Bin constraints for generating a histogram of microseismic data

Systems, methods and software can be used for processing microseismic data from a subterranean region. In some aspects, groupings of data points are identified. The data points are based on microseismic data from a subterranean region. The identification of the groupings is constrained such that each grouping includes at least a minimum number of the data points, and such that the data points in each grouping have at most a maximum extent of variation. In some instances, a histogram of the data points is generated, and each of the identified groupings corresponds to a bin in the histogram.

CROSS-REFERENCE TO RELATED APPLICATION

This application is the National Stage of, and therefore claims the benefit of, International Application No. PCT/US2014/036067 filed on Apr. 30, 2014, entitled “BIN CONSTRAINTS FOR GENERATING A HISTOGRAM OF MICROSEISMIC DATA,” which was published in English under International Publication Number WO 2015/167502 on Nov. 5, 2015. The above application is commonly assigned with this National Stage application and is incorporated herein by reference in its entirety.

BACKGROUND

This specification relates to generating a histogram of microseismic data.

Microseismic data are often acquired in association with hydraulic fracturing treatments applied to a subterranean formation. The hydraulic fracturing treatments are typically applied to induce artificial fractures in the subterranean formation, and to thereby enhance hydrocarbon productivity of the subterranean formation. The pressures generated by the fracture treatment can induce low-amplitude or low-energy seismic events in the subterranean formation, and the events can be detected by sensors and collected for analysis.

DETAILED DESCRIPTION

FIG. 1Ashows a schematic diagram of an example well system100with a computing subsystem110. The example well system100includes a treatment well102and an observation well104. The observation well104can be located remotely from the treatment well102, near the treatment well102, or at another location. The well system100can include one or more additional treatment wells, observation wells, or other types of wells. The computing subsystem110can include one or more computing devices or systems located at the treatment well102, at the observation well104, or in other locations. The computing subsystem110or any of its components can be located apart from the other components shown inFIG. 1A. For example, the computing subsystem110can be located at a data processing center, a computing facility, or another location. The well system100can include additional or different features, and the features of the well system can be arranged as shown inFIG. 1Aor in another configuration.

The example treatment well102includes a well bore101in a subterranean zone121beneath the surface106. The subterranean zone121can include all or part of a rock formation, or the subterranean zone121can include more than one rock formation. In the example shown inFIG. 1A, the subterranean zone121includes various subsurface layers122. The subsurface layers122can be defined by geological, stratigraphic, or other properties of the subterranean zone121. For example, each of the subsurface layers122can correspond to a particular lithology, a particular fluid content, a particular stress or pressure profile, or another characteristic. In some cases, one or more of the subsurface layers122can be a fluid reservoir that contains hydrocarbons or other types of fluids. One or more of the subsurface layers122can include sandstone, carbonate materials, shale, coal, mudstone, granite, or other materials.

The example treatment well102includes an injection treatment subsystem120, which includes instrument trucks116, pump trucks114, and other equipment. The injection treatment subsystem120can apply an injection treatment to the subterranean zone121through the well bore101. The injection treatment can be a fracture treatment that fractures the subterranean zone121. For example, the injection treatment may initiate, propagate, or open fractures in one or more of the subsurface layers122. A fracture treatment may include a mini fracture test treatment, a regular or full fracture treatment, a multi-stage fracture treatment, a follow-on fracture treatment, a re-fracture treatment, a final fracture treatment or another type of fracture treatment.

The fracture treatment can inject a treatment fluid into the subterranean zone121, for example, at one or more fluid pressures or fluid flow rates. Fluids can be injected above, at or below a fracture initiation pressure, above at or below a fracture closure pressure, or at a combination of these and other fluid pressures. The fracture initiation pressure for a formation is the minimum fluid injection pressure that can initiate or propagate artificial fractures in the formation. Application of a fracture treatment may or may not initiate or propagate artificial fractures in the formation. The fracture closure pressure for a formation is the minimum fluid injection pressure that can dilate existing fractures in the subterranean formation. Application of a fracture treatment may or may not dilate natural or artificial fractures in the formation.

In the example shown inFIG. 1A, the pump trucks114may include mobile vehicles, immobile installations, skids, hoses, tubes, fluid tanks or reservoirs, pumps, valves, or other structures and equipment. In some cases, the pump trucks114are coupled to a working string disposed in the well bore101. During operation, the pump trucks114can pump fluid through the working string and into the subterranean zone121. The pumped fluid can include a pad, proppants, a flush fluid, additives, or other materials.

A fracture treatment can be applied at a single fluid injection location or at multiple fluid injection locations in a subterranean zone, and the fluid may be injected over a single time period or over multiple different time periods. In some cases, a fracture treatment can use multiple different fluid injection locations in a single well bore, multiple fluid injection locations in multiple different well bores, or a combination of these. Moreover, the fracture treatment can inject fluid through a well bore, such as, for example, vertical well bores, slant well bores, horizontal well bores, curved well bores, or a combination of these and others.

In the example shown inFIG. 1A, the instrument trucks116can include mobile vehicles, immobile installations, or other structures. The instrument trucks116can include an injection control system that monitors and controls the fracture treatment applied by the injection treatment subsystem120. In some implementations, the injection control system can communicate with other equipment to monitor and control the injection treatment. For example, the instrument trucks116may communicate with the pump truck114, subsurface instruments, and monitoring equipment.

The fracture treatment, as well as other activities and natural phenomena, can generate microseismic events in the subterranean zone121, and microseismic data can be collected from the subterranean zone121. For example, the microseismic data can be collected by one or more sensors112associated with the observation well104, or the microseismic data can be collected by other types of systems. The microseismic information detected in the well system100can include acoustic signals generated by natural phenomena, acoustic signals associated with a fracture treatment applied through the treatment well102, or other types of signals. For example, the sensors112may detect acoustic signals generated by rock slips, rock movements, rock fractures or other events in the subterranean zone121. In some cases, the locations of individual microseismic events can be determined based on the microseismic data.

Microseismic events in the subterranean zone121may occur, for example, along or near induced pre-existing natural fractures or hydraulic fracture planes induced by fracturing activities. The orientation of a fracture can be influenced by the stress regime, the presence of fracture systems that were generated at various times in the past (e.g., under the same or a different stress orientation).

The observation well104shown inFIG. 1Aincludes a well bore111in a subterranean region beneath the surface106. The observation well104includes sensors112and other equipment that can be used to detect microseismic information. The sensors112may include geophones or other types of listening equipment. The sensors112can be located at a variety of positions in the well system100. InFIG. 1A, sensors112are installed at the surface106and beneath the surface106in the well bore111. Additionally or alternatively, sensors may be positioned in other locations above or below the surface106, in other locations within the well bore111, or within another well bore. The observation well104may include additional equipment (e.g., working string, packers, casing, or other equipment) not shown inFIG. 1A. In some implementations, microseismic data are detected by sensors installed in the treatment well102or at the surface106, with or without the use of an observation well.

In some cases, all or part of the computing subsystem110can be contained in a technical command center at the well site, in a real-time operations center at a remote location, in another location, or a combination of these. The well system100and the computing subsystem110can include or access a communication infrastructure. For example, well system100can include multiple separate communication links or a network of interconnected communication links. The communication links can include wired or wireless communications systems. For example, sensors112may communicate with the instrument trucks116or the computing subsystem110through wired or wireless links or networks, or the instrument trucks116may communicate with the computing subsystem110through wired or wireless links or networks. The communication links can include a public data network, a private data network, satellite links, dedicated communication channels, telecommunication links, or a combination of these and other communication links.

The computing subsystem110can analyze microseismic data collected in the well system100. For example, the computing subsystem110may analyze microseismic event data from a fracture treatment of a subterranean zone121. Microseismic data from a fracture treatment can include data collected before, during, or after fluid injection. The computing subsystem110can receive the microseismic data at one or more time periods. In some cases, the computing subsystem110receives 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 subsystem110immediately upon detection by the sensors112. In some cases, the computing subsystem110receives some or all of the microseismic data after the fracture treatment has been completed. The computing subsystem110can receive the microseismic data, for example, in a format produced by microseismic sensors or detectors, or in another format (e.g., after the microseismic data has been formatted, packaged, or otherwise processed).

The computing subsystem110can be used to generate a histogram based on microseismic events. The histogram can be used, for example, to identify dominant fracture orientations in the subterranean zone121.FIG. 2shows an example of a histogram. The dominant fracture orientations can be identified, for example, based on local maxima in the histogram data. The dominant fracture orientations can correspond to the orientations of fracture families in the subterranean zone121. In some cases, the microseismic data corresponding to each dominant fracture orientation are used to generate one or more fracture planes.

Some of the techniques and operations described herein may be implemented by a computing subsystem configured to provide the functionality described. In various embodiments, a computing device may include any of various types of devices, including, but not limited to, personal computer systems, desktop computers, laptops, notebooks, mainframe computer systems, handheld computers, workstations, tablets, application servers, storage devices, or another of computing system or electronic device.

FIG. 1Bis a diagram of the example computing subsystem110ofFIG. 1A. The example computing subsystem110can be located at or near one or more wells of the well system100or at a remote location. All or part of the computing subsystem110may operate independent of the well system100or independent of any of the other components shown inFIG. 1A. The example computing subsystem110includes a processor160, a memory150, and input/output controllers170communicably coupled by a bus165. The memory can include, for example, a random access memory (RAM), a storage device (e.g., a writable read-only memory (ROM) or others), a hard disk, or another type of storage medium. The computing subsystem110can be preprogrammed or it can be programmed (and reprogrammed) by loading a program from another source (e.g., from a CD-ROM, from another computer device through a data network, or in another manner). The input/output controller170is coupled to input/output devices (e.g., a monitor175, a mouse, a keyboard, or other input/output devices) and to a communication link180. The input/output devices receive and transmit data in analog or digital form over communication links such as a serial link, a wireless link (e.g., infrared, radio frequency, or others), a parallel link, or another type of link.

The communication link180can include any type of communication channel, connector, data communication network, or other link. For example, the communication link180can include a wireless or a wired network, a Local Area Network (LAN), a Wide Area Network (WAN), a private network, a public network (such as the Internet), a WiFi network, a network that includes a satellite link, or another type of data communication network.

The memory150can store instructions (e.g., computer code) associated with an operating system, computer applications, and other resources. The memory150can also store application data and data objects that can be interpreted by one or more applications or virtual machines running on the computing subsystem110. As shown inFIG. 1B, the example memory150includes microseismic data151, geological data152, fracture data153, other data155, and applications156. In some implementations, a memory of a computing device includes additional or different information.

The microseismic data151can include information on the locations of microseisms in a subterranean zone. For example, the microseismic data can include information based on acoustic data detected at the observation well104, at the surface106, at the treatment well102, or at other locations. The microseismic data151can include information collected by sensors112. In some cases, the microseismic data151has been combined with other data, reformatted, or otherwise processed. The microseismic event data may include information relating to microseismic events (locations, magnitudes, uncertainties, times, etc.). The microseismic event data can include data collected from one or more fracture treatments, which may include data collected before, during, or after a fluid injection.

The geological data152can include information on the geological properties of the subterranean zone121. For example, the geological data152may include information on the subsurface layers122, information on the well bores101,111, or information on other attributes of the subterranean zone121. In some cases, the geological data152includes information on the lithology, fluid content, stress profile, pressure profile, spatial extent, or other attributes of one or more rock formations in the subterranean zone. The geological data152can include information collected from well logs, rock samples, outcroppings, seismic imaging, or other data sources.

The fracture data153can include information on fracture planes in a subterranean zone. The fracture data153may identify the locations, sizes, shapes, and other properties of fractures in a model of a subterranean zone. The fracture data153can include information on natural fractures, hydraulically-induced fractures, or any other type of discontinuity in the subterranean zone121. The fracture data153can include fracture planes calculated from the microseismic data151. For each fracture plane, the fracture data153can include information (e.g., strike angle, dip angle, etc.) identifying an orientation of the fracture, information identifying a shape (e.g., curvature, aperture, etc.) of the fracture, information identifying boundaries of the fracture, or other information.

The applications156can include software applications, scripts, programs, functions, executables, or other modules that are interpreted or executed by the processor160. Such applications may include machine-readable instructions for performing one or more of the operations represented inFIGS. 4 and 5. The applications156may include machine-readable instructions for generating a user interface or a plot, such as, for example, the histogram represented inFIG. 2. The applications156can obtain input data, such as microseismic data, geological data, or other types of input data, from the memory150, from another local source, or from one or more remote sources (e.g., via the communication link180). The applications156can generate output data and store the output data in the memory150, in another local medium, or in one or more remote devices (e.g., by sending the output data via the communication link180).

The processor160can execute instructions, for example, to generate output data based on data inputs. For example, the processor160can run the applications156by executing or interpreting the software, scripts, programs, functions, executables, or other modules contained in the applications156. The processor160may perform one or more of the operations represented inFIG. 4 or 5, or it may generate the histogram shown inFIG. 2. The input data received by the processor160or the output data generated by the processor160can include any of the microseismic data151, the geological data152, the fracture data154, or the other data155.

FIG. 2is a plot showing an example histogram200. The example histogram200shown inFIG. 2is a graphical representation of the distribution of basic plane orientations identified from a set of microseismic data. A histogram can be generated based on other types of data, and a histogram can represent other types of information. The example histogram200can be generated by the example techniques represented inFIGS. 4 and 5, or by another technique.

The example histogram200shown inFIG. 2includes a plot of a surface206representing fracture plane orientation probabilities. In some cases, a histogram includes another type of plot. For example, a histogram can convey the same or similar information by a bar plot, a topographical plot, or another type of plot. In the example shown inFIG. 2, each fracture plane orientation is represented by two variables—the strike angle and the dip angle. A histogram can be used to represent a distribution of quantities over one variable, two variables, three variables, or more.

The surface206shown inFIG. 2is plotted in a three-dimensional coordinate system. Some example histograms are plotted in two dimensions (e.g., for a distribution over a single variable), three dimensions (e.g., for a distribution over two variables), or four dimensions (e.g., for a distribution over two variables over time). In the example shown inFIG. 2, the three-dimensional coordinate system is represented by the vertical axis204aand the two horizontal axes204band204c. The horizontal axis204brepresents a range of dip angles, and the horizontal axis204crepresents a range of strike angles (units of degrees). The vertical axis204arepresents a range of probabilities.

Parameters of the histogram200can be computed, for example, by generating bins that each represent a distinct orientation range or grouping. For example, a bin can represent a range of strike angles and a range of dip angles. In some instances, each bin corresponds to a grouping of data points, and the range for each individual bin is based on the data points one of the groupings. For example, the groupings can be identified based on the example process shown inFIG. 5, and a histogram bin can be created for each identified grouping. In the histogram200shown inFIG. 2, each of the histogram bins corresponds to an intersection of sub-ranges along the horizontal axes204band204c.

Additional parameters of the histogram200can be computed, for example, by computing the quantity of fracture orientations associated with each bin. In the histogram200shown inFIG. 2, the quantity for each bin is represented by the level of the surface206for each of the groupings represented in the plot. The quantities represented inFIG. 2are normalized probability values. Generally, the quantity for each bin in a histogram can be a normalized quantity or a non-normalized quantity. For example, the quantity of fracture planes for each bin can be a probability value, a frequency value, an integer number value, or another type of value.

The quantity of fracture planes for each bin of the histogram can be computed, for example, by assigning each fracture plane, by assigning each identified grouping of fracture planes to a bin, or by a combination of these and other techniques. In some cases, the fracture planes are basic planes defined by microseismic data points, and each of the basic planes defines an orientation corresponding to one of the bins.

The example histogram200represents the probability distribution of basic planes associated with180microseismic events. In this example, each bin represents a sub-range of strike values within the strike range indicated in the histogram200(0° through 360°) and a sub-range of dip values within the dip range indicated in the histogram200(60° through 90°). The surface206map exhibits several local maxima (peaks), five of which are labeled as208a,208b,208c,208d, and208einFIG. 2.

The peaks in the histogram200represent the bins associated with higher quantities than surrounding bins. The bins represented by the peaks correspond to a set of fracture planes having similar or parallel orientations. In some cases, each local maximum (or peak) in the histogram can be considered as corresponding to a dominant (i.e., principal) orientation trend. An orientation trend can be considered a dominant fracture orientation, for example, when more basic planes are aligned along this direction than along its neighboring or nearby directions. A dominant fracture orientation can represent a statistically significant quantity of basic planes that are either parallel, substantially parallel, or on the same plane.

The example shown inFIG. 2is a histogram based on two angular parameters of each basic plane (i.e., strike and dip angles). A histogram can be based on other parameters of the basic planes. For example, a third parameter of each basic plane can be incorporated in the histogram data. The third parameter can be, for example, the distance d of the basic plane from the origin. A histogram can be generated for distance-related parameters, orientation-related parameters, or combinations of them. In some examples, a histogram can be generated for the values d tan (θ) and d tan (φ) for each basic plane, based on the distance d of each basic plane from the origin, the strike angle φ of each basic plane, and the dip angle θ of each basic plane. In some cases, a two dimensional histogram can be generated based on any two independent variables, such as, for example, tan (θ), tan (φ), the strike angle φ, the dip angle θ, or others.

FIGS. 3A and 3Bare plots showing an example fracture plane orientation.FIG. 3Ashows a plot300aof an example basic plane310defined by three non-collinear microseismic events306a,306b, and306c.FIG. 3Bshows a plot300bof the normal vector308for the basic plane310shown inFIG. 3A. InFIGS. 3A and 3B, the vertical axis304arepresents the z-coordinate, the horizontal axis304brepresents the x-coordinate, and the horizontal axis304crepresents the y-coordinate. The plots300aand300bshow a rectilinear coordinate system; other types of coordinate systems (e.g., spherical, elliptical, etc.) can be used.

As shown inFIG. 3A, the basic plane310is a two-dimensional surface that extends through the three-dimensional xyz-coordinate system. The normal vector308indicates the orientation of the basic plane310. A normal vector can be a unit vector (a vector having unit length) or a normal vector can have non-unit length.

As shownFIG. 3B, the normal vector308has vector components (a, b, c). The vector components (a, b, c) can be computed, for example, based on the positions of the microseismic events306a,306b, and306c, based on the parameters of the basic plane310, or based on other information. In the plot300b, the x-component of the normal vector308is represented as the length a along the x-axis, the y-component of the normal vector308is represented as the length b along the y-axis, and the z-component of the normal vector308is represented as the length c along the z-axis. (In the example shown, the y-component b is a negative value.)

The orientation of the basic plane310can be computed from the normal vector308, the microseismic events themselves, parameters of the basic plane310, other data, or any combination of these. For example, the dip θ and the strike φ of the basic plane310can be computed from the normal vector308based on the equations

θ=arc⁢⁢tan⁢a2+b2c,φ=arc⁢⁢tan⁢ba.(1)
In some cases, computational techniques can account for and properly manage the sensitivity of these equations in extreme cases, for example, where the parameter a or c is very small.

In some cases, the orientation of one or more basic planes can be used as input for generating histogram data. For example, a histogram of the basic plane orientations can be generated from a set of basic planes. In some cases, the histogram data is generated by assigning each basic plane to a grouping based on the basic plane's orientation (θ, φ) and computing the quantity of basic planes associated with each bin. In some cases, the histogram is plotted, or the histogram data can be used or processed without displaying the histogram.

FIG. 4is a flow chart of an example process400for identifying dominant fracture orientations. Some or all of the operations in the process400can be implemented by one or more computing devices. In some implementations, the process400may include additional, fewer, or different operations performed in the same or a different order. Moreover, one or more of the individual operations or subsets of the operations in the process400can be performed in isolation or in other contexts. Output data generated by the process400, including output generated by intermediate operations, can include stored, displayed, printed, transmitted, communicated or processed information.

In some implementations, some or all of the operations in the process400are executed in real time during a fracture treatment. An operation can be performed in real time, for example, by performing the operation in response to receiving data (e.g., from a sensor or monitoring system) without substantial delay. An operation can be performed in real time, for example, by performing the operation while monitoring for additional microseismic data from the fracture treatment. Some real time operations can receive an input and produce an output during a fracture treatment; in some cases, the output is made available to a user within a time frame that allows the user to respond to the output, for example, by modifying the fracture treatment.

In some cases, some or all of the operations in the process400are executed dynamically during a fracture treatment. An operation can be executed dynamically, for example, by iteratively or repeatedly performing the operation based on additional inputs, for example, as the inputs are made available. In some cases, dynamic operations are performed in response to receiving data for a new microseismic event (or in response to receiving data for a certain number of new microseismic events, etc.).

At402, microseismic data from a fracture treatment are received. For example, the microseismic data can be received from memory, from a remote device, or another source. The microseismic event data may include information on the measured locations of multiple microseismic events, information on a measured magnitude of each microseismic event, information on an uncertainty associated with each microseismic event, information on a time associated with each microseismic event, etc. The microseismic event data can include microseismic data collected at an observation well, at a treatment well, at the surface, or at other locations in a well system. Microseismic data from a fracture treatment can include data for microseismic events detected before, during, or after the fracture treatment is applied. For example, in some cases, microseismic monitoring begins before the fracture treatment is applied, ends after the fracture treatment is applied, or both.

At404, coplanar subsets of microseismic events are identified. A coplanar subset of microseismic events can include three microseismic events or more than three microseismic events. For example, each subset can be a triplet of microseismic event locations. In some cases, the coplanar subsets are identified by identifying all triplets in a set of microseismic event data. For example, for N microseismic event locations, N(N−1)(N−2)/6 triplets can be identified. In some cases, less than all triplets are identified as subsets. For example, some triplets (e.g., collinear or substantially collinear triplets) may be excluded.

At406, a basic plane is identified for each coplanar subset of microseismic events. For example, a basic plane can be identified by calculating the parameters of a basic plane based on a triplet of microseismic event locations. In some cases, a plane can be defined by the three parameters a, b, and c of a basic plane model. These parameters can be calculated based on the x, y and z coordinates of three non-collinear points in a subset, for example, by solving a system of linear equations for the three parameters. For example, the parameters of a plane defined by three non-collinear events (x1, y1, z1), (x2, y2, z2) and (x3, y3, z3) can be computed based on solving the following system of equations:

At412, the quantity of basic planes in each of a plurality of groupings is calculated. In some cases, each grouping can be used to generate a respective bin in a histogram. In some cases, each covers an independent, discrete sub-range of orientations. The bins may collectively cover a full range of basic plane orientations, or the bins may collectively cover multiple adjoining or non-adjoining sub-ranges of orientations. Each individual bin may correspond to a solid angle in three-dimensional space. A solid angle can be defined, for example, by a range of dip angles and a range of strike angles, or by angular ranges based on combinations of the strike angle and the dip angle.

In some implementations, the orientation ranges for each grouping are pre-computed values. For example, the grouping can be determined independent of the basic plane orientations. In some implementations, groupings are determined based on the orientations of the basic planes identified at406. For example, as shown inFIG. 4, the basic planes can be sorted based on the orientation values at408, and the groupings can be identified from the sorted basic planes at410(e.g., using the technique shown inFIG. 5or another technique). The groupings can be identified at410in a number of different manners.FIG. 5, discussed in more detail below, depicts an example of an iterative method for identifying groupings of data points.

The quantity of basic plane orientations in each grouping can be a probability value, a frequency value, an integer number of planes, or another type of value. For example, the quantity of basic planes in a given grouping can be the number of basic planes having a basic plane orientation associated to the given grouping. As another example, the quantity of basic planes in a given grouping can be the number of basic planes having a basic plane orientation associated to the given orientation range, divided by the total number of basic planes identified. The quantities can be normalized, for example, so that the quantities sum to one (or another normalization value).

At414, dominant fracture orientations are identified from the quantities calculated at412. The dominant fracture orientations can be identified, for example, as the groupings having the local higher maxima of basic plane orientations. In some cases, the dominant fracture orientations are identified based on the local maxima in histogram data generated from the quantities. A single dominant fracture orientation can be identified, or multiple dominant fracture orientations can be identified. In some cases, a dominant fracture orientation is identified based on the height, width, and other parameters of a peak in the histogram data. The dominant fracture orientation can be identified as the center point of a grouping, the dominant fracture orientation can be computed as the mean orientation of basic planes in the grouping, or the dominant fracture orientation can be computed in another manner.

A dominant fracture orientation identified from the quantities calculated at412can represent the orientation of physical fractures within the subterranean zone. In some rock formations, fractures typically form in sets (or families) having parallel or similar orientations. Some formations include multiple sets of fractures. For example, a formation may include a first set of fractures having a primary orientation, which may be dictated by a maximum stress direction. A formation may also include a second set of fractures having a secondary orientation, which is different from the primary orientation. The secondary orientation may be separated from the primary orientation, for example, by ninety degrees or by another angle. In some cases, each of the dominant fracture orientations corresponds to the orientation of a fracture set in a subterranean zone.

At416, a histogram of the basic plane orientation values is displayed. The histogram indicates the quantity of basic plane orientations in each of the groupings. An example histogram is shown inFIG. 2. The quantities can be displayed in another format or as another type of histogram. A histogram can be plotted, for example, in two dimensions or three dimensions. In some cases, the histogram is plotted as a continuous line or surface, as an array of discrete glyphs (e.g., a bar chart), as topographical regions, or as another type of graphical presentation. In addition to presenting a histogram, or as an alternative to presenting a histogram, the basic plane orientation values can be presented as numerical values, algebraic values, a numerical table, or in another format.

At418, fracture planes are generated. The fracture planes can be generated, for example, based on the microseismic data points and the dominant fracture orientations identified at414. In some cases, a grouping of microseismic events associated with each of the dominant fracture orientations is identified, and a fracture plane is generated from each grouping. In some cases, the fracture planes are identified based on the locations and other parameters of the measured microseismic events. For example, a fracture can be generated by fitting the individual groupings of microseismic events to a plane. Other techniques can be used to generate a fracture plane.

In some cases, the histogram is displayed in real time during the fracture treatment, and the histogram can be updated dynamically as additional microseismic events are detected. For example, each time a new microseismic event is received, additional basic planes can be identified and the quantity of basic planes in each grouping can be updated accordingly. In some cases, the groupings are also updated dynamically as microseismic data is received.

FIG. 5shows an example process410for identifying groupings of data points. The example process410can be implemented as an iterative process that receives a set of data points, and groups the data points according to predetermined criteria or constraints. In some implementations, the data points represent basic plane orientations or other parameters of basic planes, or the data points may represent other information based on microseismic data from a fracture treatment.

Some or all of the operations in the example process410shown inFIG. 5can be implemented by one or more computing devices. In some implementations, the process410may include additional, fewer, or different operations performed in the same or a different order. Moreover, one or more of the individual operations or subsets of the operations in the process410can be performed in isolation or in other contexts. Output data generated by the process410, including output generated by intermediate operations, can include stored, displayed, printed, transmitted, communicated or processed information.

In the example shown inFIG. 5, the groupings are iteratively determined by repeatedly identifying groupings and adding data points or removing data points (or both) in each grouping based on predetermined constraints. In some cases, the predetermined constraints can include a minimum number of data points in a grouping, a maximum extent of variation of the data points in each grouping, or a combination of these and other constraints. The minimum number of data points can refer to a threshold number of data points that must be included in some or all of the groupings. For example, the minimum number of data points can be a constant integer value for all groupings. The maximum extent of variation can refer to a maximum extent to which the data points in an individual grouping, on average, are permitted to deviate from the other data points in the grouping. For example, the maximum extent of variation can be a maximum standard deviation or another measure of variance. For example, the following equation describes n groupings in a given set of data points, where the ithgrouping is supported by Nidata points:
[n,Ni|i=1n]=f(Nmin, σbin)  (3)
where Nminrepresents the minimum number of data points in a grouping and the σbinrepresents the local standard deviation associated with the grouping. In some implementations, some or all of the predetermined constraints can be specific to one or more groupings. In some implementations, the predetermined constraints are the same for all groupings.

In some cases, an “un-associated” (UA) grouping can be identified. The UA grouping may include one or more data points that cannot be added to any of the other groupings without preventing the grouping to meet the predetermined constraints. In some cases, the UA grouping can be a measure of the quality of the collected data set. For example, a high number of data points in the UA grouping may indicate that the data set includes a lot of “noises.” In some cases, the data points in the UA grouping are not included in the further steps of calculating the qualities of basic planes (e.g.,412inFIG. 4).

To this end, at502, multiple data points are identified. As described previously, the data points can be generated based on microseismic data from a subterranean region. In some cases, the data points may represent basic planes, each defined by a coplanar subset of microseismic events and having an orientation relative to a common axis. As described previously, in some cases, the data points can be sorted based on orientation values of the basic planes (e.g., at408inFIG. 4); or the data point can be unsorted.

At504, one or more predetermined constraints are determined In the example shown, the predetermined constraints include Nmin, the minimum number of data points in a grouping. In some implementations, the one or more predetermined constraints can include a maximum extent of variation of the data points in a grouping. In some implementations, Nminand the other predetermined constraints can be determined independent of the data points. In some implementations, one or more of the predetermined constraints can be determined based on the data points, for example, based on the number of the data points, the mean of the data points, the standard deviation of the data points, or other characteristics of the data points. In some implementations, one or more predetermined constraints can be determined based on user inputs, based on information stored in databases or calculated in real time, or other information.

In the example shown inFIG. 5, operations510,512,520,522,530,532,534and540can be iterated for each grouping to be identified. In some cases, the iterations can end when all the data points in the data set are allocated to their respective groupings or identified as unassociated. The example iterative process inFIG. 5begins at510, where a new grouping, here the first grouping, is identified. In some cases, a minimum number of data points are identified to be included in the first grouping. For example, the first grouping may include the first Nminnumber of data points in the data set.

At512, an additional data point is added to the current grouping. In some cases, the additional data point can be the next data point after the first Nminnumber of data points in the data set. At520, the first grouping, accounting for the additional data point, is evaluated to determine whether the first grouping meets the predetermined constraints. For example, the extent of variation of the first grouping can be calculated to determine whether the predetermined maximum extent of variation is exceeded. In some implementations, as described previously, the maximum extent of variation can be a maximum standard deviation. In such a case, the standard deviation of the first grouping, accounting for the additional data point, is calculated and compared to the maximum standard deviation. If the standard deviation of the first grouping, accounting for the additional data point, does not exceed the maximum standard deviation, at522, the additional data point is accepted in the first grouping.

Operations512,520, and522are repeated for each additional data point being added, until the current grouping no longer meets the predetermined constraints. When the first grouping does not meet the predetermined constraints, at530, the number of data points in the first grouping is compared to the minimum number of data points in a grouping to determine whether the first grouping has sufficient number of data points. For example, if the predetermined constraints include a maximum standard deviation, and the current grouping, accounting for the additional data point, has a standard deviation that is larger than the maximum standard deviation, the additional data point will not be accepted in the first grouping. Instead, at530, the number of data points in the first grouping is compared to Nmin. If the number of data points in the first grouping is greater than or equal to Nmin, the first grouping has sufficient number of data points. In such a case, the iterative process proceeds to510, where a subsequent grouping is identified. In some cases, the subsequent groupings include the next minimum number of data points in the data set. The iterative process may then continue to512and520, where an additional data point is added to the subsequent grouping, and the subsequent grouping is evaluated to determine whether the subsequent grouping meets the predetermined constraints.

If the number of data points in a grouping (e.g., the first grouping or the subsequent grouping) is determined to be smaller than Nmin(e.g., at530), one or more data points in the grouping may be removed. At532, a further determination is made to evaluate whether removing one or more data points in the grouping can cause the grouping to meet the predetermined constraints. In some implementations, the extent of variation of the grouping, excluding one or more data points but accounting for the additional data point, is compared to the extent of variation of the grouping without the additional data point. For example, if the predetermined constraints include a maximum standard deviation and the data points in the grouping are sorted, a temporary standard deviation of the grouping, excluding the first data point but including the additional data point, is calculated. The temporary standard deviation is then compared to the standard deviation of the grouping that includes the first data point but excludes the additional data point. If the temporary standard deviation is greater than or equal to the standard deviation, removing data points does not decrease the extent of variation of the grouping. Therefore, removing data points does not cause the grouping to meet the predetermined constraints.

If the temporary standard deviation is smaller than the standard deviation, removing the first data point may decrease the extent of variation of the grouping. In such a case, further tests can be performed to determine whether removing one or more data points may cause the grouping to meet the predetermined constraints. For example, the temporary standard deviation can be compared with the maximum standard deviation. If the temporary standard deviation does not exceed the maximum standard deviation, removing the first data point does cause the grouping to meet the predetermined constraints. If the temporary standard deviation exceeds the maximum standard deviation, a second temporary standard deviation may be calculated. The second temporary standard deviation may be calculated based on the data points in the grouping that exclude the first two data points but include the additional data point. The second temporary standard deviation may be compared to the temporary standard deviation to determine whether removing the second data point continues to decrease the extent of variation of the grouping. If the second temporary standard deviation is greater than or equal to the temporary standard deviation, removing the second data point does not further reduce the extent of variation of the grouping, and therefore removing data points does not cause the grouping to meet the predetermined constraints. If the second temporary standard deviation is smaller than the temporary standard deviation, removing the second data point continues to reduce the extent of variation of the grouping. In such a case, the second temporary standard deviation can be compared with the maximum standard deviation to determine whether the predetermined constraints are met. This process may be repeated until it is determined whether removing one or more data points can cause the grouping to meet the predetermined constraints.

If removing one or more data points in the grouping causes the grouping, accounting for the additional data point, to meet the predetermined constraints, at534the one or more data points are removed from the grouping and the additional data point is accepted in the grouping. The removed data points can be allocated to the UA grouping. The iterative process continues to512, where an additional data point is added, and further tests are performed to determine whether the grouping has at least Nminnumber of data points and meets the predetermined constraints.

If removing one or more data points in the grouping does not cause the grouping, accounting for the additional data point, to meet the predetermined constraints, at540, the additional data point is rejected. The rejected data point can be allocated to the UA grouping. The example iterative process continues to512, where an additional data point is added.

In some instances, the operations512-540can be repeated for each additional data point being added, until a grouping meets the predetermined constraints. In such a case, the iterative process continues to510, where a subsequent grouping is identified. The operations510-540can be repeated until all the data points in the data set are allocated to their respective groupings.

As described previously, operations520and532can include repeated evaluations of standard deviations. In some implementations, the standard deviation can be computed by evaluating the mean and then calculating the standard deviation. For example, the standard deviation (σ) of data points (Xi) in a grouping including N data points can be computed based on the following equations, where μ represents the mean of the grouping:

In some implementations, the standard deviation may be calculated in an incremental manner to take advantage of the fact that the data points are added to a grouping incrementally. Such a method may be more efficient and therefore may save computational cost. For example, a subsequent mean (μn) and a subsequent standard deviation (σn) of a grouping of n data points, including an additional data point (Xn), can be computed based on the mean (μn−1) and the standard deviation (σn−1) of the grouping that does not include the additional data point. The following equations are examples of this technique:

Bins can be identified from the groupings of data points. In some cases, each grouping can correspond to a respective bin in the histogram, and can subsequently be used in operations412-418ofFIG. 4described above to identify dominant fracture orientations, generate a histogram of the basic plane orientations, and generate fracture planes.

In some implementations, the grouping techniques described in connection withFIG. 4can be adapted to the known properties about the data points in the data set. For example, a user may know that a grouping that meets the predetermined constraints and has a minimum number of data points does not exist in some regions. In such a case, all the data points in these regions may be allocated directly to the UA grouping. Alternatively or in combination, the predetermined constraints and the minimum number of data points may be tuned for data points in these regions to adjust for the known properties of these regions.

The grouping techniques described in connection withFIG. 5, can be performed on real-time data, post data, or a combination of real-time and post data. In instances where the grouping is performed on real-time (or other non-post data), the algorithms can be operated to update the identified fracture orientations as new data comes in. When new data comes in, whether it is a single microseismic event or multiple microseismic events, the techniques described in connection withFIG. 4andFIG. 5can be performed to generate updated fracture planes and/or generate an updated histogram of the basic plane orientations. In some cases, grouping techniques can reach the same solution regardless of whether the analysis is performed on entirely post data, on partially post data and partially non-post data, or on entirely non-post data (including, real-time data).

In instances, where the initial data points are grouped with an adaptive technique, such as described in connection withFIG. 5, assimilating a new data point into the groupings can necessitate some or all of the groupings be redefined. For example, including a new data point in an existing grouping may change the extent of variation of the grouping. The change may prevent the grouping from meeting the predetermined constraints. In some cases, one or more data points may be removed from the grouping to cause the grouping to meet the predetermined constraints. The removed data points may be included in adjacent groupings, which may in turn prevent the adjacent groupings from meeting the predetermined constraints. Therefore, as new data points are assimilated into the groupings, the groupings may be re-evaluated and the existing data points re-associated with different groupings. In some cases, the new data points and/or the removed data points can be allocated to the UA grouping.

FIG. 6Ais a plot showing an example data set600. The plot shown inFIG. 6Ais a graphical representation of the distribution of data points in the example data set600. In some cases, data points in the example data set600can represent microseismic data gathered from a hydraulic fracturing process, or another type of data. For example, the sample values of the data points can represent basic plane orientations or other information derived from microseismic data. In the example shown inFIG. 6A, a two-dimensional coordinate system is represented by the horizontal axis620and the vertical axis610. The horizontal axis620represents the index of data points in the example data set600. The vertical axis610represents the values of the data points in the example data set600.

FIG. 6Bis a plot showing groupings of data points in the example data set600ofFIG. 6Aaccording to the example process500shown inFIG. 5. In the example shown inFIG. 6B, groupings650,652,654,656and658are identified. The UA grouping650includes data points that may represent unsuitable data (e.g., noise) for further calculation. In the example shown, the grouping technique identifies four distinct patterns and allocates data points into the groupings652,654,656, and658according to these patterns. Each of the groupings652,654,656, and656has different characteristics, which may indicate four fracture planes based on the seismic data gathered from the hydraulic fracturing process.

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. A computer can include a processor that performs actions in accordance with instructions, and one or more memory devices that store the instructions and data. A computer may also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic disks, magneto optical disks, or optical disks. However, a computer need not have such devices. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, flash memory devices, and others), magnetic disks (e.g., internal hard disks, removable disks, and others), magneto optical disks, and CD ROM and DVD-ROM disks. In some cases, the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

A number of examples have been described. Various modifications can be made without departing from the scope of the present disclosure. Accordingly, other embodiments are within the scope of the following claims.