbon production problem in the field and to analyze, in response to detecting the hydrocarbon production problem, the surveillance data (235) to identify one of the pre-determined root causes of the hydrocarbon production problem.
  • the statistical classifier (233) is a Bayesian classifier.
  • the display (237) may be a two dimensional (2D) display, a three dimensional (3D) display, or other suitable display device.
  • the processor and memory of the model-driven surveillance and diagnostics computer system (208) are not explicitly depicted in FIG. 1.2 so as not to obscure other elements of the model-driven surveillance and diagnostics computer system (208). An example of such processor and memory is described in reference to FIG. 3 below.
  • the data repository (234) (and/or any information stored therein) may be a data store such as a database, a file system, one or more data structures (e.g., arrays, link lists, tables, hierarchical data structures, etc.) configured in a memory, an extensible markup language (XML) file, any other suitable medium for storing data, or any suitable combination thereof.
  • the data repository (234) may be a device internal to the model-driven surveillance and diagnostics computer system (208). In some embodiments, the data repository (234) may be an external storage device operatively connected to the model-driven surveillance and diagnostics computer system (208).
  • model-driven surveillance and diagnostics tool in particular the analysis engine (232) and the statistical classifier (233), are described in reference to FIG. 2 below.
  • FIG. 2 depicts an example method for model-driven surveillance and diagnostics in accordance with one or more embodiments.
  • the method depicted in FIG. 2 may be practiced using the model-driven surveillance and diagnostics computer system (208) described in reference to FIGS. 1.1 and 1.2 above.
  • one or more of the elements shown in FIG. 2 may be omitted, repeated, and/or performed in a different order. Accordingly, embodiments of the model-driven surveillance and diagnostics should not be considered limited to the specific arrangements of elements shown in FIG. 2 .
  • a thermal-hydraulic production system model of a wellsite and a surface facility in the field is generated.
  • the wellsite and surface facility may be those depicted in FIG. 1.1 above.
  • the thermal-hydraulic production system model is a physics-based mathematical model that has been tuned or calibrated so that the computed signals associated with the surface facilities and the wellsite system match the corresponding measured signals.
  • these computed signals and measured signals correspond to surveillance data, such as pressure data, temperature data, and/or flow rate data.
  • the wellbore may be equipped with a downhole pump
  • the surveillance data may also includes downhole pump surveillance data, such as electrical current to a downhole pump, electrical voltage at the downhole pump, frequency of the electrical current, well head tubing fluid temperature, well head tubing fluid pressure, downhole pump intake pressure, downhole pump discharge pressure, downhole pump intake fluid temperature, downhole pump motor windings temperature, and well head annulus fluid pressure.
  • thermal-hydraulic production system model examples are described in reference to FIGS. 3.1-3.7 below.
  • a hydrocarbon production problem is simulated using the thermal-hydraulic production system model to generate a set of feature vectors corresponding to the pre-determined root causes.
  • each feature vector includes a number of parameter values corresponding to physical parameters associated with the hydrocarbon production.
  • the list of pre-determined root causes includes zero flow through a downhole pump, low flow rate through the downhole pump, and operating the downhole pump that is not submerged in liquid.
  • physical parameters forming each feature vector includes one or more of electrical current to a downhole pump, electrical voltage at the downhole pump, frequency of the electrical current, well head tubing fluid temperature, well head tubing fluid pressure, downhole pump intake pressure, downhole pump discharge pressure, downhole pump intake fluid temperature, downhole pump motor windings temperature, and well head annulus fluid pressure.
  • the feature vector further includes one or more derivative(s) ( i.e., rate(s) of change, or higher order derivative(s)) of these physical parameters.
  • the one or more derivative(s) are numerical derivatives of the signal.
  • PDFs probability density functions
  • a statistical classifier of the hydrocarbon production problem is configured using the set of feature vectors, and optionally the PDFs. Specifically, the statistical classifier is configured to classify the hydrocarbon production problem according to the list of pre-determined root causes. In one or embodiments, the statistical classifier includes a Bayesian classifier.
  • the hydrocarbon production problem in the field is detected.
  • the hydrocarbon production problem may be detected using conventional surveillance problem detecting technique.
  • the hydrocarbon production problem may be detected based on detecting pressure surveillance data, temperature surveillance data, and/or flow rate surveillance data exceeding one or more pre-defined threshold.
  • Element 216 using the statistical classifier and in response to detecting the hydrocarbon production problem, surveillance data from the wellsite and the surface facility are analyzed to identify a root cause from the list of pre-determined root causes as causing the detected hydrocarbon production problem.
  • analyzing the surveillance data includes using the statistical classifier to generate a classification probability associated with each of the pre-determined root causes based on the surveillance data. Accordingly, the root cause is identified based on the corresponding classification probability meeting a pre-determined criterion.
  • the statistical classifier is a Bayesian classifier and classification probability is generated at least in part based on previous classification probabilities. For example, the Bayesian classifier obtains previous surveillance data at a previous time and analyzes the previous surveillance data to generate a previous classification probability associated with each of the pre-determined root causes. Subsequently, the Bayesian classifier obtains the surveillance data at a current time and updates the previous classification probabilities based on the surveillance data.
  • Examples of identifying the root cause using Bayesian updating are described in reference to FIGS. 3.1 , 3.2 , 3.3 , 3.4 , 3.5 , 3.6 , and 3.7 below.
  • the identified root cause is presented to a user.
  • a user input is received from the user that specifies a particular corrective action with respect to the reported root cause. Accordingly, the corrective action is performed based on the user input to address the automatically detected hydrocarbon production problem.
  • FIGS. 3.1 , 3.2 , 3.3 , 3.4 , 3.5 , 3.6 , and 3.7 depict an example of model-driven surveillance and diagnostics in accordance with one or more embodiments.
  • the example depicted in FIGS. 3.1 , 3.2 , 3.3 , 3.4 , 3.5 , 3.6 , and 3.7 is practiced using the model-driven surveillance and diagnostics computer system (208) described above.
  • the example depicted in FIGS. 3.1 , 3.2 , 3.3 , 3.4 , 3.5 , 3.6 , and 3.7 relates to the representative production system shown in FIG. 1.1 , with the following example parameters:
  • the engineer monitors the liquid flow rate measurement that may arrive as frequently as several times per minute with modern multiphase flow meters.
  • the liquid flow rate drops unexpectedly from 2744 STB/D to 2670 STB/D, indicating a hydrocarbon production problem.
  • the engineer is responsible for investigating and determining the cause for the decline.
  • root cause problems may lead to such a decline, including:
  • Root causes for production system performance problems
  • Subsystem Examples of root problems Reservoir pressure decline Fast pressure decline due to small compartments; lack of aquifer pressure drive or gas cap drive
  • Wellbore inflow (skin) Fines migration into rock pore spaces; liquid gas condensate formation in gas-filled pore spaces; changes in absolute or relative permeability due to mechanical or chemical changes
  • Well tubing Liquid accumulation in the well (liquid loading); sand entry into the well; artificial lift problems; erosion/hole in tubing; packer leak; scale formation; debris in the well Choke Partial blockage of the choke (scale formation; sand loading); erosional deterioration of the choke Flow line Flow assurance formation of hydrate, wax, asphaltene; liquid drop out in flow lines; sanding; leaks; corrosion
  • the root causes may include a change in a reservoir inflow performance, a change in a tubing characteristic, and a change in a surface characteristic.
  • the root causes may also be related to a gas lift well.
  • the root causes may include gas failing to flow into a bottom value in a gas lift well, a flowrate to a gas lift well being incorrect, a gas lift valve being stuck in an open position, and an injection through multiple gas lift values
  • FIG. 3.1 shows an example of the thermal-hydraulic production system model (236) depicted in FIG. 1.2 above.
  • the thermal-hydraulic production system model (236) includes a model A (311) for a base scenario, a model B (312) for a flowline block scenario, a model C (313) for a well blockage scenario, and a model D (314) for an inflow problem scenario.
  • the model A (311), model B (312), model C (313), and model D (314) are PIPESIM® models. As shown in FIG.
  • the model A (311) includes the model element A (315), model element B (316), model element C (317), model element D (318), and model element E (319), collectively representing the normal operations described above.
  • each of the model B (312), model C (313), and model D (314) includes similar model elements with certain modification.
  • the flowline block scenario introduces a reduction to 1" in flowline diameter at one point along the flowline (106);
  • the well blockage scenario introduces a reduction to 1" in tubing diameter at one point along the well tubing in the wellbore (103);
  • the inflow problem scenario that reduces the PI to 2.908 in the inflow model.
  • FIG. 3.2 shows a cross-plot of predicted bottom hole pressure and wellhead pressure under the three root cause problem scenarios described above.
  • the base case shown in TABLE 2 is omitted in FIG. 3.2 for clarity.
  • the bottom hole pressure and wellhead pressure form the feature vector and the cross-plot correspond to a 2-dimensional (2D) feature vector space (320).
  • the feature vector A (321), feature vector B (322), and feature vector C (323) correspond to the well blockage scenario, inflow problem scenario, and flowline block scenario, respectively.
  • these three feature vectors are within a range of approximately 30 psi in either the x-axis or y-axis of the cross-plot.
  • various methods may be used to process the continuous field measurements of flow rate, pressure, and temperature to detect when conditions have moved away from the base case in TABLE 2, and when this happens, to determine which of the three root cause problems likely have occurred:
  • the probabilities in (Eq 1) are between zero and one, and the four probabilities sum to one.
  • the modeled data d under each scenario may be computed using PIPESIM®.as shown in TABLE 2, where the j th row provides the modeled data under scenario j which is referred to as d j .
  • PIPESIM® is a registered trademark of Schlumberger Technology Corporation, located in Houston, Texas, United States of America.
  • the actual sensor readings are modeled as the modeled data plus some degree of uncertainty, represented as additive noise.
  • Equation 4 corresponds to the so-called "forward problem” of computing the PDF for the measurement m k , given the scenario state S j .
  • Bayesian inference or Bayesian updating is a method of inference in which Bayes' rule is used to update the probability estimate for a hypothesis as additional evidence is acquired.
  • S j P k ⁇ 1 S j ⁇ j 0 3
  • P m k M k
  • S j P k ⁇ 1 S j j 0, 1, 2, 3
  • (Eq 6) is used to recursively compute, from one time to the next, the probability that the production system has moved into scenario state S j .
  • Alerts may be implemented based on the behavior of these probabilites, in order to (1) warn that the production system has moved away from the base case S 0 , and (2) provide a pre-diagnostic that the production system appears to be approaching the root cause scenario having the largest posterior probability in (Eq 6).
  • (Eq 6) is recursive, that is, the output P k (S j ) at one time is considered to be the input P k-1 (S j ) at the next time.
  • (Eq 6) provides a convenient computation that allows adaptation of the method over time. In particular, if additional types of measurements are introduced into the production system, such as multiphase flow rates at the wellhead, (Eq 6) still applies with the number of measurements L increased by one.
  • the method developed in (Eq 1) through (Eq 6) is illustrated in the following example described in references to FIG. 3.3-3.6 .
  • FIGS. 3.3-3.6 show a 3-dimensional (3D) feature vector space expanded from the 2D feature vector space (320) shown in FIG. 3.2 to include a set of three measurements: (1) well flow rate (e.g., from metering devices or well tests), (2) wellhead pressure (e.g., tubing head pressure or other pressure), and (3) bottom hole pressure.
  • the 3D feature vector space is presented in FIGS. 3.3-3.6 as a composite of two 2D cross-plots where each feature vector is represented as a node in each of the two 2D cross-plots.
  • the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334) are 3D feature vectors expanded from the 2-dimensional feature vector A (321) for the well blockage scenario, feature vector B (322) for the inflow problem scenario, and feature vector C (323) for the flowline block scenario, respectively as shown in FIG. 3.2 above.
  • each of the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334) may be the same throughout the four example cases described in reference to FIGS. 3.3 , 3.4 , 3.5 , and 3.6 below.
  • the behavior of the Bayesian root cause diagnosed using (Eq 1) - (Eq 6) is evaluated using simulated noisy data to represent continuous field measurement data.
  • the measurement covariance ⁇ in (Eq 3) may be a 3x3 matrix with diagonal entries [400, 80, 80].
  • the measurement covariance matrix is set to be diagonal, which means that the measurement noise is assumed to be uncorrelated. In other examples, the covariance matrix may also have nonzero off-diagonal terms corresponding to correlation of the noise sources.
  • the diagonal entries of ⁇ correspond to the measurement variance (square of the standard deviation). Therefore, the assumed variance values of 400, 80 and 80 correspond to measurement standard deviations of ⁇ 20 STB/D for the well test measurement, and ⁇ 8.9 psi for the wellhead pressure and bottom hole pressure measurements.
  • CASE 1 Base case with noisy measurements is shown in FIG. 3.3 below.
  • FIG. 3.3 shows the 3D feature vector space A (330) where the simulated noisy measurement data (denoted as measurements (337)) is shown in the same cross-plots with the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334).
  • FIG. 3.3 shows the prior probability histogram A (335) and the posterior probability histogram A (336).
  • the prior probability histogram A (335) shows the prior scenario root cause probability of 97% chance for the base case, and 1% chance for each other problem scenario.
  • the posterior probability histogram A (336) shows the posterior scenario root cause probabilities computed using (Eq 4). Since there is no measurement evidence that any other scenario than the base scenario is true, the posterior probability is also near unity for the base case root cause in the posterior probability histogram A (336).
  • CASE 2 Flow line block scenario with noisy measurements is shown in FIG. 3.4 below.
  • FIG. 3.4 shows the 3D feature vector space B (340) where the simulated noisy measurement data (denoted as measurements (347)) is shown in the same cross-plots with the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334).
  • FIG. 3.4 shows the prior probability histogram B (345) and the posterior probability histogram B (346).
  • the prior probability histogram B (345) shows the prior scenario root cause probability of 97% chance for the base case, and 1% chance for each other scenario.
  • the posterior probability histogram B (346) shows the posterior scenario root cause probabilities computed using (Eq 4). Even though the noisy measurements are not close to any of the feature vectors in the 3D feature vector space B (340), the posterior probability is computed to be nearly unity for the flow line block root cause in the posterior probability histogram B (346).
  • CASE 3 Well blockage scenario with noisy measurements is shown in FIG. 3.5 below.
  • FIG. 3.5 shows the 3D feature vector space C (350) where the simulated noisy measurement data (denoted as measurements (357)) is shown in the same cross-plots with the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334).
  • FIG. 3.5 shows the prior probability histogram C (355) and the posterior probability histogram C (356).
  • the prior probability histogram C (355) shows the prior scenario root cause probability of 97% chance for the base case, and 1% chance for each other scenario.
  • the posterior probability histogram C (356) shows the posterior scenario root cause probabilities computed using (Eq 4). Again, even though the noisy measurements are not close to any of the feature vectors in the 3D feature vector space C (350), the posterior probability is computed to be nearly unity for the well blockage root cause in the posterior probability histogram C (356).
  • FIG. 3.6 shows the 3D feature vector space D (360) where the simulated noisy measurement data (denoted as measurements (367)) is shown in the same cross-plots with the well blockage feature vector (331), inflow problem feature vector (333), and flowline block feature vector (334).
  • FIG. 3.6 shows the prior probability histogram D (365) and the posterior probability histogram D (366).
  • the prior probability histogram D (365) shows the prior scenario root cause probability of 97% chance for the base case, and 1% chance for each other scenario.
  • the posterior probability histogram D (366) shows the posterior scenario root cause probabilities computed using (Eq 4). Even though the noisy measurements are not very close to any of the feature vectors in the 3D feature vector space D (360), the posterior probability is computed to be nearly unity for the well inflow root cause in the posterior probability histogram D (366).
  • the example shown in FIGS. 3.1-3.6 above describes a method to solve the "inverse problem" of observing online measurements (i.e., continuous field measurements) such as pressures and flow rates in an oil and gas production system, and determining directly the likelihood of the root cause for the observations.
  • the method is based on pre-defining a catalog of root cause scenarios, such as flow line blockage, well blockage, and inflow issues.
  • the method continually re-calculates the probability that each competing scenario is the true explanation for the noisy measured data, using Bayesian updating to compute the scenario posterior probabilities.
  • additional measurements may also be included in the feature vector, such as one or more of the following:
  • TABLE 3 shows the behavior of these different measurements that may be expected in the event of several hypothetical root-cause problems that are listed in the two right hand columns.
  • Three root-cause problems are listed here: deadhead ( i.e., zero flow through the running pump), low flow rate through the pump, and pump-off ( i.e., operating the pump while it is not submerged in liquid).
  • TABLE 3 indicates various levels of expected variations in the measurements, using single and double arrows up, down, and sideways corresponding to increasing, decreasing, and substantially unchanged.
  • One row in this TABLE may be considered a set of features forming a feature vector corresponding to a root-cause problem.
  • the root-cause problem feature vectors are specified at the outset, or pre-determined.
  • FIG. 3.7 shows an example flowchart of the method described above that is based on coupling a mathematical simulator (371) of a physical process to a Bayesian classifier (376) into a single integrated model-based system that is fully automated.
  • this system autonomously computes the expected root-cause features (372), and automatically transfers those features into the Bayesian classifier (376).
  • the mathematical simulator (371) computes the expected signals ( i.e., features (372)) under each of the assumed root causes (370), and automatically feeds those features (372) along with a description of the measurement noise statistics (374) to the Bayesian classifier (376).
  • Measurement noise statistics (374) are used to define a catalog of measurement PDFs (373).
  • the Bayesian classifier (376) has an aspect of memory or an ability to take into account previous observations during the current Bayesian update computation. This particular aspect is advantageous because the root-cause features may vary with time, i.e., the feature vectors may be dependent on the current state of the production system. By using the current latest calibrated simulator to compute the root-cause features, this model-driven surveillance and diagnostic system out-perform traditional Bayesian classifiers that are pre-programmed with static nominal feature descriptions.
  • Embodiments of model-driven surveillance and diagnostics may be implemented on virtually any type of computing system regardless of the platform being used.
  • the computing system may be one or more mobile devices (e.g., laptop computer, smart phone, personal digital assistant, tablet computer, or other mobile device), desktop computers, servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments of the invention.
  • the computing system (400) may include one or more computer processor(s) (402), associated memory (404) (e.g., random access memory (RAM), cache memory, flash memory, etc.
  • RAM random access memory
  • the computer processor(s) (402) may be an integrated circuit for processing instructions.
  • the computer processor(s) may be one or more cores, or micro-cores of a processor.
  • the computing system (400) may also include one or more input device(s) (410), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.
  • the computing system (400) may include one or more output device(s) (408), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device.
  • a screen e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device
  • a printer external storage, or any other output device.
  • One or more of the output device(s) may be the same or different from the input device.
  • the computing system (400) may be connected to a network (412) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) via a network interface connection (not shown).
  • LAN local area network
  • WAN wide area network
  • the input and output device(s) may be locally or remotely (e.g., via the network (412)) connected to the computer processor(s) (402), memory (404), and storage device(s) (406).
  • the input and output device(s) may take other forms.
  • Software instructions in the form of computer readable program code to perform embodiments of the invention may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium.
  • the software instructions may correspond to computer readable program code that when executed by a processor(s), is configured to perform embodiments of the invention.
  • one or more elements of the aforementioned computing system (400) may be located at a remote location and connected to the other elements over a network (412). Further, embodiments of the invention may be implemented on a distributed system having a plurality of nodes, where each portion of the invention may be located on a different node within the distributed system.
  • the node corresponds to a distinct computing device.
  • the node may correspond to a computer processor with associated physical memory.
  • the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

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Claims (15)

  1. Un procédé pour effectuer un diagnostic de production d'hydrocarbures dans un champ (100), comprenant de :
    générer un modèle de système de production thermohydraulique (236) d'un emplacement de puits (204) et d'une installation de surface (202) dans le champ (100) ;
    simuler, par un processeur informatique (402), au moyen du modèle de système de production thermohydraulique (236), et selon une pluralité de causes fondamentales (370), un problème de production d'hydrocarbures afin de générer une pluralité de vecteurs d'attributs (238, 372) correspondant à la pluralité de causes fondamentales (370),
    dans lequel chacun de la pluralité de vecteurs d'attributs (238, 372) comprend une pluralité de valeurs de paramètres correspondant à une pluralité de paramètres physiques associés à la production d'hydrocarbures ;
    configurer, au moyen de la pluralité de vecteurs d'attributs (238, 372), un classificateur (233, 376) du problème de production d'hydrocarbures,
    dans lequel le classificateur (233, 376) est configuré de façon à classifier le problème de production d'hydrocarbures selon la pluralité de causes fondamentales (370) ;
    détecter le problème de production d'hydrocarbures dans le champ (100) ;
    obtenir des données de surveillance (235, 375), dans lequel les données de surveillance (235, 375) proviennent de l'emplacement de puits (204) et de l'installation de surface (202), et dans lequel les données de surveillance (235, 375) comprennent une pluralité de mesures correspondant à la pluralité de paramètres physiques ;
    obtenir une pluralité de fonctions de densité de probabilité (373), dans lequel chacune de la pluralité de fonctions de densité de probabilité (373) représente une distribution de probabilités du bruit de mesure associé à une de la pluralité de mesures ;
    générer, par le processeur informatique (402), au moyen du classificateur (233, 376), et en réponse à la détection du problème de production d'hydrocarbures, une probabilité a posteriori (377) d'au moins une de la pluralité de causes
    fondamentales (370) par modélisation de chacune des mesures de la pluralité comme somme d'une mesure réelle et d'un bruit de mesure représenté par une correspondante de la pluralité de fonctions de densité de probabilité ;
    identifier une cause fondamentale de la pluralité de causes fondamentales (370) selon la probabilité à posteriori d'au moins une de la pluralité de causes fondamentales (370) ; et
    présenter la cause fondamentale à un utilisateur.
  2. Le procédé selon la revendication 1,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans le groupe constitué par un changement dans la performance de venue de réservoir, un changement dans une caractéristique du tubage et un changement dans une caractéristique de surface.
  3. Le procédé selon la revendication 1,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans un groupe constitué par un débit nul à travers une pompe de fond, un débit faible à travers la pompe de fond, et le fonctionnement de la pompe de fond qui n'est pas submergée dans le liquide, et
    dans lequel la pluralité de paramètres physiques comprend au moins un choisi dans un groupe constitué par un courant électrique vers la pompe de fond, la tension électrique à la pompe de fond, la fréquence du courant électrique, la température du fluide du tubage de tête de puits, la pression du fluide de tubage de tête de puits, la pression d'entrée de la pompe de fond, la pression de refoulement de la pompe de fond, la température du fluide d'entrée de la pompe de fond, la température des enroulements du moteur de la pompe de fond et la pression du fluide de l'annulaire de tête de puits.
  4. Le procédé selon la revendication 1, comprenant en outre de :
    configurer en outre le classificateur (233, 376) au moyen de la pluralité de fonctions de densité de probabilité (373).
  5. Le procédé selon la revendication 1,
    dans lequel l'identification de la cause fondamentale se fonde sur la probabilité à posteriori associée à la cause fondamentale répondant à un critère prédéterminé.
  6. Le procédé selon la revendication 5, comprenant en outre de :
    obtenir des données de surveillance antérieures (235, 375) à un pas de temps précédent ;
    analyser, au moyen du classificateur (233, 376), les données de surveillance antérieures (235, 375) pour générer une probabilité à posteriori antérieure (378) associée à chacune de la pluralité de causes fondamentales (370), dans lequel le classificateur est un classificateur bayésien ; et
    obtenir les données de surveillance (235, 375) à un pas de temps actuel postérieurement au pas de temps antérieur,
    dans lequel la génération de la probabilité à posteriori (377) comprend la mise à jour, selon les données de surveillance (235, 375), de la probabilité de classification antérieure (378) associée à chacune de la pluralité de causes fondamentales (370).
  7. Le procédé selon la revendication 1,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans un groupe constitué par le défaut du gaz de s'écouler dans une vanne de fond dans un puits de gas-lift, un débit incorrect vers un puits de gas-lift, une vanne de gas-lift bloquée en position ouverte et une injection à travers de multiples vannes de gas-lift.
  8. Un système conçu pour effectuer le diagnostic de production d'hydrocarbures dans un champ (100), comprenant :
    un emplacement de puits (204) et une installation de surface (202) dans le champ (100) pour exécution de la production d'hydrocarbures ;
    un système informatique de surveillance et de diagnostic (208), comprenant :
    un générateur de modèles (231) exécutant sur un processeur informatique (402) configuré pour générer un modèle de système de production
    thermohydraulique (236) de l'emplacement de puits (204) et de l'installation de surface (202) dans le champ (100), et
    un moteur d'analyse (232) exécutant sur un processeur informatique (402) et configuré pour :
    simuler, au moyen du modèle de système de production thermohydraulique (236) et selon une pluralité de causes fondamentales (370), un problème de production d'hydrocarbures afin de générer une pluralité de vecteurs d'attributs (238, 372) correspondant à la pluralité de causes fondamentales (370),
    dans lequel chacun de la pluralité de vecteurs d'attributs (238, 372) comprend une pluralité de valeurs de paramètres correspondant à une pluralité de paramètres physiques associés à la production d'hydrocarbures, et
    configurer, au moyen de la pluralité de vecteurs d'attributs (238, 372), un classificateur (233, 376) du problème de production d'hydrocarbures,
    dans lequel le classificateur (233, 376) exécute sur un processeur informatique (402) et est configuré pour :
    classifier le problème de production d'hydrocarbures selon la pluralité des causes fondamentales (370),
    détecter le problème de production d'hydrocarbures dans le champ (100),
    obtenir des données de surveillance (235, 375), dans lequel les données de surveillance (235, 375) proviennent de l'emplacement de puits (204) et de l'installation de surface (202), et dans lequel les données de surveillance (235, 375) comprennent une pluralité de mesures correspondant à la pluralité de paramètres physiques ;
    obtenir une pluralité de fonctions de densité de probabilité (373), dans lequel chacune de la pluralité de fonctions de densité de probabilité (373) représente une distribution de probabilités du bruit de mesure associé à une de la pluralité de mesures,
    générer, en réponse à la détection du problème de production d'hydrocarbures, une probabilité à posteriori d'au moins une de la pluralité de causes fondamentales (370) par modélisation de chacune des mesures de la pluralité comme somme d'une mesure réelle et d'un bruit de mesure représenté par une correspondante de la pluralité de fonctions de densité de probabilité.
    identifier une cause fondamentale de la pluralité de causes fondamentales (370) selon la probabilité à posteriori d'au moins une de la pluralité de causes fondamentales (370), et
    présenter la cause fondamentale à un utilisateur ; et
    un dépôt (234) configuré pour stocker les données de surveillance (235, 375) et le modèle de système de production thermohydraulique (236).
  9. Le système selon la revendication 8,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans le groupe constitué par un changement dans la performance de venue de réservoir, un changement dans une caractéristique du tubage et un changement dans une caractéristique de surface.
  10. Le système selon la revendication 8,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans un groupe constitué par un débit nul à travers une pompe de fond, un débit faible à travers la pompe de fond, et le fonctionnement de la pompe de fond qui n'est pas submergée dans le liquide, et
    dans lequel la pluralité de paramètres physiques comprend au moins un choisi dans un groupe constitué par un courant électrique vers la pompe de fond, la tension électrique à la pompe de fond, la fréquence du courant électrique, la température du fluide du tubage de tête de puits, la pression du fluide du tubage de tête de puits, la pression d'entrée de la pompe de fond, la pression de refoulement de la pompe de fond, la température du fluide d'entrée de la pompe de fond, la température des enroulements du moteur de la pompe de fond et la pression du fluide de l'annulaire de tête de puits.
  11. Le système selon la revendication 8, où le moteur d'analyse est en outre configuré pour :
    configurer en outre le classificateur (233, 376) au moyen de la pluralité de fonctions de densité de probabilité (373).
  12. Le système selon la revendication 8,
    dans lequel l'identification de la cause fondamentale se fonde sur la probabilité à posteriori associée à la cause fondamentale répondant à un critère prédéterminé.
  13. Le système selon la revendication 12,
    dans lequel le moteur d'analyse est en outre configuré pour :
    obtenir des données de surveillance antérieures (235, 375) à un pas de temps précédent ; et
    obtenir les données de surveillance (235, 375) à un pas de temps actuel postérieurement au pas de temps antérieur,
    dans lequel le classificateur (233, 376) est en outre configuré pour :
    analyser les données de surveillance antérieures (235, 375) pour générer une probabilité à posteriori antérieure (378) associée à chacune de la pluralité de causes fondamentales (370), dans lequel le classificateur est un classificateur bayésien, et
    dans lequel la génération de la probabilité à posteriori (377) comprend la mise à jour, selon les données de surveillance (235, 375), de la probabilité de classification antérieure (378) associée à chacune de la pluralité de causes fondamentales (370).
  14. Le système selon la revendication 8,
    dans lequel la pluralité de causes fondamentales (370) comprend au moins une choisie dans un groupe constitué par le défaut du gaz de s'écouler dans une vanne de fond dans un puits de gas-lift, un débit incorrect vers un puits de gas-lift, une vanne de gas-lift bloquée en position ouverte et une injection à travers de multiples vannes de gas-lift.
  15. Un produit de programme informatique comprenant un code incorporé de programme lisible par ordinateur pour l'exécution d'un procédé selon l'une quelconque des revendications 1 à 7.
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