This application is the National Stage of, and therefore claims the benefit of, International Application No. PCT/US2018/059260 filed on Nov. 5, 2018, entitled “SPATIALLY LOCATING A MICROSEISMIC EVENT UTILIZING AN ACOUSTIC SENSING CABLE,” which was published in English under International Publication Number WO 2020/096565 on May 14, 2020. The above application is commonly assigned with this National Stage application and is incorporated herein by reference in its entirety.
This application is directed, in general, to locating subsurface formation microseismic events, and more specifically, to utilizing an acoustic sensing cable to generate a three dimensional fracture model.
In the hydrocarbon production industry, there can be a need to identify and measure subsurface hydraulic fractures proximate to a borehole. Microseismic monitoring is often used to estimate the size and orientation of hydraulic fractures. Traditionally, the seismic sensors used for microseismic monitoring are geophones. Recently, fiber optic distributed acoustic sensing (DAS) cables are increasingly being used due to cost and operational advantages. Unlike geophone-based seismic sensing, which often requires drilling a dedicated (and non-producing) observation well to host the sensor array, a DAS cable can be deployed in any well, including the one that is being hydraulically fractured. The DAS method provides a longer sensing-aperture and a much denser spatial-sampling than most geophone arrays. It therefore has the potential to provide more-accurate positioning of microseismic events.
A major challenge with DAS microseismic monitoring can be that the DAS cable lacks broadside sensitivity in that it only records the component-of-motion that runs in the direction of the DAS cable. DAS cable cannot resolve the direction of incidence in the plane perpendicular to the cable. Consequently, when only a straight portion of a single acoustic sensing cable is available, the exact source-location of each microseismic event is undefined. What is known is that the true locations falls on a ring (or ring-like shape) around the fiber as opposed to a unique point in three dimensional (3D) space. By comparison, a conventional tri-axial geophone system is able to resolve the microseismic source-location to a singular position in 3D space due to its ability to resolve the directional polarization of seismic energy. This directional polarization is used to reduce the solution-space from a ring-shaped region to a single point. Due to the lack of directional polarization sensing in DAS, the interpretive value of DAS microseismic results are significantly reduced.
Reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIGS. 7A1, 7B1, 7C1, 7D1, and 7E1 are illustrations of charts of example overhead view, similar to
In the hydrocarbon production industry, certain well systems utilize a hydraulic fracturing process to enhance well production. Hydraulic fracturing (HF) is a well-stimulation technique in which pressurized fluid (primarily water, containing sand or other proppants) is injected into subsurface rock formations in order to create fractures, i.e., pathways for petroleum and/or natural gas to flow out more easily. Knowing how long, wide, and high a fracture is can be a useful diagnostic to know if the HF treatment is operating as intended. Additionally, fracture dimensions can be used to further optimize future HF treatments within the same or other wells. The dimensions can be used in conjunction with production modeling to estimate potential recoverable reserves associated with the well being treated.
During the HF process, the fluid injection can cause changes in stress in the subsurface, leading to the formation of fractures and the stress-induced failure of surrounding rock. Each sudden release of stress creates acoustic emissions which are seismic in nature. The majority of these emissions is very small, and are classified as “microseismic events” as they are generally too small in magnitude to be felt. Many of these microseismic events are detectable by conventional vibration sensors, such as geophones or accelerometers. Additionally, an acoustic sensing cable, deployed in a nearby well, can also be used to detect these microseismic events. One common kind of acoustic sensing cable is a fiber optic distributed acoustic sensing (DAS) cable.
The wavefield of seismic energy, released from a microseismic event, include a compressional wave (P-wave) and a shear wave (S-wave). Depending on the interaction between the seismic raypath and the stratigraphy around the well, these P-waves and S-waves can be received by the acoustic sensing cable as direct waves, reflected waves, and refracted waves. The arrival times of the P-waves and S-wave can be measured at points along the acoustic sensing cable. Given these arrival times, and an understanding of the rock properties (specifically the seismic velocity and velocity-anisotropy) in the region around the acoustic sensing cable, the source-location of the microseismic event can be computed. Only certain source-locations will create the P-wave and S-wave arrival times that are observed by the acoustic sensing cable. Computation of the most likely source-locations is performed by trial and error. After considering, potentially thousands, of source-locations, the best-matching source-location (or locations) are found. By mapping the source-locations of all microseismic events associated with a growing fracture, the approximate geometry of the fracture can be inferred.
Since the early 2000s, horizontal drilling has become a common practice in developing unconventional reserves. Horizontal wellbores can provide more efficient access to hydrocarbon rock formations than vertical wellbores. Horizontal wells can be hydraulically fractured in stages, where a stage can be associated with a segment or section of the wellbore. During each stage, fluid is injected into designated sections of the wellbore. This process typically creates a number of fractures within the rock that are oriented in a vertical plane which is perpendicular to the direction of minimum principal stress within the rock formation.
A stage of injection can have a duration of one to two hours. In other aspects, the duration can be shorter or longer. During the stage duration, HF induced microseismic events can be detected. The actual number of events detected can vary depending on the duration of the stage, the formation conditions, and other factors. It is common to detect 100 to 5000 microseismic events per stage. The actual number of microseismic events is a function of the geo-mechanical properties of the rock, the sensitivity of the monitoring system, the environmental noise, and the intensity of the HF treatment.
Traditionally, the seismic sensors used for microseismic monitoring are geophones. Recently, fiber-optic DAS has become attractive due to cost and operational advantages. While DAS is not the only kind of acoustic sensing cable, it is by far the most common. Other common acoustic sensing cables include fiberoptic cables with discrete (fiber Bragg grating) sensors, or piezoelectric hydrophone sensor cables.
An acoustic sensing cable can be installed in the HF wellbore, such as a same-well monitoring scenario, or in an adjacent well, such as an offset-well monitoring scenario. When a microseismic event occurs near the “heel” of the monitoring well, i.e., the general area where the vertical orientation portion of a well turns to a more horizontal orientation, seismic emissions (waves) from the microseismic event can be detectable on both the vertical and the horizontal portions of the acoustic sensing cable. The “L” shaped geometry allows for triangulation of the microseismic event source-location.
In contrast, when only the horizontal section of the cable is able to detect a microseismic event, the source-location of the microseismic event is highly non-unique. The P-wave and S-wave arrival times, observed on the acoustic sensing cable, can be created by a multitude of different source-locations. When the acoustic sensing cable is placed within a perfectly straight horizontal well, the set of plausible source-locations can only be determined to fall within a locus ring, i.e., a ring-shaped region of subsurface which has a high probability of containing the actual three dimensional (3D) position of the event. A specific point in 3D space cannot be determined.
This locus ring surrounds the fiber at a singular point along the fiber and the plane of the ring is perpendicular to the fiber at that point. This representative ring shape can be described by two parameters—the distance along the acoustic sensing cable to the centre of the ring, and the distance outward from the acoustic sensing cable to points along the ring, i.e., ring radius. There is only one true source-location, but its position within the locus ring is unknown due to the lack of knowledge of the seismic wavefield's directional polarization.
Since the 3D source-location of each individual microseismic event cannot be uniquely mapped, the subsequent step of identifying fracture geometry parameters from the microseismic event cloud is jeopardized. When the subsurface contains layers or regions with different rock velocities or material properties, the shape of the locus ring may include radius-discontinuities or warping, but the locus will generally retain a ring-like aspect.
This disclosure relates to a method of inferring the 3D locations, i.e., spatial locality, of microseismic events and calculating fracture geometry parameters, (i.e., properties, such as fracture azimuth, fracture half-length, fracture height, and microseismic cloud width), utilizing only a largely-straight segment of a single DAS cable, or more generally, an acoustic sensing cable. Although the source-location of each individual microseismic event is highly non-unique due to the directional ambiguity of acoustic sensing cables, it can be assumed, through previous experimentation and verification, that the bulk of microseismic events in the same HF stage, are associated with one, or a limited number of, fracture(s) and are therefore spatially-correlated. This correlation can be utilized to reduce the non-uniqueness in the source-location of each individual microseismic event and to derive the shape of the microseismic event cloud in 3D, allowing for identification of the fracture geometry parameters.
A catch percentage parameter can be utilized to determine the correlation. The catch percentage represents the percentage of microseismic events that can be associated with a fracture plane. There can be outlier microseismic events that are not associated with the fracture plane. Typically, a catch percentage parameter can be 70%. Other catch percentage parameters can be used.
Once the optimum, i.e., best fit, fracture plane azimuth(s) is determined, a 3D microseismic event cloud can be populated. For the locus rings that intersect with the fracture plane, the intersection can be one point, e.g., tangential, or two points. This technique can result in a cloud that is planar and not volumetric. Fracture geometry parameters, such as half-length and height, can be measured on this planar cloud of source-locations. In one aspect of this disclosure, the microseismic cloud-width is assumed to be zero. Computed this way, the half-length and height can be slightly overestimated. This error can be minimal while the true microseismic cloud width is small compared to the fracture parameter length or height. This is typically a safe assumption to make through previous experience.
Alternatively, the microseismic cloud width can be estimated by taking the maximum of the distances between the microseismic events' locus rings to the fracture plane. It is equivalent to gradually increasing the thickness of the fracture plane, thereby creating a representative fracture cuboid, until all microseismic events are encompassed, i.e., when the catch percentage parameter reaches 100.0%.
After the microseismic cloud width is determined, the 3D event cloud can be recomputed, since each microseismic event's locus ring now intersects a cuboid rather than a fracture plane. The resulting potential solutions form a continuous arc rather than one or two discrete points. One method to create discrete points can be to randomly choose a point from the candidate arc as the source-location of that microseismic event. Even though the source-location of individual microseismic events determined this way can differ from its actual source-location, the overall shape of the resulting microseismic event cloud can remain an acceptable approximation of the actual microseismic event cloud.
In an alternative aspect, the same (primary) methodology can be applied to a subset, instead of the entirety, of available microseismic events. The subset can be defined by different attributes, such as, a common computed seismic attribute, (e.g., event origin-time, magnitude, P-wave/S-wave amplitude ratio, and moment tensor solution), a common derived attribute, (e.g., distance from a subterranean feature, and cloud shape), and association with a particular phase of a HF fluid treatment plan, (e.g., portion of a well operation plan).
Another aspect can include a more complex locus shape, compared to utilizing a representative locus ring, in the 3D space. For example, when a non-homogeneous seismic velocity model is used for locating microseismic events, the resulting locus of solutions i.e., trajectory of possible source-locations, of each event can take on a more complex shape in 3D than a ring. The vertical fracture plane would intersect the complex-shaped locus in the same way as it intersects the ring. Such a non-homogeneous seismic velocity model can include complex stratigraphy, in which each layer is assigned unique seismic propagation properties, e.g., P-wave velocity, S-wave velocity, and anisotropy.
In another aspect, a-priori and independent information about the fracture plane can be utilized to assist or constrain the process of determining the optimal fracture plane. For example, knowledge of the regional stress orientation and dominant fracture plane orientation from a previously completed HF can be used to limit the search range for fracture azimuth.
Turning now to the figures,
The observation well 204 can be located remotely from the treatment well 202, near the treatment well 202, or at another suitable location, as long as the sensing equipment in observation well 204 can detect the microseismic events generated in treatment well 202. Borehole 211 can, but does not need to, include a bend from a generally vertical orientation to a generally horizontal orientation, identified as heel 213.
The offset-well monitoring system 200 can include one or more additional treatment wells, observation wells, and other well systems. The computing subsystem 210 can include one or more computing devices or systems located at the treatment well 202, at the observation well 204, or in other locations. The computing subsystem 210, and one or more sub-components, can be located apart from the other components shown in diagram 200. For example, the computing subsystem 210 can be located at a data processing center, a computing facility, and other suitable locations.
The offset-well monitoring system 200 can include additional or different features, and the features of the offset-well monitoring system 200 can be arranged as shown in diagram 200 and in other suitable configurations. The example treatment well 202 includes a borehole 201 in a subsurface zone 221 beneath the surface 206. In the example shown, the subsurface zone 221 includes various subsurface layers 222. The subsurface layers 222 can be defined by geological or other properties of the subsurface zone 221.
The well system controller 216 and pump 214 can apply a fluid treatment plan to the subsurface zone 221 through the borehole 201. The injection treatment can be a HF treatment that fractures the subsurface zone 221. For example, the injection treatment may initiate, propagate, or open fractures in one or more of the subsurface layers 222. A fracture treatment may include a mini fracture test treatment, a regular or full fracture treatment, a follow-on fracture treatment, a re-fracture treatment, a final fracture treatment, and other fracture treatments. The fracture treatment can pump in an injection fluid into, or pump out an injection fluid out of, the subsurface zone 221 at any suitable fluid pressure and fluid flow rate. Injection fluids can be pumped above, at, or below a fracture initiation pressure, above, at, or below a fracture closure pressure, or at other suitable combinations of these and other injection fluid pressures. A fracture treatment can be applied by any appropriate system, using any suitable technique.
The HF, i.e., fracture treatment, as well as other activities and natural phenomena, can generate microseismic events in the subsurface zone 221, and microseismic data can be collected from the subsurface zone 221. For example, the microseismic data can be collected by a single acoustic sensing cable 212 inserted in observation well 204. The microseismic information detected in the offset-well monitoring system 200 can include acoustic/seismic waves generated by natural phenomena, acoustic/seismic waves associated with a fracture treatment applied through the treatment well 202, or other seismic waves. For example, the acoustic sensing cable 212 can detect acoustic/seismic waves generated by rock slips, rock movements, rock fractures, and other snap, crackle, and pop events in the subsurface zone 221. Microseismic events in the subsurface zone 221 can occur, for example, along or near induced hydraulic fractures. The microseismic events can be associated with pre-existing natural fractures or hydraulic fracture planes induced by fracturing activities. In some environments, the majority of detectable microseismic events can be associated with shear-slip rock fracturing. Such events can correspond to induced tensile hydraulic fractures that have significant width generation.
The computing subsystem 210 can include a processor and analyzer component capable of analyzing microseismic data collected in the offset-well monitoring system 200. For example, the computing subsystem 210 can analyze microseismic data from a fracture treatment of the subsurface zone 221. Microseismic data from a fracture treatment can include data collected before, during, and after fluid injection. The computing subsystem 210 can receive the microseismic data at any suitable time. In some instances, the computing subsystem 210 receives the microseismic data in real time (or substantially in real time) during the fracture treatment. For example, the microseismic data may be sent to the computing subsystem 210 immediately upon detection by the acoustic sensing cable 212. In some instances, the computing subsystem 210 receives some or all of the microseismic data after the fracture treatment has been completed.
The computing subsystem 210 can receive the microseismic data in various suitable formats. For example, the computing subsystem 210 can receive the microseismic data in a format produced by microseismic sensors or detectors, or the computing subsystem 210 can receive the microseismic data after the data has been formatted, packaged, or otherwise processed. The computing subsystem 210 can receive the microseismic data by any suitable means. For example, the computing subsystem 210 can receive the microseismic data by a wired or wireless communication link, by a wired or wireless network, or by one or more disks or other tangible media.
Some of the techniques and operations described herein may be implemented by a computing subsystem 210 configured to provide the functionality described. In various aspects, a computing device may include various types of devices, including, but not limited to, personal computer systems, desktop computers, laptops, notebooks, mainframe computer systems, handheld computers, workstations, tab lets, application servers, storage devices, cloud data centers, or other types of computing or electronic devices.
The intersection of locus rings 324 and 334 occur at one or two points 341. A one point intersection can occur if locus ring 324 and locus ring 334 are tangential. Since both sections of the single acoustic sensing cable can detect the microseismic event, the possible source-location can be reduced to the intersection points 341, e.g., at most two possibilities.
The x-axis 401 is the distance along the single acoustic sensing cable. The length of the x-axis can be equal to the length of the single acoustic sensing cable, while in other aspects, the x-axis can be longer. In this example, an arbitrary point on the x-axis was identified as the 0.0 foot mark with the relative (absolute value) x distance increasing in both directions from the 0.0 foot mark. The y-axis 402 is the horizontal offset-distance from the observation well (containing the acoustic sensing cable) to the fracture well. The 0.0 foot mark of the y-axis 402 is defined as being at the observation well 410 line, i.e., at the single acoustic sensing cable.
Chart 420 is demonstrating a linear fit of two fracture planes, 430 and 432, on the 2D source-locations. This example shows that there are two fractures present in the subsurface formation, and it shows the likely 3D positioning of the fractures utilizing the collection of 2D source-locations. Chart 420 is demonstrating a 2D chart. A 3D chart using the data can be created to represent the fracture positioning within the subsurface formation. The fracture planes 430 and 432 may not exactly match the fracture azimuths as they exist in the subsurface formation, for example, the projected fracture planes can be 3-5° (degrees) different than the actual fracture azimuths. The fractures planes 430 and 432 can be within an acceptable error deviation of the well system operation plan.
In the offset-well system, the bulk of microseismicity is away from the observation well 410. The fracture azimuth(s) can be derived directly from the x-d plot of source-locations 434 via a linear regression algorithm.
Fracture azimuth(s) can be directly measured from the x-d plot because for offset-well systems, |y| (the absolute value of y) is much greater than |z|, where y is the overhead view distance of the microseismic event source-location across the acoustic sensing fiber and z is the vertical depth of the microseismic event source-location minus the depth of the fiber, |y|>>|z| therefore, d≡√{square root over (y2+z2)}≈|y|, therefore the x-d plot is a good approximation of the x-y plot so long as the sign of y is disregarded.
The assumed fracture plan 614 is oriented utilizing the azimuth angle α. Depending on the azimuth angle α, the number of intersection points between the assumed fracture plane 614 and the locus ring 612 can be two, one or zero. The intersection point 617 (and the hidden second point) can then be identified in 3D space, thereby confirming, or rejecting, the assumed fracture plane 614.
In a same-well system, the azimuth angle α cannot be directly obtained from the x-d plot, because the conditions |y|>>|z| and d≡√{square root over (y2+z2)}≈|y| do not hold true. Instead, the azimuth angle α can be derived via a grid search algorithm. A grid search over potential fracture azimuth angles α is conducted. For each α, a vertical fracture plane, such as plane 614, is defined. For each microseismic event, the locus ring of potential source-locations 612, centered at location 610, is cut by the intersecting vertical fracture plane. Each microseismic event's 3D location is reduced from infinite possibilities to (at most) two, one above, or at, and the other below, or at, the vertical depth of the single acoustic sensing cable. The solution set can be further reduced to simplify the analysis by, without loss of generosity, selecting the one above, or at, the vertical depth of the single acoustic sensing cable, i.e., z≥0. Thus, a discrete 3D event cloud can be generated for each value of α (see
When the trial azimuth angle α is smaller than the actual fracture plane azimuth (see
When the trial fracture plane azimuth angle α is greater than the actual fracture plane azimuth (see
The calculated 3D source-locations of microseismic events, i.e., the calculated microseismic event cloud, are shown by the dark gray plus symbols 706. The 3D source-location of each individual microseismic event is calculated by intersecting the trial fracture plane (not shown) with the locus ring of that microseismic event (see the method as described in
The 3D source-location of each individual microseismic event is calculated by intersecting the trial fracture plane (not shown) with the locus ring of that microseismic event (see the method as described in
The 3D source-location of each individual microseismic event is calculated by intersecting the trial fracture plane (not shown) with the locus ring of that microseismic event (see the method as described in
The calculated 3D source-locations of microseismic events, i.e., the calculated microseismic event cloud, are shown by the dark gray plus symbols 729. The 3D source-location of each individual microseismic event is calculated by intersecting the trial fracture plane (not shown) with the locus ring of that microseismic event (see the method as described in
The 3D source-location of each individual microseismic event is calculated by intersecting the trial fracture plane (not shown) with the locus ring of that microseismic event (see the method as described in
For each of the trial fracture plane azimuths, the catch percentage parameter can be plotted, shown by example as line 810. For each of the trial fracture plane azimuths, the calculated microseismic event cloud V-shaped angles can be plotted, shown by example as line 815. A minimum catch percentage parameter can be specified, such as 70.0%. Utilizing the specified minimum catch percentage parameter, the largest, i.e., most obtuse, V-shape angle on line 815 can be identified. That largest representative angle is likely the optimum fracture plane azimuth for the actual fracture plane, shown as optimum point 820. In other aspects, the calculated microseismic event cloud can contain outliers and more complex fracture geometries can be represented. In these cases, a lower catch parameter and a less stringent V-shape angle parameter can be used to identify the actual fracture plane azimuth.
In the above described examples, demonstrated through
Proceeding to a decision step 910, the process determines if the microseismic data were collected in an offset-well or same-well monitoring system. In the offset-well monitoring system, the fracture treatment well and observation well are distinct and separated by a gap of a certain size. In the same-well monitoring system, the fracture treatment well is observed from within the treatment well, or from a distinct well which is close enough that the microseismic events will overlap both the treatment well and observation well. If the decision step 910 determines that this is an offset-well, then the method proceeds to a step 915. In a step 915, a linear fit algorithm of the x-d plot is implemented. If decision step 910 determines that this is a same-well, then the method proceeds to a step 920 to implement a grid search algorithm.
From step 915 and step 920, the method 900 proceeds to a step 925 where an optimal fracture plane is determined. Proceeding to a step 930, the 2D locus ring of each microseismic event is intersected with the optimal fracture plane to populate a 3D microseismic event cloud. Proceeding to a step 935, fracture geometry parameters, (i.e., half-length, height, and cloud width), can be derived utilizing the 3D microseismic event cloud. The method 900 ends at a step 950.
Acoustic sensing controller 1110 is optically, electrically, or communicatively coupled to single acoustic sensing cable 1124. For optical sensing systems, acoustic sensing controller 1110 can initiate a light source to generate an optical signal through single acoustic sensing cable 1124 and to receive a return optical signal through single acoustic sensing cable 1124. For electrical sensing systems, acoustic sensing controller 1110 can supply power to acoustic sensors within the acoustic sensing cable 1124 and receive a return electrical or communicative readings from the sensors within the cable. Single acoustic sensing cable 1124 can be one or more of a general single acoustic sensing cable, a coherent Rayleigh interrogator cable, cable coated or treated with a material to enhance acoustic sensitivity, cable containing fiber Bragg grating reflectors, and cable including other enhancements and sensors to improve detection capabilities. Single acoustic sensing cable 1124 may also contain discrete acoustic sensing sensors such as hydrophones.
Fluid controller 1115 is connected to fluid pipe 1120. Fluid pipe 1120 extends partially or fully into borehole 1122. Fluid controller 1115 is capable to pump injection fluid into, and out of, borehole 1122 or a portion/section of borehole 1122. Fluid controller 1115 is also capable of changing the injection fluid pressure.
The components of apparatus 1100 can be combined or separated into one or more components. For example, in an alternative aspect, acoustic sensing controller 1110 can be combined with HF processor 1105. Other combinations are possible as well.
HF processor 1205 can provide commands, instructions, and information to the other components, as well as receive data and information. Acoustic sensing controller 1210 can receive commands and instructions from HF processor 1205 and can control and generate a light source. Acoustic sensing controller 1210 can be optically connected to single sensing cable 1211 and its light source can be utilized to send optical signals through the cable 1211. Cable 1211 can be inserted into a borehole that is part of well system 1230. Acoustic sensing controller 1210 can also receive optical signals from cable 1211. Alternatively, acoustic sensing controller 1210 can receive electrical measurements from discrete electronic acoustic sensors within the cable 1211. Acoustic sensing controller 1210 can interpret the received signals and communicate the interpreted information to HF processor 1205. Alternatively, acoustic sensing controller 1210 can communicate a digital signal, utilizing the received optical or electrical signal, to the HF processor 1205, where the digital signal data is communicating the received optical or electrical signal data.
Fluid controller 1215 can be fluidly connected to fluid pipe 1216 and communicatively coupled to HF processor 1205. Fluid pipe 1216 can extend from the fluid controller 1215 and be inserted partially or fully into a borehole that is part of well system 1230. Well system 1230 can comprise of one or more boreholes. Single sensing cable 1211 and fluid pipe 1216 can be inserted into the same or different borehole. Fluid controller 1215 can control injection fluid parameters, such as, how the injection fluid is pumped into, and out, of the borehole, the temperature of the injection fluid, the pressure applied to the injection fluid pumping action, and other parameters.
As acoustic sensing controller 1210 collects the received microseismic event source-locations from single sensing cable 1211, acoustic sensing controller 1210 can communicate the information in real-time, near real-time, delayed time, and in batch mode to hydraulic fracturing processor 1205. HF processor 1205 can utilize this information to adjust the commands and instructions issued to the other components. In some aspects, the HF processor 1205 can be supplemented by a human operator to assist in interpreting the collected source-locations.
HF processor 1205 can utilize the microseismic event source-locations collected to calculate fracture geometry parameters. There can be one or more sets of fracture geometry parameters. For example, there can be two or more fractures in the subsurface formation that generate microseismic events, and various combinations of subsets of source-locations can be utilized, such as computed attributes, derived attributes, and operational plan phases. This analyzed information can be communicated by HF processor 1205 to communicator 1220.
Communicator 1220 can be communicatively coupled, through communication 1226, to one or more of a computing device (computer, laptop, mobile device, server, and other device types), a network (private network, public network, Ethernet, TCP/IP, and other types of networks), a data storage system (database, hard disk, memory device, cloud storage, and other data storage device types), a display device (monitor, printer, and other display device types), and other electronic equipment (other well system controllers, equipment, and other well system associated devices).
A portion of the above-described apparatus, systems or methods may be embodied in or performed by various digital data processors or computers, wherein the computers are programmed or store executable programs of sequences of software instructions to perform one or more of the steps of the methods. The software instructions of such programs may represent algorithms and be encoded in machine-executable form on non-transitory digital data storage media, e.g., magnetic or optical disks, random-access memory (RAM), magnetic hard disks, flash memories, and/or read-only memory (ROM), to enable various types of digital data processors or computers to perform one, multiple or all of the steps of one or more of the above-described methods, or functions, systems or apparatuses described herein.
Portions of disclosed embodiments may relate to computer storage products with a non-transitory computer-readable medium that have program code thereon for performing various computer-implemented operations that embody a part of an apparatus, device or carry out the steps of a method set forth herein. Non-transitory used herein refers to all computer-readable media except for transitory, propagating signals. Examples of non-transitory computer-readable media include, but are not limited to: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as floptical disks; and hardware devices that are specially configured to store and execute program code, such as ROM and RAM devices. Examples of program code include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
In interpreting the disclosure, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced.
Those skilled in the art to which this application relates will appreciate that other and further additions, deletions, substitutions and modifications may be made to the described embodiments. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the claims. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, a limited number of the exemplary methods and materials are described herein.
It is noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.
Aspects disclosed herein include:
Each of aspects A, B, and C can have one or more of the following additional elements in combination:
Element 1: wherein the acoustic sensing cable is located in a horizontal portion of the first borehole. Element 2: wherein the acoustic emission source is a microseismic emission initiated by hydraulic fracturing of a subsurface fracture. Element 3: wherein the arrival time values are one or more of a measured compressional wave (P-wave) arrival times and a measured shear wave (S-wave) arrival times, where the P-wave and the S-waves are at least one of a direct wave, a reflected wave, and a refracted wave emitted by the microseismic emission source. Element 4: wherein the fracture plane approximately aligns with a lateral direction of the subsurface fracture. Element 5: wherein the hydraulic fracturing is initiated in said first borehole. Element 6: wherein the hydraulic fracturing is performed in a proximate second borehole. Element 7: wherein the number of the fracture planes can be one or more. Element 8: wherein the collection of acoustic emission sources is divided into sub-collections, where each sub-collection is associated to one of the fracture planes. Element 9: wherein a subset of the collection of acoustic emission sources is utilized for the identifying said orientation, where the subset is determined by the acoustic emission sources sharing a common attribute. Element 10: wherein the common attribute is a computed seismic attribute and is one or more of origin time, magnitude, P-wave to S-wave ratio, and moment tensor solution. Element 11: wherein the identifying the orientation utilizes a linear regression algorithm. Element 12: wherein the identifying the orientation utilizes a grid search algorithm, utilizing a targeted catch percentage parameter and a targeted V-shape artifact parameter. Element 13: wherein the linear regression algorithm or the grid search algorithm utilizes at least one a-priori parameter, including, regional stress orientation and dominant fracture orientation from a previous hydraulic fracturing. Element 14: further including, calculating a fracture geometry parameter utilizing the reduced sets of 3D positions. Element 15: wherein the fracture geometry parameter includes at least one of fracture azimuth, fracture half-length, fracture height, and fracture width. Element 16: further including, calculating a fracture geometry parameter utilizing the solution set. Element 17: wherein the identifying an orientation of a fracture parameter utilizes at least one of a grid search algorithm and a linear regression algorithm, and utilizes zero or more a-prior parameters. Element 18: wherein the first borehole and the second borehole are the same borehole. Element 19: wherein the hydraulic fracturing processor is operable to compute one or more fracture geometry parameters of the subsurface fracture, utilizing the solution set. Element 20: wherein the fracture geometry parameters comprise at least one of a length, a width, and a height of the subsurface fracture. Element 21: wherein more than one subsurface fracture is located in the first borehole, and the hydraulic fracturing processor is operable to compute fracture geometry parameters for at least one of the subsurface fractures. Element 22: wherein the acoustic sensing cable is a single fiber optic distributed acoustic sensing (DAS) cable. Element 23: wherein the acoustic sensing cable comprises fiber Bragg grating reflectors. Element 24: wherein the acoustic sensing cable is connected to a coherent Rayleigh interrogator. Element 25: wherein the acoustic sensing cable is treated to enhance acoustic sensitivity. Element 26: wherein the acoustic sensing cable is comprised of discrete acoustic sensors operable to capture measurements of acoustic emission, and operable to transmit the measurements. Element 27: further including, a fluid controller operable to cause microseismic events in the subsurface fracture utilizing, in the first borehole, an injection fluid pressure change and an injection fluid volume change.
Filing Document | Filing Date | Country | Kind |
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PCT/US2018/059260 | 11/5/2018 | WO |
Publishing Document | Publishing Date | Country | Kind |
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WO2020/096565 | 5/14/2020 | WO | A |
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Entry |
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James et al., Fracture Detection and Imaging Through Relative Seismic Velocity Changes Using Distributed Acoustic Sensing and Ambient Seismic Noise, Dec. 2017, The Leading Edge, Special Section: Fiber-Optic Distributed Sensing, pp. 1009-1017 (Year: 2017). |
Number | Date | Country | |
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20210318457 A1 | Oct 2021 | US |