EP4602401A1 - Automated seismic event detection method for downhole distributed acoustic sensing data processing - Google Patents

Abstract

Systems and methods may be used to automate a real-time seismic event detection process. The methods may include data preprocessing, waveform transform, coherent signal detection, detection clustering, event filtering, and so forth, and includes detecting and saving time windows that contain seismic events. The generated event list and associated time-windowed data may be used for event location, magnitude estimation, and source mechanism inversion. This, in turn, may significantly reduce the data volume to process further.

Description

AUTOMATED SEISMIC EVENT DETECTION METHOD FOR DOWNHOLE DISTRIBUTED ACOUSTIC SENSING DATA PROCESSING

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/383,728, entitled “Automated Seismic Event Detection Method for Downhole Distributed Acoustic Sensing Data Processing,” filed November 15, 2022, which is hereby incorporated by reference in its entirety for all purposes.

BACKGROUND

[0002] The present disclosure relates to an automated method for induced seismicity or microseismic event detection using downhole distributed acoustic sensing data.

[0003] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission of any kind.

[0004] Seismic monitoring is an important element for hydraulic fracturing treatment in unconventional reservoirs, water circulation in enhanced geothermal systems (EGS), as well as carbon injection and storage in subsurface reservoirs. In hydraulic fracturing treatments, microseismicity is important for mapping and characterizing created fractures. In fluid injection and storage operations, potential seismicity hazards may occur due to the injected large-volume fluid. Seismic monitoring, being also a regulatory requirement, offers an early warning tool to avoid and mitigate seismic hazards. Seismic event detection is the first step for subsequent processing encompassing event location, magnitude estimation, and source mechanism inversion. [0005] Recently, distributed acoustic sensing (DAS) is increasingly widely used for seismic monitoring. DAS makes use of Rayleigh scattering of laser pulses in fiber-optic cables to measure strain rate along a fiber. One significant advantage of DAS is the high spatial-temporal resolution and large coverage. The fiber can be kilometers long and the channel spacing may be in the order of meters, such that thousands of measurements can be obtained at each time sample. The sampling rate of DAS can be thousands of Hz. Compared with traditional geophone sensors, where there are only tens of sensing points with large inter-sensor spacing, DAS can provide a more complete picture of seismic waveforms propagating in time and space. As a consequence, the dense spatial and temporal measurements generate extremely large volumes of data. Furthermore, the signal-to-noise ratio of DAS data is generally lower than that of the geophone data. These challenges require new methods for automatic real-time seismic monitoring and analysis.

[0006] To overcome these challenges, several machine learning-based solutions have been developed. In such solutions, the event detection process is treated as an image recognition/classification problem, which is solved by convolutional neural network (CNN). The data is converted to randomly cropped windows with a certain size. The training dataset is prepared by manually labeling the seismic events and background noise. The seismic events can be either embedded synthetically (following the rules of wave propagation) or manually picked from existing field data by experts. The performance of the machine learning-based solutions is highly dependent on the training dataset, as expected. The robustness and accuracy may be gradually improved as the model is trained with a growing dataset, but it takes time and resources to manually pick and verify the events for training data preparation. Furthermore, it is nontrivial to define an optimal network architecture that is suitable for all tasks for the machine learningbased solutions. Therefore, there is a need to overcome these deficiencies.

SUMMARY

[0007] A summary of certain embodiments described herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure.

[0008] Certain embodiments of the present disclosure include a method for automating a realtime seismic event detection process based on downhole distributed acoustic sensing (DAS) measurement data. The method may include data preprocessing comprising minimizing noise and boosting seismic signals of the DAS measurement data. The method may also include utilizing an apparent velocity-time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data. The method may further include utilizing a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity-time transform of the DAS measurement data. In addition, the method may include utilizing a density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters. The method may also include generating windowed data files that contain visualizations of the one or more seismic events.

[0009] Certain embodiments of the present disclosure also include a control system configured to minimize noise and boost seismic signals of DAS measurement data. The control system is also configured to utilize an apparent velocity -time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data. The control system is further configured to utilize a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity-time transform of the DAS measurement data. In addition, the control system is configured to utilize a density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters. The control system is also configured to generate windowed data files that contain visualizations of the one or more seismic events.

[0010] Certain embodiments of the present disclosure also include a tangible, non-transitory computer readable medium that includes processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: minimize noise and boost seismic signals of DAS measurement data; utilize an apparent velocity -time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data; utilize a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity-time transform of the DAS measurement data; utilize a density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters; and generate windowed data files that contain visualizations of the one or more seismic events.

[0011] Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:

[0013] FIG. 1 illustrates a schematic diagram of an oil and gas well system having a fiberoptic cable in a wireline deployment and a seismic source, in accordance with embodiments of the present disclosure;

[0014] FIG. 2 illustrates a schematic diagram of the oil and gas well system having the fiberoptic cable in a completion deployment and a seismic source, in accordance with embodiments of the present disclosure;

[0015] FIG. 3 illustrates a schematic diagram of the oil and gas well system having the fiberoptic cable in a permanent deployment and a seismic source, in accordance with embodiments of the present disclosure;

[0016] FIG. 4 is an illustration of how a distributed acoustic sensing (DAS) system uses light pulses propagating through a fiber-optic cable as an information carrier and optical fibers of the fiber-optic cable as the sensing medium to determine when seismic events have occurred, in accordance with embodiments of the present disclosure;

[0017] FIG. 5 illustrates a workflow for automated seismic event detection using DAS data, in accordance with embodiments of the present disclosure;

[0018] FIG. 6 illustrates an example of a scan window with a trace window size of 100 meters and a time window size of 0.8 second, in accordance with embodiments of the present disclosure; [0019] FIG. 7 illustrates coherency values in the v-t domain corresponding to the scan window illustrated in FIG. 6, in accordance with embodiments of the present disclosure;

[0020] FIG. 8 illustrates a 15-second data recording together with detected coherent signals indicated by dots, in accordance with embodiments of the present disclosure;

[0021] FIG. 9 illustrates coherent detections after applying DBSCAN, in accordance with embodiments of the present disclosure; and

[0022] FIG. 10 illustrates a visualization of saved window data containing a microseismic event, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

[0023] One or more specific embodiments of the present disclosure will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers’ specific goals, such as compliance with system-related and operation-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

[0024] Certain examples commensurate in scope with the originally claimed subject matter are discussed below. These examples are not intended to limit the scope of the disclosure. Indeed, the present disclosure may encompass a variety of forms that may be similar to or different from the examples set forth below. [0025] When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “certain embodiments,” “one embodiment,” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Furthermore, the phrase A “based on” B is intended to mean that A is at least partially based on B. Moreover, unless expressly stated otherwise, the term “or” is intended to be inclusive (e.g., logical OR) and not exclusive (e.g., logical XOR). In other words, the phrase A “or” B is intended to mean A, B, or both A and B.

[0026] As used herein, the terms "connect," "connection," "connected," "in connection with," and "connecting" are used to mean "in direct connection with" or "in connection with via one or more elements"; and the term "set" is used to mean "one element" or "more than one element." Further, the terms "couple," "coupling," "coupled," "coupled together," and "coupled with" are used to mean "directly coupled together" or "coupled together via one or more elements." As used herein, the terms "up" and "down," "uphole" and "downhole", "upper" and "lower," "top" and "bottom," and other like terms indicating relative positions to a given point or element are utilized to more clearly describe some elements. Commonly, these terms relate to a reference point as the surface from which drilling operations are initiated as being the top (e.g., uphole or upper) point and the total depth being the lowest (e.g., downhole or lower) point, whether the well (e.g., wellbore, borehole) is vertical, horizontal or slanted relative to the surface.

[0027] In addition, as used herein, the terms "real time", "real-time", or "substantially real time" may be used interchangeably and are intended to describe operations (e.g., computing operations) that are performed without any human -perceivable interruption between operations. For example, as used herein, data relating to the systems described herein may be collected, transmitted, and/or used in control computations in "substantially real time" such that data readings, data transfers, and/or data processing steps occur once every second, once every 0.1 second, once every 0.01 second, or even more frequently, during operations of the systems (e.g., while the systems are operating). In addition, as used herein, the terms "continuous", "continuously", or "continually" are intended to describe operations that are performed without any significant interruption. For example, as used herein, control commands may be transmitted to certain equipment every five minutes, every minute, every 30 seconds, every 15 seconds, every 10 seconds, every 5 seconds, or even more often, such that operating parameters of the equipment may be adjusted without any significant interruption to the closed-loop control of the equipment. In addition, as used herein, the terms "automatic", "automated", "autonomous", and so forth, are intended to describe operations that are performed are caused to be performed, for example, by a control system (i.e., solely by the control system, without human intervention).

[0028] In seismic sensing, near-surface seismic monitoring may have insufficient spatiotemporal resolutions caused by a limited seismic sensor density (e.g., limited by deployment difficulties or cost effectiveness). Recent development of optical fiber-based seismic sensing technologies such as distributed acoustic sensing (DAS) transforms fiber-optic cables into dense seismic sensor arrays, therefore providing the oil and gas industry with new options for seismic sensing. For instance, the DAS may provide higher sensor count, more flexible deployment, and long-term operation capability in comparison to particle motion sensors such as geophones and accelerometers. In certain embodiments, the DAS may include heterodyne Distributed Vibration Sensing (hDVS) that may enable new high-performance applications such as efficiently conducting borehole seismic and flow profiling applications.

[0029] The DAS may be used in various geophysical applications such as borehole seismic, surface seismic, shallow wellbore seismic, and so on. For example, DAS-based seismic acquisition systems may be used in borehole seismic to passively (e.g., without using controlled seismic sources) measure borehole seismic data for applications such as reservoir characterization and micro-seismic. The borehole seismic data may include seismic data (e.g., P-waves, S-waves, converted waves) measured using receivers (e.g., seismic sensors) in a well (e g., a cased well or an open well). The borehole seismic data may be measured by DAS systems during or after drillings of exploration and appraisal wells. In some cases, subsurface imaging may use 3D vertical seismic profile (VSP) technology for improved imaging quality (e.g., high resolutions). The DAS systems may reduce VSP acquisition time from a few hours (e.g., using conventional seismic operations) to a few minutes. In certain embodiments, a fiber-optic sensor array may be used in geophysical applications. Fiber-optic sensors may be based on the DAS. Unlike particle motion sensors, the fiber-optic sensors may measure strains caused by seismic waves traveling along the sensor array.

[0030] The disclosed methods and systems provide a robust and automated deterministic (i.e., analytically-based, non-machine-learning) process for seismic event detection using DAS data, which can serve as a fast alternative to machine learning-based solutions. In this approach, a workflow has been developed that fully exploits the coherency of the seismic waveform recorded by high-resolution DAS measurements for automated event detection in a real-time manner. The automatic deployment and performance of this approach requires minimal (or no) user intervention (i.e., no need for tuning parameters). The output of this method includes the time windows that contain seismic events. These can also be used as labeled data for the machine-learning algorithm training.

[0031] With the preceding in mind, turning now to the figures, FIG. 1 illustrates a schematic diagram of an oil and gas well system 10 having a fiber-optic cable in a wireline deployment and a seismic source. A distributed acoustic sensing (DAS) system 12 may be deployed in a borehole 14 that is drilled from a surface 16 into a subterranean formation 18. The DAS system 12 may be treated as densely distributed sensing points 20 of a fiber-optic cable 22. The fiber-optic cable 22 may measure strains caused by seismic wavefields traveling along the DAS system 12. An interrogator 24 may provide a light source (e.g., laser) 26 and a light recorder 28 configured to record light detections (e.g., detections of back scattered light signals from the sensing points 20 of the fiber-optic cable 22). In certain embodiments, the fiber-optic cable 22, the interrogator 24, and the other relevant devices or components (e.g., power supplies, control circuitry, cables) may form a Rayleigh scattering based DAS system 12, which may use the fiber-optic cable 22 to provide distributed strain sensing. That is, the fiber-optic cable 22 may become a sensing element, therefore enabling higher sensing point count (e.g., densely distributed fiber-optic sensing points 20), more flexible deployment (e.g., flexibility of the fiber-optic cable 22), and long-term operation capability (e.g., durability of the fiber-optic cable 22) in comparison to other seismic sensors such as geophones and accelerometers. The DAS system 12 may enable acoustic frequency strain signals to be detected over large distances (e.g., a length of the well) and in relatively harsh environments (e.g., a borehole environment).

[0032] Using the DAS system 12 may improve efficiencies of borehole seismic operations and reduce operational cost. Certain conventional borehole seismic tools may no longer be used in the borehole seismic operations. For example, operations like rigging loggers up and down along the borehole 14 may be eliminated or reduced as the fiber-optic sensing points 20 of the fiber-optic cable 22 are stationary in the borehole 14 while recording strains in conjunction with other stationary logging devices.

[0033] The fiber-optic cable 22 may include one or more optical fibers on which the fiberoptic sensing points 20 are distributed. The one or more optical fibers may be single mode or multi-mode optical fibers. In addition, in certain embodiments, the fiber-optic cable 22 may include one or more claddings to provide protections for the one or more optical fibers.

[0034] The borehole 14 may be surrounded by borehole casings 30. The borehole 14 may refer to a drilling well inside a wellbore wall or a rock face that bounds the drilling well. The borehole 14 may be a cased well or an open well. In certain embodiments, the borehole casings 30 may include pipes lowered into an open well and cemented in place. The borehole casings 30 may be configured to withstand a variety of forces, such as collapse, burst, and tensile, as well as chemically aggressive brines.

[0035] During borehole seismic acquisition, a passive seismic event 32 (e.g., earthquake) may generate a plurality of seismic wavefields 34 that travel in various directions within the subterranean formation 18, and some of the seismic wavefields 34 may arrive at the fiber-optic cable 22 fiber 22. The passive seismic event 32 may include, but is not limited to, naturally- occurring events such as earthquakes caused by tectonics, volcanic activity, tidal forces, and so forth; induced events by human activities like oil/gas production and fluid injections; and so forth. In certain embodiments, the fiber-optic cable 22 may be connected to a control system 36, and the interrogator 24, for example, via a wireline cable or other suitable cable. In certain embodiments, the interrogator 24 may be integrated into the control system 36. However, in other embodiments, the interrogator 24 may be separate from the control system 36. [0036] The control system 36 may be configured to control operations of the fiber, provide certain signal sources (e.g., light source for the fiber-optic cable 2), and receive and process data acquired by the fiber. In certain embodiments, the control system 36 may include one or more processor(s) 38, memory 40, storage 42, a display 44, and communication circuitry 46. The interrogator 24 may receive light signals from the fiber-optic sensors 20 and convert the light signals into fiber sensor data. The processor(s) 38 may receive the fiber sensor data from the interrogator 24. Data analysis and data processing based on the received data may be executed by the processor(s) 38 using processor-executable code stored in the memory 40 and/or the storage 42. The analyzed and processed data may be stored in the storage 42 for later usage. Analytic and processing results may be displayed via the display 44. Based on the analytic and processing results described in greater detail herein, the processor(s) 38 may adjust (e g., automatically adjust, in certain embodiments) operating parameters of the interrogator 24 (e.g., source light signals provided light by the light source 26) to adjust the borehole seismic acquisition. In certain embodiments, the processor(s) 38 may generate notification to users (e.g., well operators) based on the analytic and processing results via the communication interface.

[0037] In addition, as described in greater detail herein, the communication circuitry 46 may simultaneously stream processed raw data together with metadata or other processed results at the edge to an off-site (e.g., remote) data processing center 48.

[0038] As discussed above, the interrogator 24 may include a light source 26 that may provide source light signals (e.g., laser impulses) for the fiber-optic sensors 20. For example, in certain embodiments, the light source 26 may include wavelength tunable lasers (e.g., semiconductor lasers), such as distributed Bragg reflector (DBR) laser, vertical cavity surface-emitting laser (VCSEL), external cavity laser, distributed feedback (DFB) laser, or other suitable lasers. [0039] As also discussed above, the interrogator 24 may also include a light recorder 28 that may receive light signals (e.g., back scattered light signals associated with local measurement of dynamic strains caused by incident seismic wavefields 34) from the fiber-optic sensors 20, convert the light signals to electrical signals (e.g., using photodetectors), and further convert (e.g., digitalize) the electrical signals into the fiber sensor data. In certain embodiments, the photodetectors may include a PIN photodiode (e.g., InGaAs PIN, GaAs PIN, or Si PIN), an avalanche photodiode (e.g., InGaAs avalanche, GaAs avalanche, or Si avalanche), or other suitable photodetector (e.g., Schottky, GaP, Ge, InAs, InAsSb, or HgCdTe photodiode).

[0040] The processor(s) 38 may be any type of computer processor or microprocessor capable of executing computer-executable code. The processor(s) 38 may include single-threaded processor(s), multi -threaded processor(s), or both. The processor(s) 38 may also include hardware-based processor(s) each including one or more cores. The processors) 38 may include general purpose processor(s), special purpose processor(s), or both. The processor(s) 38 may be communicatively coupled to other internal components (such as interrogator 24, memory 40, storage 42, and display 44).

[0041] The memory 40 and the storage 42 may be any suitable articles of manufacture that can serve as media to store processor-executable code, data, or the like. These articles of manufacture may represent computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor(s) 38 to perform the presently disclosed techniques. The memory 40 and the storage 42 may also be used to store data described (e.g., fiber sensor data, and so forth), various other software applications for data analysis and data processing. In certain embodiments, the memory 40 and the storage 42 may include one or more databases to store additional data such as historical data (borehole seismic data acquired in previous operations) that may be used for borehole seismic monotoring. The memory 40 and the storage 42 may represent non-transitory computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor(s) 38 to perform various techniques described herein. It should be noted that non-transitory merely indicates that the media is tangible and not a signal.

[0042] The display 44 may operate to depict visualizations associated with software or executable code being processed by the processor(s) 38. In certain embodiments, the display 44 may be a touch display capable of receiving inputs from a user (e.g., a well operator or a data processor) of the control system 36. The display 44 may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. Additionally, in certain embodiments, the display 44 may be provided in conjunction with a touch-sensitive mechanism (e.g., a touch screen) that may function as part of a control interface for the control system 36. It should be noted that the components described above with regard to the control system 36 are example components and the control system 36 may include additional or fewer components as shown.

[0043] Besides a deployment illustrated in FIG. 1, in certain embodiments, the DAS system 12 may be deployed in other locations to acquire the borehole seismic data. For example, FIG. 2 illustrates a schematic diagram of the oil and gas well system 10 having the DAS system 12 in a completion deployment and the seismic source 32. The completion deployment may be used in a well completion, which is a process of configuring a well ready for a production (e.g., oil or gas) or an injection (e.g., CO2 injection). For example, the well completion may include running in production tubing 50 and associated downhole tools as well as perforating and stimulating. In other embodiments, the DAS system 12 may be coupled to the production tubing 50. For another example, FIG. 3 illustrates a schematic diagram of the oil and gas well system 10 having the DAS system 12 in a permanent deployment and the seismic source 32. The permanent deployment may be used in a production well after the well completion. The DAS system 12 may be permanently cemented behind the borehole casings 30.

[0044] FIG. 4 is an illustration of how the DAS system 12 uses light pulses 52 propagating through a fiber-optic cable 22 as an information carrier and optical fibers of the fiber-optic cable 22 as the sensing medium to determine when seismic events have occurred, as described in greater detail herein. As described above, an interrogation unit (e.g., the interrogator 24 illustrated in FIGS. 1-3) continuously creates laser pulses through the fiber-optic cable 22. When the light passes through the fiber cores of the fiber-optic cable 22, the incident light is scattered in different directions at scattering points 54 (e.g., Rayleigh scattering at density anomaly points) within the fiber-optic cable 22 due to spatial variations in the refractive index of the fiber cores of the fiberoptic cable 22, and backscattered light 56 is generated, which may propagate back through the fiber-optic cable 22. When optical fibers are subjected to strain, temperature, and vibrations (e.g., by seismic wavefields 34 incident upon the fiber-optic cable 22), the properties of the backscattered light 56 change (wavelength, light intensity, frequency, and so forth). By analyzing characteristics of the backscattered light 56, changes in various physical parameters (temperature, axial strain, and strain rate) may be analyzed to determine the occurrence of seismic events, as described in greater detail herein.

[0045] The embodiments described herein include a general workflow for automating the realtime seismic event detection process using an analytical method using DAS data. As described in greater detail herein, the method includes steps including data preprocessing, waveform transform, coherent signal detection, detection clustering, event filtering, and so forth, and is configured to detect and save the time windows that contains seismic events. The generated event list and associated time-windowed data can be further used for event location, magnitude estimation, and source mechanism inversion. This, in turn, can significantly reduce the data volume to process further - which is beneficial for data transfer and management.

[0046] FIG. 5 illustrates a workflow 58 of automated seismic event detection using DAS data, which may be performed by the control system 36 illustrated in FIGS. 1-3, as described in greater detail herein. As illustrated in FIG. 5, in certain embodiments, the workflow 58 may include first receiving DAS data, for example, as detected by the DAS system 12 illustrated in FIGS. 1 -4 (block 60). In certain embodiments, this may include receiving raw DAS data. In addition, in certain embodiments, the DAS data may be stored in sgy, tdms, or npy files. For sgy and tdms files, the number of traces, number of samples, sampling rate, and time difference between adjacent samples may be obtained from the file headers. In certain embodiments, a user may provide necessary data for a npy file. In certain embodiments, channel spacing is another parameter that may be used. The time duration of each file should be at least a few seconds long so that most of the waveforms may be completely captured in the file. The two-dimensional (2D) DAS data may be stored in a 2D array where each row represents the sample data for one trace.

[0047] As also illustrated in FIG. 5, in certain embodiments, the workflow 58 may also include data processing (block 62). Some data processing steps may be implemented to increase the signal - to-noise ratio (SNR). The data of interest may be defined by the trace range. Then, in certain embodiments, a median removal may be applied for each time sample. This step may remove the time-variant drift/background noise. In certain embodiments, a bandpass filter may be applied to remove the signals that are outside of a range of frequencies of interest. Finally, in certain embodiments, an L2 normalization may be applied to each trace for visualization purpose. It is noted that these are relatively simple data processing schemes that have negligible computational cost, but could clean the data for further analysis.

[0048] As also illustrated in FIG. 5, in certain embodiments, the workflow 58 may also include a waveform transform (block 64). The processed data may be divided into several scan windows. The size of the scan windows may be defined by the trace window size and time window size. For each scan window, the DAS data may be transformed into an apparent velocity-time (v-t) domain. The values in the v-t domain, calculated following the semblance definition, represent the coherency of the slant-stacked waveform with a velocity of v at each time t. For example, FIG. 6 illustrates an example of a scan window with a trace window size of 100 meters and a time window size of 0.8 second. FIG. 7 illustrates coherency values in the v-t domain corresponding to the scan window illustrated in FIG. 6. Clearly, the coherency values are much higher when there are coherent waveforms.

[0049] Returning to FIG. 5, in certain embodiments, the workflow 58 may also include coherent signal detection (block 66). After obtaining the coherency values in the v-t domain (see, e.g., FIG. 7). The coherent signals may be detected using a threshold-check method. If the value is higher than the threshold, there is a coherent detection at time t and apparent velocity v. The threshold semblance value may be adaptively calculated by direct stacking of the waveform, considering that the seismic waveforms always show the moveout pattern. In certain embodiments, there may also be a user-input minimum threshold, and the final threshold may be the larger value between the adaptively calculated value and the input value. FIG. 8 illustrates an example of a 15-second DAS data recording and the detected coherent signals indicated by the dots. These detections contain both seismic events and coherent noise. However, it is relatively straightforward to distinguish them, because there is usually a cloud of coherent detections, especially for a strong event. On the contrary, the detections are discrete around the coherent noises.

[0050] Returning to FIG. 5, in certain embodiments, the workflow 58 may also include detection clustering and filtering (blocks 68 and 70). In certain embodiments, DBSCAN, the density-based spatial clustering method, may be applied to group the detections into different clusters. After this step, the detected coherent signals indicated by the dots in FIG. 8 may be grouped into different clusters, as illustrated in FIG. 9. Additional criteria may be designed to filter out the clusters of noise as indicated by the clusters containing only a few detections. The additional screening criteria may include trace coverage, apparent velocity variations, correct moveout shape, and so forth.

[0051] Returning to FIG. 5, in certain embodiments, the workflow 58 may also include saving the time window and adding the time window to a catalog (block 72). In this action, the workflow 58 may include saving the time window containing the detected event into a file, which is illustrated in FIG. 10. In addition, the file name and some information associated with the detected event may be written to the event catalog stored in a file.

[0052] As such, the workflow 58 illustrated in FIG. 5 enables an automated real-time seismic event detection process for downhole DAS measurements. In certain embodiments, the workflow 58 may include a data preprocessing step 62 to suppress noise and boost seismic signals (e.g., as detected by the DAS system 12 illustrated in FIGS. 1-4). In addition, in certain embodiments, the workflow 58 may also include an apparent velocity-time transform step 64 that fully exploits the coherency of waveforms recorded by the high-resolution DAS data. In addition, in certain embodiments, the workflow 58 may also include a semblance-based event detection algorithm and an adaptive threshold calculation method, (e.g., step 66). In addition, in certain embodiments, the workflow 58 may also include a density -based clustering algorithm to group spatial coherent detections into different clusters (e.g., step 68).

[0053] As such, the workflow 58 may include an automated real-time seismic event detection algorithm for downhole DAS data that generates windowed data files that contain events (e.g., step 72). In certain embodiments, the workflow 58 may be applied to both vertical monitoring wells and horizontal monitoring wells with minimal (or no) user intervention. In addition, in certain embodiments, the algorithms of the workflow 58 may be parallelized to speed up data processing. [0054] As described in greater detail herein, the control system 36 illustrated in FIG. 1 may be configured to automatically (e.g., without human intervention) perform the steps of the workflow 58 described herein. In addition, the control system 36 may further be configured to automatically (e.g., without human intervention) adjust one or more operating parameters of detection of the DAS measurement data illustrated in FIGS. 1-4 (e.g., operating parameters of the interrogator 24) based at least in part on the data analysis performed in the workflow 58.

[0055] While the present disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and have been described in detail herein. However, it should be understood that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the present disclosure is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the following appended claims.

[0056] The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function]...” or “step for [performing [a function]...”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. 112(f).

Claims

1. A method for automating a real-time seismic event detection process based on downhole distributed acoustic sensing (DAS) measurement data, comprising: data preprocessing comprising minimizing noise and boosting seismic signals of the DAS measurement data; utilizing an apparent velocity-time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data; utilizing a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity-time transform of the DAS measurement data; utilizing a density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters; and generating windowed data fdes that contain visualizations of the one or more seismic events.

2. The method of claim 1 , wherein the method is automatically performed on a vertical monitoring well without user intervention.

3. The method of claim 1, wherein the method is automatically performed on a horizontal monitoring well without user intervention.

4. The method of claim 1, wherein each of the steps of the method are parallelized to speed up data processing.

5. The method of claim 1, wherein the data preprocessing comprises applying a bandpass filter on the DAS measurement data to remove seismic signals that are outside of a range of frequencies of interest.

6. The method of claim 1, wherein the data preprocessing comprises applying an L2 normalization on the DAS measurement data.

7. The method of claim 1, wherein utilizing the density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into the clusters is based at least in part on trace coverage, apparent velocity variations, correct moveout shape, or a combination thereof.

8. The method of claim 1, comprising automatically adjusting one or more operating parameters of detection of the DAS measurement data.

9. A control system configured to: minimize noise and boost seismic signals of distributed acoustic sensing (DAS) measurement data; utilize an apparent velocity-time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data; utilize a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity -time transform of the DAS measurement data; utilize a density-based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters; and generate windowed data files that contain visualizations of the one or more seismic events.

10. The control system of claim 9, wherein the control system is configured to parallelize the performed steps to speed up data processing.

11. The control system of claim 9, wherein minimizing the noise and boosting the seismic signals comprises applying a bandpass filter on the DAS measurement data to remove seismic signals that are outside of a range of frequencies of interest.

12. The control system of claim 9, wherein minimizing the noise and boosting the seismic signals comprises applying an L2 normalization on the DAS measurement data.

13. The control system of claim 9, wherein utilizing the density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into the clusters is based at least in part on trace coverage, apparent velocity variations, correct moveout shape, or a combination thereof.

14. The control system of claim 9, wherein the control system is configured to automatically adjust one or more operating parameters of detection of the DAS measurement data.

15. A tangible, non-transitory computer readable medium, comprising: processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: minimize noise and boost seismic signals of distributed acoustic sensing (DAS) measurement data; utilize an apparent velocity-time transform of the DAS measurement data based on coherency of waveforms of the DAS measurement data; utilize a semblance-based event detection algorithm and an adaptive threshold calculation to detect one or more seismic events based on a velocity-time transform of the DAS measurement data; utilize a density-based clustering algorithm to group spatially coherent detections of the one or more seismic events into clusters; and generate windowed data files that contain visualizations of the one or more seismic events.

16. The tangible, non-transitory computer readable medium of claim 15, wherein the processor-executable instructions, when executed by the at least one processor, cause the at least one processor to parallelize the performed steps to speed up data processing.

17. The tangible, non-transitory computer readable medium of claim 15, wherein minimizing the noise and boosting the seismic signals comprises applying a bandpass filter on the DAS measurement data to remove seismic signals that are outside of a range of frequencies of interest.

18. The tangible, non-transitory computer readable medium of claim 15, wherein minimizing the noise and boosting the seismic signals comprises applying an L2 normalization on the DAS measurement data.

19. The tangible, non-transitory computer readable medium of claim 15, wherein utilizing the density -based clustering algorithm to group spatially coherent detections of the one or more seismic events into the clusters is based at least in part on trace coverage, apparent velocity variations, correct moveout shape, or a combination thereof.

20. The tangible, non-transitory computer readable medium of claim 15, wherein the processor-executable instructions, when executed by the at least one processor, cause the at least one processor to automatically adjust one or more operating parameters of detection of the DAS measurement data.