Automatic Dip Picking From Azimuthal Borehole Images

BACKGROUND

Modern petroleum drilling and production operations demand a great quantity of information relating to the parameters and conditions downhole. Such information typically includes the location and orientation of the borehole and drilling assembly, earth formation properties, and drilling environment parameters downhole. The collection of information relating to formation properties and conditions downhole is commonly referred to as “logging” and can be performed during the drilling process itself.

Various azimuthal borehole measurement tools exist for use in logging while drilling. One such tool is the resistivity tool, which includes one or more antennas for transmitting an electromagnetic signal into the formation and one or more antennas for receiving a formation response. Additionally, other azimuthal measurement tools may exist such as density imaging tools. These tools may be utilized to measure different properties of a formation through resistivity measurement using electromagnetic signals.

Generally, formations may be electrically isotropic or electrically anisotropic. If a formation is electrically isotropic, the resistivities measured at the various depths of investigation by such a resistivity logging tool will be the same. However, if the resistivities corresponding to the various depths of investigation are different, such differences indicate that the formation being measured is electrically anisotropic. In electrical anisotropic formations, the anisotropy is generally attributable to the interface between geological formation beddings. Geological formation beddings may be described in a formation coordinate system. A formation coordinate system may be oriented such that the x-y plane is parallel to the formation layers and the z axis is perpendicular to the formation layers. Formation bedding may also be described using a dip angle.

A dip angle θ may be the inclination from the x-y plane and the bed boundary between two geological formation beddings at a given depth. Dip picking may incorporate determining one or more dip angles θ at one or more depths. During drilling operations dip picking may be performed by determining multiple dip angles θ from azimuthal borehole images. Herein, azimuth borehole images may be obtained from measuring formation properties such as resistivity or density in different azimuthal directions during drilling operations. Additionally, dip picking may be utilized to determine the angle of formation beddings. The angle of formation beddings may provide useful information for geosteering. This may allow for the navigation along a pre-designed drilling path and indicate in real time the location of the bottom hole assembly within a formation. The real time location of the bottom hole assembly allows for the bottom hole assembly to follow a pre-designed drilling path. Currently, dip picking is an inefficient manual process because it is time-consuming, tedious, and subject to human errors.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some examples of the present disclosure and should not be used to limit or define the disclosure.

FIG. 1 illustrates a well during drilling operations.

FIG. 2 illustrates an example information handling system.

FIG. 3 illustrates another example information handling system.

FIG. 4 illustrates an example of one arrangement of resources in a computing network.

FIG. 5 illustrates measurement assembly.

FIG. 6A illustrates azimuthal borehole image with formation bedding.

FIG. 6B illustrates the unrolling process for unrolled azimuth borehole image.

FIG. 6C illustrates sliced azimuth borehole image fully unrolled.

FIG. 7 illustrates workflow for dip picking from a borehole image.

FIG. 8 illustrates workflow for dip picking from a borehole image with expanded actions.

FIG. 9A illustrates a graph of an amplitude phase graph with uniform amplitude axis.

FIG. 9B illustrates a graph of an amplitude phase graph with non-uniform amplitude axis.

FIG. 9C illustrates amplitude axis is non-uniform based on the dip feature of a tangent function.

FIG. 10A is an example of a borehole image.

FIG. 10B is an example of a gradient image.

FIG. 10C is an example of a summation voting image.

FIG. 10D is an example of the median voting image.

FIG. 10E is an example of a detected dips image.

FIG. 11 illustrates the evaluation of dip symmetry for a “good” and a “bad” dip.

DETAILED DESCRIPTION

Methods and systems herein may generally relate to methods and systems for autonomous dip picking during drilling operations. Additionally, LWD (logging while drilling) tools may perform dip picking using measurements taken downhole during a drilling operation. Measurements taken downhole may comprise formation properties such as resistivity or density in different azimuthal directions at one or more depths. During measurement operations, formation properties such as resistivity or density may be processed into an azimuthal borehole image and implemented into autonomous dip picking to determine one or more dip angles at one or more depths. Further, determined dip angles may be implemented to identify well placement and steer a bottom hole assembly along a pre-designed drill path within a formation.

FIG. 1 illustrates a drilling system 100. As illustrated, borehole 102 may extend from a wellhead 104 into a subterranean formation 106 from a surface 108. Generally, borehole 102 may include horizontal, vertical, slanted, curved, and other types of borehole geometries and orientations. Borehole 102 may be cased or uncased. In examples, borehole 102 may include a metallic member. By way of example, the metallic member may be a casing, liner, tubing, or other elongated steel tubular disposed in borehole 102.

As illustrated, borehole 102 may extend through subterranean formation 106. As illustrated in FIG. 1, borehole 102 may extend generally vertically into the subterranean formation 106, however borehole 102 may extend at an angle through subterranean formation 106, such as horizontal and slanted boreholes. For example, although FIG. 1 illustrates a vertical or low inclination angle well, high inclination angle or horizontal placement of the well and equipment may be possible. It should further be noted that while FIG. 1 generally depicts land-based operations, those skilled in the art may recognize that the principles described herein are equally applicable to subsea operations that employ floating or sea-based platforms and rigs, without departing from the scope of the disclosure.

As illustrated, a drilling platform 110 may support a derrick 112 having a traveling block 114 for raising and lowering drill string 116. Drill string 116 may include, but is not limited to, drill pipe and coiled tubing, as generally known to those skilled in the art. A kelly 118 may support drill string 116 as it may be lowered through a rotary table 120. A drill bit 122 may be attached to the distal end of drill string 116 and may be driven either by a downhole motor and/or via rotation of drill string 116 from surface 108. Without limitation, drill bit 122 may comprise roller cone bits, PDC bits, natural diamond bits, any hole openers, reamers, coring bits, and the like. As drill bit 122 rotates, it may create and extend borehole 102 that penetrates various subterranean formations 106. A pump 124 may circulate drilling fluid through a feed pipe 126 through kelly 118, downhole through interior of drill string 116, through orifices in drill bit 122, back to surface 108 via annulus 128 surrounding drill string 116, and into a retention pit 132.

With continued reference to FIG. 1, drill string 116 may begin at wellhead 104 and may traverse borehole 102. Drill bit 122 may be attached to a distal end of drill string 116 and may be driven, for example, either by a downhole motor and/or via rotation of drill string 116 from surface 108. Drill bit 122 may be a part of bottom hole assembly (BHA) 130 at distal end of drill string 116. BHA 130 may further include tools for look-ahead resistivity applications. As will be appreciated by those of ordinary skill in the art, BHA 130 may be a measurement-while drilling (MWD) or logging-while-drilling (LWD) system.

BHA 130 may comprise any number of tools, transmitters, and/or receivers to perform downhole measurement operations. For example, as illustrated in FIG. 1, BHA 130 may include a measurement assembly 134. It should be noted that measurement assembly 134 may make up at least a part of BHA 130. Without limitation, any number of different measurement assemblies, communication assemblies, battery assemblies, and/or the like may form BHA 130 with measurement assembly 134. Additionally, measurement assembly 134 may form BHA 130 itself. In examples, measurement assembly 134 may comprise azimuthal borehole instrumentation for detecting bed boundaries and determining one or more dip angles at one or more depths. In examples, measurement assembly 134 may comprise modular resistivity tool with tilted antennas. Additionally, other azimuthal measurement tools may exist such as density imaging tools such as azimuthal litho-density tools. The azimuthal borehole instrumentation may measure the inclination angle, the horizontal angle, and the azimuthal angle (also known as the rotational or “tool face” angle) of the LWD tools. Inclination angle is the deviation from vertically downward, the horizontal angle is the angle in a horizontal plane from true North, and the tool face angle is the orientation (rotational about the tool axis) angle from the high side of the borehole. In some examples, azimuthal borehole instrumentation measurements may comprise three axis accelerometer measurement of the earth's gravitational field vector relative to the tool axis and a point on the circumference of the tool called the “tool face scribe line”. (The tool face scribe line is drawn on the tool surface as a line parallel to the tool axis.) From this measurement inclination and tool face angle of the LWD tool may be determined. Additionally, a three-axis magnetometer measures the earth's magnetic field vector in a similar manner. From the combined magnetometer and accelerometer data, the horizontal angle of the LWD tool can be determined. In addition, a gyroscope or other form of inertial sensor may be incorporated to perform position measurements and further refine the orientation measurements.

In other examples, downhole sensors on measurement assembly 134 may be coupled to information handling system 138. Drill bit 122 may penetrate formation 106. In examples, formation 106 may comprise a series of formation beds 154 dipping at an angle. A first (x, y, z) coordinate system associated with the sensors of measurement assembly 134 is shown, and a second coordinate system (x, y, z″) associated with the formation beds 154 may be shown. The bed coordinate system has the z″ axis perpendicular to the bedding plane, has the y″ axis in a horizontal plane, and has the x″ axis pointing “downhill”. The angle between the z-axes of the two coordinate systems is referred to as the “dip” and is shown in FIG. 1 as the angle β.

Without limitation, BHA 130 and all parts within BHA 130 (i.e., measurement assembly 134) may be connected to and/or controlled by information handling system 138, which may be disposed on surface 108. Without limitation, information handling system 138 may be disposed downhole in BHA 130. Processing of information recorded may occur downhole and/or on surface 108. Processing occurring downhole may be transmitted to surface 108 to be recorded, observed, and/or further analyzed. Additionally, information recorded on information handling system 138 that may be disposed downhole may be stored until BHA 130 may be brought to surface 108. In examples, information handling system 138 may communicate with BHA 130 through a communication line (not illustrated) disposed in (or on) drill string 116. In examples, wireless communication may be used to transmit information back and forth between information handling system 138 and BHA 130. Information handling system 138 may transmit information to BHA 130 and may receive as well as process information recorded by BHA 130. In examples, a downhole information handling system (not illustrated) may include, without limitation, a microprocessor or other suitable circuitry, for estimating, receiving and processing signals from BHA 130. Downhole information handling system (not illustrated) may further include additional components, such as memory, input/output devices, interfaces, and the like. In examples, while not illustrated, BHA 130 may include one or more additional components, such as analog-to-digital converter, filter and amplifier, among others, which may be used to process the measurements of BHA 130 before they may be transmitted to surface 108. Alternatively, raw measurements from BHA 130 may be transmitted to surface 108.

Any suitable technique may be used for transmitting signals from BHA 130 to surface 108, including, but not limited to, wired pipe telemetry, mud-pulse telemetry, acoustic telemetry, and electromagnetic telemetry. While not illustrated, BHA 130 may include a telemetry subassembly that may transmit telemetry data to surface 108. At surface 108, pressure transducers (not shown) may convert the pressure signal into electrical signals for a digitizer (not illustrated). The digitizer may supply a digital form of the telemetry signals to information handling system 138 via a communication link 140, which may be a wired or wireless link. The telemetry data may be analyzed and processed by information handling system 138.

As illustrated, communication link 140 (which may be wired or wireless, for example) may be provided that may transmit data from BHA 130 to an information handling system 138 at surface 108. Information handling system 138 may include a personal computer 141, a video display 142, a keyboard 144 (i.e., other input devices), and/or non-transitory computer-readable media 146 (e.g., optical disks, magnetic disks) that can store code representative of the methods described herein. In addition to, or in place of processing at surface 108, processing may occur downhole.

Methods and systems may be utilized by information handling system 138 to determine properties of subterranean formation 106. Information may be utilized to produce an image, which may be generated into a two or three-dimensional model of subterranean formation 106. These models may be used for well planning, (e.g., to design a desired path of borehole 102). Additionally, they may be used for planning the placement of drilling systems within a prescribed area. This may allow for the most efficient drilling operations to reach a subsurface structure. During drilling operations, measurements taken within borehole 102 may be used to adjust the geometry of borehole 102 in real time to reach a geological target. Measurements collected from BHA 130 of the formation properties may be used to steer drilling system 100 toward a subterranean formation 106. Additionally, information from measurement assembly 134 may be gathered and/or processed by information handling system 138. For example, signals recorded by receiver, discussed below, may be stored on memory and then processed by information handling system 138.

The processing may be performed real-time during data acquisition or after recovery of BHA 130. For this disclosure, real-time is a duration of time ranging from about a second to about ten minutes. Processing may alternatively occur downhole or may occur both downhole and at surface. Information handling system 138 may process the signals, and the information contained therein may be displayed for an operator to observe and store for future processing and reference. Information handling system 138 may also contain an apparatus for supplying control signals and power to BHA 130.

Systems and methods of the present disclosure may be implemented, at least in part, with information handling system 138. While shown at surface 108, information handling system 138 may also be located at another location, such as remote from borehole 102. Information handling system 138 may include any instrumentality or aggregate of instrumentalities operable to compute, estimate, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system 138 may be a personal computer 141, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. Information handling system 138 may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Additional components of the information handling system 138 may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard 144, a mouse, and a video display 142. Information handling system 138 may also include one or more buses operable to transmit communications between the various hardware components. Furthermore, video display 142 may provide an image to a user based on activities performed by personal computer 141. For example, producing images of geological structures created from recorded signals. By way of example, video display unit may produce a plot of depth versus the two cross-axial components of the gravitational field and versus the axial component in borehole coordinates. The same plot may be produced in coordinates fixed to the Earth, such as coordinates directed to the North, East and directly downhole (Vertical) from the point of entry to the borehole. A plot of overall (average) density versus depth in borehole or vertical coordinates may also be provided. A plot of density versus distance and direction from the borehole versus vertical depth may be provided. It should be understood that many other types of plots are possible when the actual position of the measurement point in North, East and Vertical coordinates is taken into account. Additionally, hard copies of the plots may be produced in paper logs for further use.

Alternatively, systems and methods of the present disclosure may be implemented, at least in part, with non-transitory computer-readable media 146. Non-transitory computer-readable media 146 may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time. Non-transitory computer-readable media 146 may include, for example, storage media such as a direct access storage device (e.g., a hard disk drive or floppy disk drive), a sequential access storage device (e.g., a tape disk drive), compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory; as well as communications media such wires, optical fibers, microwaves, radio waves, and other electromagnetic and/or optical carriers; and/or any combination of the foregoing.

FIG. 2 illustrates an example information handling system 138 which may be employed to perform various steps, methods, and techniques disclosed herein. Persons of ordinary skill in the art will readily appreciate that other system examples are possible. As illustrated, information handling system 138 includes a processing unit (CPU or processor) 202 and a system bus 204 that couples various system components including system memory 206 such as read only memory (ROM) 208 and random-access memory (RAM) 210 to processor 202. Processors disclosed herein may all be forms of this processor 202. Information handling system 138 may include a cache 212 of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 202. Information handling system 138 copies data from memory 206 and/or storage device 214 to cache 212 for quick access by processor 202. In this way, cache 212 provides a performance boost that avoids processor 202 delays while waiting for data. These and other modules may control or be configured to control processor 202 to perform various operations or actions. Another system memory 206 may be available for use as well. Memory 206 may include multiple different types of memory with different performance characteristics. It may be appreciated that the disclosure may operate on information handling system 138 with more than one processor 202 or on a group or cluster of computing devices networked together to provide greater processing capability. Processor 202 may include any general-purpose processor and a hardware module or software module, such as first module 216, second module 218, and third module 220 stored in storage device 214, configured to control processor 202 as well as a special-purpose processor where software instructions are incorporated into processor 202. Processor 202 may be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. Processor 202 may include multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processor 202 may include multiple distributed processors located in multiple separate computing devices but working together such as via a communications network. Multiple processors or processor cores may share resources such as memory 206 or cache 212 or may operate using independent resources. Processor 202 may include one or more state machines, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA (FPGA).

Each individual component discussed above may be coupled to system bus 204, which may connect each and every individual component to each other. System bus 204 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROM 208 or the like, may provide the basic routine that helps to transfer information between elements within information handling system 138, such as during start-up. Information handling system 138 further includes storage devices 214 or computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. Storage device 214 may include software modules 216, 218, and 220 for controlling processor 202. Information handling system 138 may include other hardware or software modules. Storage device 214 is connected to the system bus 204 by a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for information handling system 138. In one aspect, a hardware module that performs a particular function includes the software component stored in a tangible computer-readable storage device in connection with the necessary hardware components, such as processor 202, system bus 204, and so forth, to carry out a particular function. In another aspect, the system may use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations may be modified depending on the type of device, such as whether information handling system 138 is a small, handheld computing device, a desktop computer, or a computer server. When processor 202 executes instructions to perform “operations”, processor 202 may perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

As illustrated, information handling system 138 employs storage device 214, which may be a hard disk or other types of computer-readable storage devices which may store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs) 210, read only memory (ROM) 208, a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with information handling system 138, an input device 222 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Additionally, input device 222 may take in data from measurement assembly 134 (e.g., referring to FIG. 1), discussed above. An output device 224 may also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with information handling system 138. Communications interface 226 generally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

As illustrated, each individual component described above is depicted and disclosed as individual functional blocks. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor 202, that is purpose-built to operate as an equivalent to software executing on a general-purpose processor. For example, the functions of one or more processors presented in FIG. 2 may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may include microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) 208 for storing software performing the operations described below, and random-access memory (RAM) 210 for storing results. Very large-scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general-purpose DSP circuit, may also be provided.

The logical operations of the various methods, described below, are implemented as: (1) a sequence of computer implemented steps, operations, or procedures running on a programmable circuit within a general use computer, (2) a sequence of computer implemented steps, operations, or procedures running on a specific-use programmable circuit; and/or (3) interconnected machine modules or program engines within the programmable circuits. Information handling system 138 may practice all or part of the recited methods, may be a part of the recited systems, and/or may operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations may be implemented as modules configured to control processor 202 to perform particular functions according to the programming of software modules 216, 218, and 220.

In examples, one or more parts of the example information handling system 138, up to and including the entire information handling system 138, may be virtualized. For example, a virtual processor may be a software object that executes according to a particular instruction set, even when a physical processor of the same type as the virtual processor is unavailable. A virtualization layer or a virtual “host” may enable virtualized components of one or more different computing devices or device types by translating virtualized operations to actual operations. Ultimately however, virtualized hardware of every type is implemented or executed by some underlying physical hardware. Thus, a virtualization computer layer may operate on top of a physical computer layer. The virtualization computer layer may include one or more virtual machines, an overlay network, a hypervisor, virtual switching, and any other virtualization application.

FIG. 3 illustrates an example information handling system 138 having a chipset architecture that may be used in executing the described method and generating and displaying a graphical user interface (GUI). Information handling system 138 is an example of computer hardware, software, and firmware that may be used to implement the disclosed technology. Information handling system 138 may include a processor 202, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 202 may communicate with a chipset 300 that may control input to and output from processor 202. In this example, chipset 300 outputs information to output device 224, such as a display, and may read and write information to storage device 214, which may include, for example, magnetic media, and solid-state media. Chipset 300 may also read data from and write data to RAM 210 (e.g., referring to FIG. 2). Bridge 302 for interfacing with a variety of user interface components 304 may be provided for interfacing with chipset 300. User interface components 304 may include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to information handling system 138 may come from any of a variety of sources, machine generated and/or human generated.

Chipset 300 may also interface with one or more communication interfaces 226 that may have different physical interfaces. Such communication interfaces may include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein may include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 202 analyzing data stored in storage device 214 or RAM 210 (e.g., referring to FIG. 2). Further, information handling system 138 receives inputs from a user via user interface components 304 and executes appropriate functions, such as browsing functions by interpreting these inputs using processor 202.

In examples, information handling system 138 may also include tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices may be any available device that may be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which may be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network, or another communications connection (either hardwired, wireless, or combination thereof), to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices.

Computer-executable instructions include, for example, instructions and data which cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

In additional examples, methods may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Examples may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

FIG. 4 illustrates an example of one arrangement of resources in a computing network 400 that may employ the processes and techniques described herein, although many others are of course possible. As noted above, an information handling system 138, as part of their function, may utilize data, which includes files, directories, metadata (e.g., access control list (ACLS) creation/edit dates associated with the data, etc.), and other data objects. The data on the information handling system 138 is typically a primary copy (e.g., a production copy). During a copy, backup, archive or other storage operation, information handling system 138 may send a copy of some data objects (or some components thereof) to a secondary storage computing device 404 by utilizing one or more data agents 402.

A data agent 402 may be a desktop application, website application, or any software-based application that is run on information handling system 138. As illustrated, information handling system 138 may be disposed at any rig site (e.g., referring to FIG. 1) or repair and manufacturing center. Data agent 402 may communicate with a secondary storage computing device 404 using communication protocol 408 in a wired or wireless system. Communication protocol 408 may function and operate as an input to a website application. In the website application, field data related to pre- and post-operations, generated DTCs, notes, and the like may be uploaded. Additionally, information handling system 138 may utilize communication protocol 408 to access processed measurements, operations with similar DTCs, troubleshooting findings, historical run data, and/or the like. This information is accessed from secondary storage computing device 404 by data agent 402, which is loaded on information handling system 138.

Secondary storage computing device 404 may operate and function to create secondary copies of primary data objects (or some components thereof) in various cloud storage sites 406A-N. Additionally, secondary storage computing device 404 may run determinative algorithms on data uploaded from one or more information handling systems 138, discussed further below. Communications between the secondary storage computing devices 404 and cloud storage sites 406A-N may utilize REST protocols (Representational state transfer interfaces) that satisfy basic C/R/U/D semantics (Create/Read/Update/Delete semantics), or other hypertext transfer protocol (“HTTP”)-based or file-transfer protocol (“FTP”)-based protocols (e.g., Simple Object Access Protocol).

In conjunction with creating secondary copies in cloud storage sites 406A-N, the secondary storage computing device 404 may also perform local content indexing and/or local object-level, sub-object-level or block-level deduplication when performing storage operations involving various cloud storage sites 406A-N. Cloud storage sites 406A-N may further record and maintain DTC code logs for each downhole operation or run, map DTC codes, store repair and maintenance data, store operational data, and/or provide outputs from determinative algorithms that are run at cloud storage sites 406A-N. In examples, computing network 400 may be communicatively coupled to measurement assembly 134 through communication link 140 (e.g., referring to FIG. 1). In examples, measurement assembly 134 (e.g., referring to FIG. 1) may measure azimuthal resistivity. The measurements taken by measurement assembly 134 may be sent to information handling system 138 for further processing.

FIG. 5 illustrates measurement assembly 134. Measurement assembly 134 may comprise one or more reduced regions 502 of reduced diameter for supporting transmitters and receivers. In examples, transmitters and receivers may be formed from wire coil. Wire coil may be placed in a reduced region 502 and spaced away from the tool surface by a constant distance. To mechanically support and protect the wire coil, a non-conductive filler material (not shown) such as epoxy, rubber, fiberglass, or ceramics may be used to fill in the reduced diameter regions.

Herein measurement assembly 134 is illustrated to comprise six coaxial transmitters 506 T5, 508 T3, 510 T1, 516 T2, 518 T4, and 520 T6. The axes of transmitters T1-T6 may coincide with the longitudinal axis of measurement assembly 134. In addition, measurement assembly 134 may comprise three tilted receiver antennas 504 R3, 512 R1, and 514 R2. Herein, “tilted” may be defined by the plane of 504 R3, 512 R1, and/or 514 R2 is not perpendicular to the longitudinal axis of measurement assembly 134.

In examples, transmitters 506 T5, 508 T3, 510 T1, 516 T2, 518 T4, and 520 T6 may be tilted and receiver antennas 504 R3, 512 R1, and 514 R2 may be coaxial. In further examples, both transmitters 506 T5, 508 T3, 510 T1, 516 T2, 518 T4, and 520 and receiver antennas 504 R3, 512 R1, and 514 R2 are tilted. Moreover, the roles of transmitters 506-520 and receivers 504-512 may be interchanged while preserving the usefulness of the measurements made by the tool. In operation, each of the transmitters 506-520 are energized in turn, and the phase and amplitude of the resulting voltage induced in each of receivers 504-512 are measured. From these measurements, or a combination of these measurements, azimuth borehole images may be formed. Azimuth borehole images may comprise borehole azimuthal measurements of formation 106 (e.g., referring to FIG. 1).

FIGS. 6A-6C illustrate borehole imaging processing for an azimuthal borehole image 600. An azimuthal borehole image 600 may be formed using the methods and systems described above. As noted above, a bed boundary 640 may be defined as the interface between two formation beds 154 (e.g., referring to FIG. 1). FIG. 6A illustrates azimuthal borehole image 600 formed using methods and systems described above. Within the formed azimuthal borehole image 600 may be measured properties of a bed boundary 640, an intersecting line 641, a borehole diameter d, and a dip spin A. Herein, intersecting line 641 may be an interface of bed boundary 640 and dip spin A may be the amplitude of the sinusoid selected to represent intersecting line 641. The azimuthal direction of borehole 102 may be marked T for top of borehole 102, B for bottom of borehole 102, L for left of borehole of 102, R for right of borehole 102, and bed boundary 640 intercepting at an angle θ with a dip spin A. Further, azimuth borehole image 600 may be sliced along a vertical axis 680 to yield an unrolled azimuth borehole image 602. FIG. 6B illustrates the unrolling process for unrolled azimuth borehole image 602 across T for the top of borehole 102. FIG. 6C illustrates sliced azimuth borehole image 602 fully unrolled. Sliced azimuth borehole image 602 may be mapped with bed boundary 640 to form a sinusoid selected to represent intersecting line 641 in the azimuthal borehole image 602. The sinusoid selected to represent intersecting line 641 may be utilized by information handling system 138 to identify and pick dips within formation beds 154.

FIG. 7 illustrates workflow 700 for dip picking from an azimuthal borehole image 600. Workflow 700 may be performed on information handling system 138 at a selected depth within borehole 102 (e.g., referring the FIG. 1). In block 702 a sinusoid from azimuth borehole image 602 (e.g., referring to FIG. 6C) may be obtained by measurement assembly 134, (e.g., referring to FIG. 5) as previously described in FIGS. 6A-6C. In block 704 the sinusoid in azimuth borehole image 602 obtained in block 702 may be processed by an image preprocessing action to form a processed azimuth borehole image. For this disclosure, an action is defined as the utilization of computational resources by information handling system 138 to perform a mathematical equation, algorithm, program execution, and/or the like. In examples, the image preprocessing action may comprise data quality control, resampling, image filtering, image equalization, and/or gradient calculation. In block 706 the processed azimuth image from block 704 may be searched by a sinusoidal pattern search action to detect one or more raw dips in the processed azimuth image. In examples, the sinusoidal pattern search action may loop through the processed azimuth image and employ one or more sinusoidal parameter combinations to detect one or more raw dips. Herein, sinusoidal parameters may be depth, amplitude, and/or phase. The sinusoidal pattern search action may employ a variety of Hough transforms to detect one or more raw dips using a selected parameter. In examples, a traditional Hough transform, or a non-uniform Hough transforms with a median voting strategy may be implemented to search and identify dip sinusoids.

In block 708 the one or more raw dips detected in block 706 may be processed by a dip post-processing action. For one or more raw dips, the dip post-processing action may evaluate various dip features and output a final dip result to block 710. This action mimics the process of human evaluation to exclude false dips and improve the reliability of dip results. In examples, dip features may be symmetry, continuity, separation, and/or the like. In summary, workflow 700 receives an azimuth borehole image, employs an image preprocessing action, a sinusoidal pattern search action, and a dip post-processing action to yield a final dip result at one depth within borehole 102. Further, as BHA 130 drills into formation beds 154 workflow 700 may be repeated a plurality of times at a plurality of depths to determine a final dip result at each of the plurality of depths. In examples, additional computational resources may be utilized to allow for additional computational operations in each action further process azimuth borehole image 602.

FIG. 8 illustrates workflow 800 for dip picking from an azimuth borehole image 602 (e.g., referring to FIG. 6). Workflow 800 may be performed on information handling system 138 for any depth within borehole 102 (e.g., referring the FIG. 1). In block 802 an azimuth borehole image may be obtained by measurement assembly 134, (e.g., referring to FIG. 5) as previously described. In examples, azimuth borehole image may be input into image pre-processing action 840. In image pre-processing action 840 one or more computational operations of blocks 804-808 may be performed. In block 804 data quality control may be performed as a quality check on the azimuth borehole image from block 802. For example, invalid data may comprise rows with empty or false values. Such invalid data may be removed from azimuth borehole image 602 to form a clean azimuth borehole image. FIG. 10A is an example of borehole image 602 and an example of the product of block 802 (e.g., referring to FIG. 8). Referring back to FIG. 8, in block 806, quality-based resampling may be performed on clean azimuth borehole image form block 804 with data quality control to form a resampled azimuth borehole image. In examples, quality-based resampling may resample azimuth borehole image with data quality control with improved or optimal depth resolution based on the quality of the clean azimuth borehole image. To determine an improved or optimal depth resolution, a median value of depth difference may be used. Herein, median value of depth difference may be an iterative process where each value of azimuth borehole image 602 is processed through a standard median filter. Additionally, a new uniform depth axis may be generated and the clean azimuth borehole image with data quality control is resampled with the new uniform depth axis. Resampling the clean azimuth borehole image with data quality control on the new uniform depth axis the with median value of depth difference produces an improved azimuth borehole image. In block 808, the improved azimuth borehole image from block 806 may be converted into a smart gradient image. As such, the improved azimuth borehole image from block 806 may be converted to a gradient image by taking the difference of data values between two adjacent data points along the depth direction or using a gradient filter (e.g., Sobel filter). Then the gradient image may be smoothed by gaussian filters with two or more window sizes. Finally, the two or more filtered images are fused into a smart gradient image by averaging value, maximizing absolute value, and/or the like. In examples, the smart gradient image from block 808 may be input into sinusoidal pattern search action 860. FIG. 10B is an example of a gradient image and an example of the product of block 808.

Referring back to FIG. 8, in pattern search action 860 any number of blocks 810-814 may be performed. In block 810 a non-uniform Hough transform may search for dips in the smart gradient image. In examples, a non-uniform Hough transform may be performed in a three-dimensional domain to search for sinusoids with different depths, amplitudes, and phases. Dips in formation 106 (e.g., referring to FIG. 1) may span distances from less than a foot to as large as tens of feet. If the search parameter space is uniform, detecting small and large dips at the same time may be time consuming. Therefore, the search amplitude may be non-uniform. To form a non-uniform search amplitude a non-uniform depth axis, a non-uniform amplitude axis, and a uniform phase axis may be constructed for the search of sinusoids. FIG. 10C is an example of a summation voting image and an example of the product of block 810. The amplitude axis is non-uniform based on the dip feature of a tangent function. Additionally, calculating the sinusoid for each parameter combination may be performed. Subsequently, the depths, amplitudes, and phases of each sinusoid may be extracted by the non-uniform Hough transform. FIG. 9A illustrates a graph of an amplitude phase graph with uniform amplitude axis 900 implemented in a uniform Hough transform. FIG. 9B illustrates a graph of an amplitude phase graph with non-uniform amplitude axis 902 implemented in a non-uniform Hough transform of block 910. FIG. 9C illustrates amplitude axis is non-uniform based on the dip feature of a tangent function 904.

Referring back to FIG. 8, in block 812 the possibility for each sinusoid extracted in block 810 may be determined by median voting. Hough transform may implement summation for voting. Specifically, voting may be equivalent to summing all values comprising edges or gradients along each sinusoid. In other examples, Hough transforms may implement median based voting. Median based voting aggregates the median value of values comprising gradients along a sinusoid as the voting, rather than the sum. In either example, a 3D Hough volume with local maxima suggesting the potential location of matched sinusoids may be produced as a product of Hough transform implementing summation or median based voting. As such in block 812 Median based voting may be applied to each sinusoid extracted in block 810 to produce a voting score for each depth of the 3D Hough volume. FIG. 10D is an example of the median voting image and an example of the product of block 812. Referring back to FIG. 8, in block 814 the maximum voting for each depth of the 3D Hough volume may be selected and its corresponding amplitude and phases recorded and exported as raw dips. In examples, the raw dips from block 814 may be input into a dip post-processing action 880.

In dip post-processing action 880 one or more computational operations of blocks 816-820 may be performed. In block 816 a variety of dip features from the raw dips of block 814 may be identified and categorized. Herein, a dip feature may utilized to differentiate a picked sinusoid from a “good” dip and a “bad” dip. For example, symmetry may be used as a dip feature to identify and categorize a raw dip. FIG. 11 illustrates the evaluation of dip symmetry for a “good” and a “bad” dip. An extraction may be applied to the background image data for “good” dip and “bad” to form background for a “good” dip 1102 and background for a “bad” dip 1104, respectively. As shown in FIG. 11, interpolation step 1120 may be applied to background for a “good” dip 1102 and background for a “bad” dip 1104. Interpolation step 1120 may comprise linear interpolation along the azimuth, Interpolation 1120 may guarantee accurate shifting for shifting step 1140. In shifting step 1140 are background for a “good” dip 1102 and background for a “bad” dip 1104 shifted based on their respective azimuth. After shifting step 1140 the symmetry feature may be quantified by the correlation between the left image and the flipped right image.

Symmetry for a dip may be determined by extracting the background image for the picked sinusoid, interpolating the background image to guarantee accurate shifting, shifting the background image based on the detected azimuth, and quantifying symmetry by correlating between the left-half image and flipped right-half image. Herein the background image may be the whole image from block 816, without the raw dips or dip features. The symmetry may range from 0 to 1, 0 being no symmetry and 1 being fully symmetrical. Additionally further dip features such as separation, dip continuity, and/or the like may be also calculated to further identify and categorize the raw dip. The output of block 816 may be dip features selected to be applied to each raw dip. Herein selecting may be performed by an operator or by a predefined selecting algorithm configured to optimize selecting dip features based on each raw dip. In block 818 a score for each raw dip may be computed based at least in part on selected dip features applied to the raw dip. The score of every raw dip may be computed by normalizing each dip feature to unity with upper and lower limits, then the lowest score in all dip features for every raw dip may be selected as the quality score for every raw dip and saved to a dip table. Herein, normalizing may be defined as converting a value into something between 0 and 1. The upper limit updates to 1 and lower limit updates to 0. In block 820 low quality dips are rejected. This may be performed by setting a base threshold and rejecting all quality scores lower than the base quality threshold. The base threshold is adjustable and may range from removing 0.01%-0.1%, 0.1%-1%, 1%-10%, or 10%-99% of all raw dips. The product of block 820 is a number of final dips. Further, as BHA 130 drills into formation beds 154 (e.g., referring to FIG. 1) workflow 800 may be repeated a plurality of times at a plurality of depths to determine a final dip result at each of the plurality of depths. An example of a theoretical image generated in block 820, as well as the products of other blocks.

Further, one or more final dips may improve steering of bottom hole assembly 130 (e.g., referring to FIG. 1) for LWD operations. For example, one or more final dips at one or depths may be used to construct geological model, showing structure of the geological formation, which may be used as a reference for making geosteering and drilling decisions. Additionally, a drip trend may be computed from all dips in the drilling region, by any summation procedure. The knowledge of dip trend of a region is also highly beneficial for geological model to determine better predictions for geosteering and drilling decisions.

The methods and systems described above are an improvement over current technology in that the method and systems herein may present workflows to perform dip picking from azimuthal borehole images. This framework splits the sophisticated automatic dip picking task into three key actions. Each action takes care of a single task, and the data interface between actions are pre-defined. This framework is powerful enough to handle the dip picking task, and also flexible enough to allow future extension of the algorithm for some complicated formations. This framework provides a high-level automation solution and yields a better performance in the dip picking task.

The preceding description provides various examples of the systems and methods of use disclosed herein which may contain different method steps and alternative combinations of components. It should be understood that, although individual examples may be discussed herein, the present disclosure covers all combinations of the disclosed examples, including, without limitation, the different component combinations, method step combinations, and properties of the system. It should be understood that the compositions and methods are described in terms of “comprising,” “containing,” or “including” various components or steps, the compositions and methods may also “consist essentially of” or “consist of” the various components and steps. Moreover, the indefinite articles “a” or “an,” as used in the claims, are defined herein to mean one or more than one of the elements that it introduces. The systems and methods may comprise any of the various features disclosed herein, comprising one or more of the following statements.

Statement 1. A method comprising: disposing an azimuthal borehole measurement tool into a borehole; obtaining an azimuthal borehole image with the azimuthal borehole measurement tool; running a pre-processing action on the azimuthal borehole image to obtain a smart gradient image; running a sinusoidal pattern search action on the smart gradient image to form a raw dip of the azimuthal borehole image; and running a dip post-processing action to form a final dip.

Statement 2. The method of statement 1, wherein the pre-processing action comprises data quality control performs a quality check on the azimuth borehole image.

Statement 3. The method of statement 2, wherein the pre-processing action comprises quality-based resampling is performed on the azimuthal borehole image with data quality control to form a resampled azimuth borehole image.

Statement 4. The method of statement 3, further comprising taking a difference of data values between two adjacent data points along a depth direction or using a gradient filter.

Statement 5. The method of statements 1-4, wherein the sinusoidal pattern search action comprises a non-uniform Hough transform to search for dips in the smart gradient image.

Statement 6. The method of statement 5, wherein the non-uniform Hough transform is performed in a three-dimensional domain to search for sinusoids with different depths, amplitudes, and phases.

Statement 7. The method of statement 6, wherein the Hough transforms implements median based voting, wherein median based voting aggregates the median value of values comprising gradients along a sinusoid and forms a 3D Hough volume.

Statement 8. The method of statement 7, further comprising recording amplitude and phase of a maximum voting for each depth of the 3D Hough volume.

Statement 9. The method of statements 1-8, wherein the dip post-processing action comprises determining one or more dip features, wherein one or more dip features comprises symmetry, separation, or dip continuity.

Statement 10. The method of statement 9, further comprising computing a score for the raw dip based at least in part on the one or more dip features.

Statement 11. A system comprising: an azimuthal borehole measurement tool disposed in a borehole configured to obtain an azimuthal borehole image; and an information handling system configured to: run a pre-processing action on the azimuthal borehole image to obtain a smart gradient image; run a sinusoidal pattern search action on the smart gradient image to form a raw dip of the azimuthal borehole image; and run a dip post-processing action to form a final dip.

Statement 12. The system of statement 11, wherein the pre-processing action comprises data quality control performs a quality check on the azimuth borehole image.

Statement 13. The system of statement 12, wherein the pre-processing action comprises quality-based resampling is performed on the azimuthal borehole image with data quality control to form a resampled azimuth borehole image.

Statement 14. The system of statement 13, wherein the information handling system is further configured to take a difference of data values between two adjacent data points along a depth direction or using a gradient filter.

Statement 15. The system of statements 11-14, wherein the sinusoidal pattern search action comprises a non-uniform Hough transform to search for dips in the smart gradient image.

Statement 16. The system of statement 15, wherein the non-uniform Hough transform is performed in a three-dimensional domain to search for sinusoids with different depths, amplitudes, and phases.

Statement 17. The system of statement 16, wherein the Hough transforms implements median based voting, wherein median based voting aggregates the median value of values along a sinusoid and forms a 3D Hough volume.

Statement 18. The system of statement 17, wherein the information handling system is further configured to record amplitude and phase of a maximum voting for each depth of the 3D Hough volume.

Statement 19. The system of statements 11-18, wherein the dip post-processing action comprises determining one or more dip features, wherein one or more dip features comprises symmetry, separation, or dip continuity.

Statement 20. The system of statement 19, wherein the information handling system is further configured to compute a score for the raw dip based at least in part on the one or more dip features.

For the sake of brevity, only certain ranges are explicitly disclosed herein. However, ranges from any lower limit may be combined with any upper limit to recite a range not explicitly recited, as well as ranges from any lower limit may be combined with any other lower limit to recite a range not explicitly recited, in the same way, ranges from any upper limit may be combined with any other upper limit to recite a range not explicitly recited. Additionally, whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range are specifically disclosed. In particular, every range of values (of the form, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values even if not explicitly recited. Thus, every point or individual value may serve as its own lower or upper limit combined with any other point or individual value or any other lower or upper limit, to recite a range not explicitly recited.

Therefore, the present examples are well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular examples disclosed above are illustrative only and may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Although individual examples are discussed, the disclosure covers all combinations of all of the examples. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. It is therefore evident that the particular illustrative examples disclosed above may be altered or modified and all such variations are considered within the scope and spirit of those examples. If there is any conflict in the usages of a word or term in this specification and one or more patent(s) or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted.

Automatic Dip Picking From Azimuthal Borehole Images

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