Patent Application
20250155598
Seismic data is employed to infer characteristics about the subsurface, and has a variety of uses, including oilfield exploration and production, among others. Recently, there has been an interest in recognizing changes in the subsurface over relatively short, e.g., non-geological time scales, particularly in handling greenhouse gases through subsurface carbon sequestration. For example, carbon dioxide (CO2) may be injected into a subsurface formation through a well, generating a plume body. It may be desirable to analyze the position, shape, stability, and/or other characteristics of the CO2 plume body, which may change on such non-geological time scales. This type of analysis may also be applicable to reservoir production, e.g., the withdrawing of hydrocarbons from a reservoir.
In such applications, “time-lapse” seismic projects may be conducted for reservoir surveillance and CO2 monitoring. Time-lapse seismic data includes a baseline dataset and multiple monitoring datasets. The time-lapse difference between the baseline data and the monitoring data may be calculated to derive the subsurface property changes caused by oil/gas production or CO2 sequestration and its plume body migration, for example.
Direct subtraction of the baseline data from the monitoring data may result in “artifacts”, e.g., areas where desirable data signals have been removed or modified and/or where spurious signals have not been removed. Such artifacts may be caused by “non-repeatability” of time-lapse data, e.g., the influence of factors other than the presence of features of interest in the subsurface. These factors may include seasonal change of near surface conditions, water velocity change depending on the salinity and temperature, different source wavelets used in the baseline data acquisition and the monitoring data acquisition. These factors and others can lead to the measurements being influenced in a manner that is not repeatable, e.g., might be different each time a measurement is taken or otherwise difficult to anticipate.
Some data non-repeatability issues are accounted for in current processing schemes. For example, non-repeatability related to dislocation of sources and receivers can be effectively removed by data interpolation or data regularization procedures. However, many other data non-repeatability problems (e.g., near surface condition changes) are more difficult to solve, because the artifacts are generated according to a variety of factors, including changing surface environment, sensor arrangement and/or sensitivity, and/or other sources of noise, which makes modeling and adaptive subtraction thereof a challenge.
Embodiments of the disclosure include a method that includes receiving baseline data representing an area corresponding to a first time, receiving first measurement data representing a first portion of the area corresponding to a second time subsequent to the first time, training a machine learning model to reduce an influence of a non-repeatability factor based on a combination of the baseline data and the first measurement data, receiving second measurement data representing a second portion of the area corresponding to the second time, the second portion of the area including a feature of interest that was not present at the first time or that has changed between the first and second times, and modifying the second measurement data using the machine learning model to remove the non-repeatability factor in the second measurement data.
Embodiments of the disclosure include a computing system that includes one or more processors, and a memory storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving baseline data representing an area corresponding to a first time, receiving first measurement data representing a first portion of the area corresponding to a second time subsequent to the first time, training a machine learning model to reduce an influence of a non-repeatability factor based on a combination of the baseline data and the first measurement data, receiving second measurement data representing a second portion of the area corresponding to the second time, the second portion of the area including a feature of interest that was not present at the first time or that has changed between the first and second times, and modifying the second measurement data using the machine learning model to remove the non-repeatability factor in the second measurement data.
Embodiments of the disclosure include a non-transitory, computer readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations. The operations include receiving baseline data representing an area corresponding to a first time, receiving first measurement data representing a first portion of the area corresponding to a second time subsequent to the first time, training a machine learning model to reduce an influence of a non-repeatability factor based on a combination of the baseline data and the first measurement data, receiving second measurement data representing a second portion of the area corresponding to the second time, the second portion of the area including a feature of interest that was not present at the first time or that has changed between the first and second times, and modifying the second measurement data using the machine learning model to remove the non-repeatability factor in the second measurement data.
It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object could be termed a second object, and, similarly, a second object could be termed a first object, without departing from the scope of the invention. The first object and the second object are both objects, respectively, but they are not to be considered the same object.
The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.
Attention is now directed to processing procedures, methods, techniques and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques and workflows disclosed herein may be combined and/or the order of some operations may be changed. Although embodiments of the present disclosure are discussed in terms of seismic data acquisition, it will be appreciated that any sort of measurement and/or visual data may be processed using one or more of the embodiments discussed herein.
Computer facilities may be positioned at various locations about the oilfield 100 (e.g., the surface unit 134) and/or at remote locations. Surface unit 134 may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors. Surface unit 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit 134 may also collect data generated during the drilling operation and produce data output 135, which may then be stored or transmitted.
Sensors(S), such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. As shown, sensor(S) is positioned in one or more locations in the drilling tools and/or at rig 128 to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. Sensors(S) may also be positioned in one or more locations in the circulating system.
Drilling tools 106b may include a bottom hole assembly (BHA) (not shown), generally referenced, near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly includes capabilities for measuring, processing, and storing information, as well as communicating with surface unit 134. The bottom hole assembly further includes drill collars for performing various other measurement functions.
The bottom hole assembly may include a communication subassembly that communicates with surface unit 134. The communication subassembly is adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, or wired drill pipe communications. The communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other known telemetry systems.
Typically, the wellbore is drilled according to a drilling plan that is established prior to drilling. The drilling plan typically sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected
The data gathered by sensors(S) may be collected by surface unit 134 and/or other data collection sources for analysis or other processing. The data collected by sensors(S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be stored in separate databases, or combined into a single database.
Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations. Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 may then send command signals to oilfield 100 in response to data received. Surface unit 134 may receive commands via transceiver 137 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.
Wireline tool 106c may be operatively connected to, for example, geophones 118 and a computer 122a of a seismic truck 106a of
Sensors(S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, sensor S is positioned in wireline tool 106c to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the field operation.
Sensors(S), such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, the sensor(S) may be positioned in production tool 106d or associated equipment, such as Christmas tree 129, gathering network 146, surface facility 142, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation.
Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).
While
The field configurations of
Data plots 208a-208c are examples of static data plots that may be generated by data acquisition tools 202a-202c, respectively; however, it should be understood that data plots 208a-208c may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.
Static data plot 208a is a seismic two-way response over a period of time. Static plot 208b is core sample data measured from a core sample of the formation 204. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot 208c is a logging trace that typically provides a resistivity or other measurement of the formation at various depths.
A production decline curve or graph 208d is a dynamic data plot of the fluid flow rate over time. The production decline curve typically provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc.
Other data may also be collected, such as historical data, user inputs, economic information, and/or other measurement data and other parameters of interest. As described below, the static and dynamic measurements may be analyzed and used to generate models of the subterranean formation to determine characteristics thereof. Similar measurements may also be used to measure changes in formation aspects over time.
The subterranean structure 204 has a plurality of geological formations 206a-206d. As shown, this structure has several formations or layers, including a shale layer 206a, a carbonate layer 206b, a shale layer 206c and a sand layer 206d. A fault 207 extends through the shale layer 206a and the carbonate layer 206b. The static data acquisition tools are adapted to take measurements and detect characteristics of the formations.
While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield 200, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.
The data collected from various sources, such as the data acquisition tools of
The oilfield configuration of
Each wellsite 302 has equipment that forms wellbore 336 into the Earth. The wellbores extend through subterranean formations 306 including reservoirs 304. These reservoirs 304 contain fluids, such as hydrocarbons. The wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks 344. The surface networks 344 have tubing and control mechanisms for controlling the flow of fluids from the wellsite to processing facility 354.
Attention is now directed to
The component(s) of the seismic waves 368 may be reflected and converted by seafloor surface 364 (i.e., reflector), and seismic wave reflections 370 may be received by a plurality of seismic receivers 372. Seismic receivers 372 may be disposed on a plurality of streamers (i.e., streamer array 374). The seismic receivers 372 may generate electrical signals representative of the received seismic wave reflections 370. The electrical signals may be embedded with information regarding the subsurface 362 and captured as a record of seismic data.
In one implementation, each streamer may include streamer steering devices such as a bird, a deflector, a tail buoy and the like, which are not illustrated in this application. The streamer steering devices may be used to control the position of the streamers in accordance with the techniques described herein.
In one implementation, seismic wave reflections 370 may travel upward and reach the water/air interface at the water surface 376, a portion of reflections 370 may then reflect downward again (i.e., sea-surface ghost waves 378) and be received by the plurality of seismic receivers 372.
The sea-surface ghost waves 378 may be referred to as surface multiples. The point on the water surface 376 at which the wave is reflected downward is generally referred to as the downward reflection point.
The electrical signals may be transmitted to a vessel 380 via transmission cables, wireless communication or the like. The vessel 380 may then transmit the electrical signals to a data processing center. Alternatively, the vessel 380 may include an onboard computer capable of processing the electrical signals (i.e., seismic data). Those skilled in the art having the benefit of this disclosure will appreciate that this illustration is highly idealized. For instance, surveys may be of formations deep beneath the surface. The formations may typically include multiple reflectors, some of which may include dipping events, and may generate multiple reflections (including wave conversion) for receipt by the seismic receivers 372. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface 362.
Marine seismic acquisition systems tow each streamer in streamer array 374 at the same depth (e.g., 5-10 m). However, marine based survey 360 may tow each streamer in streamer array 374 at different depths such that seismic data may be acquired and processed in a manner that avoids the effects of destructive interference due to sea-surface ghost waves. For instance, marine-based survey 360 of
Embodiments of the present disclosure may include a method of suppressing the non-repeatability (in other words, enforcing repeatability) in time-lapse seismic data, e.g., using deep learning, such as a neural network or another type of machine learning model. In general, the method may first establish the nonlinear mapping between the baseline data and the monitoring data outside of an area of interest (AOI), which may include a feature of interest, such as an oil/gas reservoir area or a CO2 injection area. The method may then apply a trained network to the monitoring data, at least partially eliminating the difference between the monitoring data and the baseline data caused by factors other than changes in the area (e.g., production from reservoir or injection of CO2). In other words, after applying the data repeatability enforcement with this trained machine learning model, the difference between the baseline data and the monitoring data that does not represent the feature of interest may be reduced, e.g., minimized, while the time-lapse data difference representing the feature of interest may be generated based on the change in the feature of interest (e.g., production of oil from the reservoir, CO2 plume body change, etc.). It will be appreciated that embodiments of the present disclosure may be applied to raw pre-stack seismic data, processed pre-stack seismic data, post-stack seismic data, or seismic images, which may all be considered varieties of “seismic data”, broadly.
The method 400 may include preprocessing baseline and monitoring data, as at 402. The baseline data may be data collected at a “first” time. The first time may be prior to changes or potentially even the existence of the feature of interest in the subsurface area of interest, e.g., before production from a reservoir or before injection of CO2 to create a plume. The monitoring data may be collected at a later, “second” time. The second time may be when the feature of interest is apparent or has changed. The data may be seismic data, and thus seismic surveys may be employed to collect the data. Interpolation or data regularization may be implemented as part of the preprocessing to enable the source/receiver geometry of the baseline data and the monitoring data comparable, so as to permit direct comparison of the baseline data and measurement data. Accordingly, the baseline data and the monitoring data may represent the same area, but at different times. Thus, but for geological changes (e.g., presence of and/or changes in features of interest), the baseline and monitoring data sets would ideally be the same, but because of a variety of factors that contribute to the non-repeatability of the measurements, the two sets of data may differ.
The method 400 may then include determining an area of interest (AOI), as at 404. For example, the AOI may be determined based on a priori information or migration images, such that the seismic data acquired outside of the AOI are not affected by the reservoir property change or the CO2 plume body change. The AOI may be or include the location of one or more features of interest, such as a CO2 plume body, reservoir, etc.
The method 400 may also include generating a training data set, as at 406, and a testing data set, as at 408. The baseline data and the corresponding monitoring data acquired outside of the AOI may be used as the training and testing data sets. The extracted portion of the monitoring data may be the input into the deep learning network for the data repeatability enforcement. The corresponding baseline data may be used as the ground truth.
A machine learning model may then be trained using the training data set, as at 410. The extracted baseline and monitoring data may be input into the machine learning model (e.g., deep learning neural network) as training pairs. Thus, the machine learning model may be trained to force the monitoring data to more closely resemble the baseline data, e.g., by modifying the monitoring data. The difference between the output and the ground truth (the extracted baseline data), that is, the “residual”, may be back-propagated to the machine learning model to update the network parameters. This procedure may be repeated until the network is trained (i.e., the residual is reduced to a certain level or the residual value does not decrease anymore).
The trained machine learning model may then be implemented to modify testing data, e.g., to enforce repeatability and remove effects of factors that are not related to features of interest, as at 412. The difference between the input monitoring dataset (training data) and the baseline dataset may be caused by many mechanisms is removed except for the time-lapse data difference contributed from the reservoir property change, sequestered CO2, or CO2 plume body migration within the AOI.
In an example, the system 601 may be illustrative of collecting baseline data. That is, there is no feature of interest at the time represented in
The system 651 may be designed such that some of the seismic waves from the sources 652 propagate through the feature of interest 656 before reaching at least one of the receivers 654, and some of the seismic waves from the sources 652 do not propagate through the feature of interest 656 before reaching the receivers 654. Accordingly, two sets of seismic records may be provided by the system 651: one set that propagates through the feature of interest 656 and may thus be expected to differ from the seismic records recorded by the system 601 of
The method 700 may begin by receiving baseline data representing an area at a first time, before introducing or changing of a feature of interest, as at 702. As mentioned above, the feature of interest might be CO2 (e.g., a plume body) or a reservoir from which hydrocarbon is produced, among other possibilities. Thus, at this point, the CO2 may not yet have been injected (e.g., no CO2 plume body) or the reservoir may not have been produced (e.g., hydrocarbons extracted therefrom). It may also be the case that the reservoir has been partially produced, but another alteration (e.g., treatment such as fracturing) has not yet occurred, or simply that the reservoir is going to be produced more than it already has.
The method 700 may then include acquiring first measurement data representing the area, but not representing the feature of interest, as at 704. The first measurement data may be a subset of the monitoring data mentioned above with reference to
The method 700 may then include training a machine learning model to modify the first measurement data based on the baseline data, as at 706. As also noted above, if the first measurement data did not propagate through the feature of interest, in geological time, the area 600 of
The method 700 may then include acquiring second measurement data representing the area and including the feature of interest, as at 708. As explained above with reference to, and illustrated in,
The method 700 may then include modifying the second measurement data using the machine learning model that was trained in 708, as at 710. For example, the method 700 may be configured to change the amplitude, strength, polarity, etc., of the (e.g., seismic data) that is acquired, so as to remove or at least mitigate the influence of the non-repeatability factors from the second measurement data.
The method 700 may then include generating an enhanced image of the area including the feature of interest based on the modified second measurement data, as at 712. This generation of the enhanced image may, itself, be a practical application, capitalizing on the functionality of a computer to produce a digital image that accurately represents the subsurface (or any other area of interest). For example, displaying the digital image (e.g., using a computer display) may provide additional detail to a human operator about the existence, location, etc., of the feature of interest, which might have otherwise been too obscured to accurately recognize.
Further, the image may be used to determine characteristics of, performance of, and modifications to on-going or planned operations. For example, the change in shape, location, etc., of a CO2 plume may result in a user changing one or more parameters of the physical system being used to inject CO2. Similarly, change in the location, quality, content, etc., of the reservoir may impact production parameters and/or well plans for future wells in the reservoir. Various other practical applications for the enhanced images, which may provide better accuracy than legacy imaging techniques that retain the non-repeatability factors, may be apparent.
The workflow 800 may then feed this first measurement data 802 to a data repeatability enforcement machine learning model (e.g., convolutional neural network or CNN) 810. The CNN 810 may also be fed the baseline data 812 that was previously collected, such that the baseline data and the first measurement data 802 provide training pairs that represent the same regions within the area 600 (but outside of the feature of interest 656). Output 814 of the CNN 810 may be the modified version of the first measurement data 802, depicted as a seismic image 820, with the non-repeatability factors attenuated. Thus, the output 814 may be compared to the baseline data 812, and the residual differences therebetween used to further train the CNN 810.
The second measurement data 902 is then fed to the machine learning model (e.g., the CNN 810) that was trained according to the workflow 800 of
In one or more embodiments, the functions described can be implemented in hardware, software, firmware, or any combination thereof. For a software implementation, the techniques described herein can be implemented with modules (e.g., procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, classes, and so on) that perform the functions described herein. A module can be coupled to another module or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, or the like can be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, and the like. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
In some embodiments, any of the methods of the present disclosure may be executed using a system, such as a computing system.
A processor can include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
The storage media 1006 can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of
In some embodiments, computing system 1000 contains one or more repeatability enforcement module(s) 1008. In the example of computing system 1000, computer system 1001a includes the repeatability enforcement module 1008. In some embodiments, a single repeatability enforcement module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of repeatability enforcement modules may be used to perform some or all aspects of methods.
It should be appreciated that computing system 1000 is only one example of a computing system, and that computing system 1000 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of
Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are all included within the scope of protection of the invention.
Geologic interpretations, models and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to embodiments of the present methods discussed herein. This can include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 1000,
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.
This application claims priority to U.S. Provisional Patent Application Ser. No. 63/364,484, which was filed on May 10, 2022, and is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2023/021706 | 5/10/2023 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63364484 | May 2022 | US |
