HK1224381B - System and method for automatically correlating geologic tops - Google Patents

Description

System and method for automatically correlating geological topography

Priority claim/related applications

This application claims the benefit under 35 USC 119(e) of U.S. Provisional Patent Application Serial No. 61/813,124, filed April 17, 2013, and entitled “System and Method for Automatically Correlating Geologic Tops,” the entire contents of which are incorporated herein by reference.

appendix

Appendix A (8 pages) contains more details of the time warping method used in this method. Appendix A forms part of this specification and is incorporated herein by reference.

Technical Field

Aspects of the present disclosure relate to a system and process for interpreting geological formations using data acquired from a well bore. More particularly, aspects of the present disclosure relate to a computing system configured to help analysts quickly and accurately identify and model subsurface geological formations in three dimensions.

Background Art

In geology and related fields, stratigraphy involves the study of the layers of rock and soil that make up the Earth's subsurface terrain. In the field of oil and gas exploration, the identification of regional stratigraphic layers is particularly important because they can identify the likely locations of oil and gas deposits. Furthermore, the identification of faults is crucial not only for identifying potential locations for resources but also for safe drilling. To identify the various stratigraphic layers in the Earth's subsurface terrain, geologists examine data in the form of well logs.

Well logs are records of the geological formations penetrated by a wellbore. These logs can then be analyzed by a geologist to identify the stratigraphic contacts or well tops penetrated by the wellbore. Typically, well logs from an area, such as an oil field or a portion of an oil field, are displayed as two-dimensional or three-dimensional maps. The geologist begins taking a well log in one wellbore, identifies the well top, and identifies corresponding well tops in the same logs in other wellbores. As oil fields increase in size, analyzing the three-dimensional acquisition of well logs using such conventional techniques becomes increasingly difficult and time-consuming. Furthermore, as the number of well logs and wellbores increases, the likelihood of achieving consistently accurate results decreases, and different geologists may interpret the same data in significantly different ways.

Aspects of the present disclosure were developed with these and other issues in mind.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 depicts an example of a wellbore penetrating a formation in an oil field;

FIG2 depicts an exemplary three-dimensional plot of well logs along with their respective wellbores according to location;

3A-3C each depict an exemplary three-dimensional plot of well logs positioned according to location with a well top identified across multiple well logs;

FIG4 depicts an exemplary method for performing automated top correlation;

FIG5 depicts an exemplary three-dimensional plot of well logs positioned according to location with two well tops identified across multiple well logs;

FIG6 depicts an exemplary graph for determining natural neighbors for each well;

FIG7 is a block diagram illustrating an example of a general purpose computing system that may be used to implement a system for automatically correlating geological tops; and

8-12 illustrate examples of pseudocode that implement automated top-level dependencies.

DETAILED DESCRIPTION

The present disclosure is particularly applicable to systems and methods for automatically correlating geological tops using at least one process in a general-purpose computing system, such as described below, and will be described in this context. However, it will be appreciated that the systems and methods have greater utility because they can be implemented using other computer systems and models, such as client-server computer systems, mainframe computer systems with terminals, stand-alone computer systems, cloud-based computer systems, or software-as-a-service (SaaS) models. For example, in a SaaS model system implementation, the computing system would be one or more computing resources, such as one or more cloud computing resources or one or more server computers, in a back-end component having at least one processor that executes multiple lines of computer code, causing the at least one processor to implement the method described below. Users using various computing devices, such as desktop computers, laptop computers, and tablet computers, can connect to the back-end component via a communication path, such as a wired or wireless computer network or a cellular network, to upload seismic data to the back-end component, which performs automatic correlation of geological tops and returns the results to the user in the form of user interface data that can be displayed by the user on the computing device.

According to one aspect, a system and method for automatically correlating address tops using at least one processor is provided. The system receives well logs from different wellbores and one or more user seed selections that identify the well tops to be correlated. Each of the seed selections is added to a priority queue sorted by the confidence level of each selection. The user-selected selection is assigned the highest confidence level. The system performs the correlation by: selecting a window of well log data associated with the user manual selection (selected at the top of the priority queue); and then finding the best match with the corresponding window in the adjacent wellbore. The new selection in the target well is then estimated using a correlation function. A quality value and a confidence value can then be calculated for each selection using a correlation function, such as dynamic time warping, and added to the priority queue based on the confidence value. The system can be configured so that selections that fall below a preset quality or confidence value are discarded and not added to the queue. The system can then proceed to the next selection in the priority queue.

Embodiments of the present disclosure relate to systems and methods for automatically correlating geological tops. In particular, the present disclosure provides systems and methods for receiving a series of well logs and being able to automatically correlate well tops identified by a user across many wellbores using the provided well logs. The well tops identified by the user are designated as "seed picks," which identify the well tops to be correlated. The system then uses the seed picks to find the corresponding location of the well top (the "pick") in each of the provided well logs by performing dynamic time warping on the well logs and following the path through the provided wellbores (that yields the highest confidence in the pick).

Referring to FIG1 , an exemplary oilfield 100 is depicted. In this example, three layers 110, 120, 130 of the earth's formation are illustrated, but it should be understood that the formations can vary in thickness from a few feet to tens of feet. Thus, a wellbore a thousand feet deep may penetrate hundreds of formations, which may or may not have a consistent thickness and may not be at a consistent depth throughout the oilfield 100. The depicted oilfield 100 also includes numerous boreholes 140-150 that penetrate the surface and through the formations.

Referring to Figure 2, an example of a well log obtained at nine different wellbores is depicted. In this example, the well log is created by measuring various properties of the subsurface formation. The diagram is three-dimensional, so that the well logs are spaced relative to their actual physical location, and the top of the diagram is the measurement at ground level with the depth decreasing as the page goes down. The varying widths in the well log signature represent formations that vary with depth. For example, depending on the composition of the formation, the emitted gamma radiation may increase or decrease, resulting in peaks or valleys in the well log. The resulting measurements are then visually depicted as wider or narrower bars in the well log. Therefore, if consecutive areas of a well log emit similar amounts of gamma radiation, the well log displays a consistent width. A geologist viewing the well log can then determine that the area consists of a certain type of formation in a consistent layer and is a single well top.

Referring to FIG3A , a well log is depicted using a seed selection 300 selected by a user. The system operates by finding a location in other well logs that corresponds to the seed selection 300. The user identifies the top of the well and selects the location of that well log as the seed selection. The seed selection can be graphically illustrated by overlaying an indicator at the location of the top of the well on the well log. This can be done by starting at the well log that is physically closest to the wellbore with the seed selection. Each of the well logs of the adjacent wells is evaluated and, for each well log, the selection corresponding to the seed selection is correlated. Each selection that is correlated is recorded and also assigned a quality value based on its degree of match with the seed selection and a monotonically non-increasing confidence value, which is a combination of the confidence of the seed selection and the quality of the new selection. The process is then repeated using the selection with the highest quality value and correlating that selection with its adjacent well logs. The process is repeated using the highest confidence selection until a selection has been achieved at each wellbore or until no remaining selections can be made (e.g., if the correlation fails). For example, referring to Figure 3B, a seed pick 300 is used to pick the same well top at a first correlation pick 310 and a second correlation pick 320. Referring now to Figure 3C, the first pick 310 and the second pick 320 can then be used to correlate a third pick 330 and a fourth pick 340. Thus, the system starts at the seed pick, and the correlation is then propagated through the well logs.

Referring to FIG4 , a method for automatically correlating geological tops is depicted. According to one aspect, automated top correlation is initiated by a user selecting one or more well logs from a set of wellbores for analysis (operation 400). FIG8-12 together are exemplary pseudocode that may implement the automated top correlation method shown in FIG4 . The method shown in FIG4 may be implemented as code executed by a processor of a computer system, wherein the code causes the processor to perform various processes of the method described below.

There must be at least two wellbores, each with a well log. Generally, the data will consist of a much larger set of wellbores. For example, a set of wellbores may include all wellbores across an entire oil field (possibly hundreds of wellbores) or a subset thereof. The user may select at least one seed pick that is part of the well top from the provided well logs (operation 410). The at least one seed pick may each be assigned a maximum confidence value and added to a priority queue prioritized by confidence value. For example, the confidence range may be from 0 to 1 or 0 to 100%, with a confidence of 1 or 100% being the highest. In this case, the seed pick will be assigned a confidence value of 1 or 100%.

The confidence priority queue can include elements containing identifiers for each wellbore in the queue, a confidence value for each selection, and any other information relevant to the execution. The queue can be configured as a priority queue based on the confidence values of the elements. The confidence priority queue can initially contain any seed selections, but as the correlation is executed, new elements are added to the confidence priority queue for each selection made by the system based on its confidence value.

The user may also have the option of providing boundaries and thresholds for the system (operation 420). For example, the system may receive a minimum confidence threshold for automatically selecting a selection. Similarly, the system may receive a minimum quality threshold for automatically selecting a selection. The user may also optionally provide the system with boundaries for limiting the correlation analysis to certain stratigraphic information, such as, for example, a stratigraphic interval. For example, FIG5 depicts nine well logs from nine wellbores, where first well tops 500-508 and second well tops 510-518 have been identified. In this case, the user may choose to limit the analysis based on previously correlated well tops. For example, the user may choose to correlate well tops located at a higher depth with first well tops 500-508. In this case, correlation does not need to be performed across the entire well log, as the correlated well tops will be located above first well tops 500-508. Similarly, the correlation may be limited to below second well tops 510-518 or between well tops. The correlation may also be performed based on a structural model in which the well logs are aligned in the Wheeler transform domain. The user can also use previously correlated well tops to create boundaries for correlation.

Referring back to FIG4 , once a set of well logs has been selected for analysis and seed selection has been selected, the system can determine which well logs are from wellbores that are adjacent to each other in some sense (operation 430). This can be accomplished by constructing a graph based on the location of each wellbore. For example, the system can use the location of each wellbore to create a weighted graph in which the distances between nodes can be assigned based on the physical location of each well. FIG6 provides an illustrative example of how the locations of nodes 610-619 are positioned based on their physical locations relative to each other. Graph 600 can initially be a fully weighted graph in which each pair of vertices is connected by a different weighted edge and edge weights are assigned based on the distances between the vertices. Then, using the locations of the vertices, the system can use any natural neighbor selection method to determine which nodes are natural neighbors. For example, the system can perform a Delaunay triangulation to determine the natural neighbors of each node by performing edge connections to form triangles, wherein the triangle has a circumscribed circle (a circle connecting the three vertices that form the triangle) that does not contain any nodes. For example, FIG6 illustrates only the edges 620-652 that connect each pair of vertices that are natural neighbors according to the Delaunay triangulation. Other graph connection strategies can be envisioned and easily incorporated into the well log correlation algorithm. For example, the system can use any method to create the graph, and the neighbors can be those vertices in the graph that are connected to the original vertex (corresponding to the original well log) by arcs in the graph.

Referring back to Figure 4, once the confidence priority queue has been initialized with a seed pick and neighbors have been established, the system can begin execution by selecting the pick at the front of the queue (operation 440). Correlations such as dynamic time warping can then be performed on the selected neighbors (operation 450). Other possible correlation algorithms that can be used include cross-correlation or cross-correlation while applying a systematic shift, extension, or compression of the source data series to the target data series. The correlation can operate by measuring the similarity between the two data sequences. In this case, the correlation can be specifically configured to find a portion of a relatively large data sequence that is most closely similar to a relatively small data sequence.

For example, when dynamic time warping is applied to a well log, the system may determine which portion of a second well log is most similar to a particular portion of a first well log. This may be accomplished by supplying the system with a sequence of "source" data. The source may be the portion of the well log that was initially associated with the seed pick, and dynamic time warping is performed using the source data and the second well log to identify the portion of the second well log that is most closely similar to the source. The system may then consider this best match to be the same portion of the well top as the well top identified by the seed pick. The identified best match may be used at a later time as a source for performing dynamic time warping on other well logs.

Dynamic Time Warping involves finding the sequence in the target that is most similar to the source. One advantage of dynamic time warping is that it allows comparison of two sequences that can vary in time, speed, or distance. This allows the system to correlate well tops regardless of differences in width and depth. One method of performing dynamic time warping is subsequence dynamic time warping. Subsequence dynamic time warping is particularly well suited for situations where the source is much smaller than the target. An initial cost matrix (C[i][j]) of the "distance" between each source or target pairing can be calculated. This calculation can be described by Equation 1:

For i = 1, M+1, j = 1, N (1)

Where w _n is the assigned weight for each input well log, and source[i] and target[j] are the corresponding well logs for the source and target in two-dimensional arrays of size M and N. Any number of local minima can be recorded. A cumulative cost matrix can then be calculated by accumulating the distances calculated in the initial cost matrix. Then, using the locations of the local minima, the system can backtrack from the location of the local minima in the cumulative cost matrix to the location where the cumulative cost matrix reaches zero. The path obtained through the cumulative cost matrix during the backtracking is then saved as the best warped path. Further details and examples describing subsequence dynamic time warping can be found in Appendix A, which is incorporated herein by reference.

The system may also calculate a cumulative confidence and a quality value for each selection made by dynamic time warping or other types of correlation (operation 460). The quality value provides a quality measurement that indicates the similarity between the selected target selection and the source. The calculation of the quality value for the match may be implemented using any available method. In one example, the system may calculate a quality value for the selection itself and a cumulative confidence value combined with the selections on which the selection is based. The cumulative confidence value may be calculated monotonically as a non-increasing function of the cumulative confidence value of the source and the quality value of the current selection. For example, if the selection made by dynamic time warping has a quality value of 0.9 and the source used to make the selection has a confidence value of 0.9, the cumulative confidence value may be a combination of the quality and confidence values using a monotonically non-increasing function.

In one example, the system can calculate a Pearson quality measure (q) between the source and the target. The Pearson quality measure is described by Equation 2:

(2)

Where X and Y represent the correlated data sequences that are input into the warping function. As described above, the system can be configured to utilize quality values ranging from 0 to 1. Therefore, any negative correlation found can be set to have a value of 0, since we are not interested in inverse correlations.

The cumulative confidence value for the choice made at the target node can be calculated by taking the confidence value of the choice from the source and multiplying it by the quality value of the current target choice. This relationship can be described by Equation 3:

(3)

Where C(i) is the confidence value of the source, and q is the quality value of the selection. Therefore, the cumulative confidence value of the new selection is a function of the confidence value of the source selection. For example, if the confidence value of the source is 0.9 and the quality value of the new selection is 0.9, the cumulative confidence value can be the product of the two values (0.81).

For each choice determined by dynamic time warping, an element identifying the choice and including a confidence value is added to a confidence priority queue (operation 480). In some cases, the system may reject choices that fall below a quality value or an accumulated confidence value (operation 470). Choices that meet a confidence threshold may be added to a confidence priority queue (operation 480).

Referring back to Figure 6, if the seed selection is at node 610, correlation will begin at the neighbors of node 610 (here, nodes 611, 613, and 614). This correlation will result in selections made at each node, where each selection is assigned a confidence value. These selections are then added to a confidence priority queue. The system then performs correlation on the new highest confidence selection in the queue. For example, if node 613 produces the highest confidence selection, the selection at node 613 is used for the next round of correlation. The natural neighbors of node 613 include nodes 611, 615, and 614. Then, correlation between the selections at node 613 is performed for each natural neighbor (nodes 611, 615, and 614). In this case, the result of the correlation of node 615 is added to the queue, but nodes 611 and 614 have already been correlated, and the corresponding selections already exist in the queue. When a selection is already in the queue, the confidence of the selection can be compared with the confidence of the new selection. If the new selection has a higher confidence, then that confidence can be used to update the confidence of the selection in the queue, and the queue is reordered appropriately. If the confidence in the new selection is lower than the corresponding confidence in the queue, then the new selection is discarded.

It should be understood that the use of dynamic time warping, Delaunay triangulation, and Pearson quality measures represents a single embodiment of a system for automatically correlating geological tops. Other methods or algorithms may be used in place of the provided methods. The system is configured to measure the similarity between two well logs from adjacent wells. More specifically, the system is configured to identify a portion of a well log that is most similar to a specified source portion of another well log. The identified portion can then be used as a source for measuring the similarity between the source portion and the well logs of any adjacent wells. Whenever a selection has been identified, a confidence value can be assigned to it. The confidence value can be a function of the degree of similarity between the identified selection and the source selection and the confidence value of the source selection. Thus, the confidence value is a monotonically non-increasing cumulative confidence measure. In addition, the well log locations can be input into a fully connected or partially connected graph, and any method can be used to determine the neighbors of the wells.

The system can also utilize any algorithm for maximizing the cumulative confidence value for each correlated well log and producing a reproducible, unique result. For example, by adding each correlated pick to a confidence priority queue and then performing correlation on the neighbors of the current highest confidence pick, the system progresses through the well logs along the path with the highest confidence. This is similar in operation to a longest path or maximum spanning tree algorithm. In one example, a shortest path algorithm can be used by inverting the cumulative confidence values. In other examples, a parallel relaxation algorithm can be used.

FIG7 depicts an exemplary automated top correlation system (ATCS) 700 according to aspects of the present invention. ATCS 700 includes a computing device 702 or other computing device or system that includes an automated top correlation application (ATCA) 704. ATCS 700 also includes a data source 706 that stores well logs. While the data source is shown as being located on computing device 702, it is contemplated that data source 706 may be a database located on another computing device or computing system connected to computing device 702.

The computing device 702 can be a laptop computer, a personal digital assistant, a tablet computer, a smart phone, a standard personal computer, or another processing device. The computing device 702 includes a display 708, such as a computer monitor, for displaying data and/or a graphical user interface. The computing device 702 can also include an input device 710, such as a keyboard or a pointing device (e.g., a mouse, trackball, pen, or touch screen) for interacting with various data entry forms to submit image slice selection data and/or surface fault point input data.

According to one aspect, the displayed set of well logs is itself a table of entries that responds to user input. For example, a user of computing device 702 can interact with the well logs by using a mouse to select a specific area of the well logs to submit seed selection data. It is also contemplated that a user can submit seed selection data by interacting with one or more displayed fields (not shown) to enter coordinates corresponding to a specific section of the well logs. After the seed selection data is entered, a seed selection request is generated and provided to ATCA 704 for processing.

According to one aspect, the displayed well log itself is another table of entries that responds to user input. For example, a user of computing device 702 can interact with the displayed well log by using a mouse to select at least one specific point on the well log to submit seed selection input data. It is also contemplated that the user can submit seed selection input data by interacting with one or more displayed fields (not shown) to enter coordinates corresponding to the at least one specific point. After the seed selection input data is entered and submitted, a well top correlation request is generated and provided to ATCA 704 for processing.

While ATCS 700 is depicted as being implemented on a single computing device, it is contemplated that in other aspects ATCA 704 may be performed by a server computing device (not shown) that receives seed selection requests, log selection requests, and/or other input data from a remote client computer (not shown) via a communications network such as the Internet.

According to one aspect, computing device 702 includes a processing system 712 comprising one or more processors or other processing devices. Computing device 702 also includes a computer readable medium ("CRM") 714 configured with ATCA 704. ATCA 704 includes instructions or modules executable by processing system 712 to interpret well tops in well logs.

The CRM 714 may include volatile media, nonvolatile media, removable media, non-removable media, and/or another available media that can be accessed by the computing device 700. By way of example and not limitation, the CRM 714 includes computer storage media and communication media. Computer media includes non-transitory memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Communication media may embody computer-readable instructions, data structures, program modules, or other data, and include information transport media or systems.

GUI module 716 displays a plurality of well logs received, for example, from data source 706, in response to a well log retrieval request. The well log retrieval request may be generated, for example, by a user of computing device 702 interacting with the well log retrieval request (not shown). The well log image may be displayed as described in connection with operations 400, 410, and 420 of FIG. 4 .

As described above, the well logs each contain multiple measurements compiled into a three-dimensional graph. The GUI module 716 displays a particular well log or group of well logs in response to a seed pick selection request, such as described above in connection with operations 400 and 410 of FIG.

The well top correlation module 718 generates an indicator of a well top by performing dynamic time warping on the well logs. The well top indicator corresponds to a well top at a first location on at least a first well log specified by a user-selected seed selection, such as described above in connection with operation 410 of FIG. 4 .

According to one aspect, the well top correlation module 718 performs dynamic time warping starting at the natural neighbor of the highest confidence pick, such as described in conjunction with operations 430, 440, and 450 of FIG. 4. The well top correlation module 718 may proceed to the next highest confidence pick and perform dynamic time warping on the natural neighbor of that pick. This may be repeated until all well logs have been analyzed. According to another aspect, the well top correlation module 718 also assigns a confidence value to each pick made using dynamic time warping.

The above description includes exemplary systems, methods, techniques, instruction sequences and/or computer program products that embody the technology of the present disclosure. However, it is to be understood that the disclosure may be implemented without these specific details. In the present disclosure, the disclosed methods may be implemented as a collection of instructions or software that can be read by a device. Furthermore, it is to be understood that the specific order or hierarchy of steps in the disclosed methods are examples of exemplary methods. Based on design preferences, it is to be understood that the specific order or hierarchy of steps of the present methods can be rearranged while remaining within the disclosed subject matter. The accompanying method claims present the elements of the various steps in a sample order and are not necessarily intended to be limited to the specific order or hierarchy presented.

The disclosure may be provided as a computer program product or software, which may include a machine-readable medium having stored thereon instructions that can be used to program a computer system (or other electronic device) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) that can be read by a machine (e.g., a computer). Machine-readable media may include, but are not limited to, magnetic storage media (e.g., floppy disks), optical storage media (e.g., CD-ROMs); magneto-optical storage media; read-only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of media suitable for storing electronic instructions.

It is believed that the present disclosure and its many attendant advantages will be understood from the foregoing description, and it will be apparent that various changes may be made in the form, construction and arrangement of parts without departing from the disclosed subject matter or sacrificing all of its substantial advantages. The form described is illustrative only, and it is the intention of the following claims to cover and include such changes.

Although the present disclosure has been described with reference to various embodiments, it will be understood that these embodiments are illustrative and that the scope of the present disclosure is not limited to these embodiments. Many variations, modifications, additions, and improvements are possible. More generally, embodiments according to the present disclosure have been generally described in the context of specific implementations. The functionality in the block diagrams may be separated or combined in the block diagrams or described using different terms in various embodiments of the present disclosure.

For illustrative purposes, the foregoing description has been described with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. In light of the above teachings, many modifications and variations are possible. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, so as to enable others in the art to best utilize the disclosure and various embodiments with various modifications as are suitable for the particular use contemplated.

The systems and methods disclosed herein may be implemented via one or more components, systems, servers, devices, other subcomponents, or distributed among such elements. When implemented as a system, such a system may specifically include and/or involve components such as software modules, a general-purpose CPU, RAM, etc. found in a general-purpose computer. In embodiments where the innovation resides on a server, such a server may include or involve components such as a CPU, RAM, etc., such as those found in a general-purpose computer.

In addition, the systems and methods herein may be implemented via implementations having more than irrelevant or disparate software, hardware, and/or firmware components than those set forth above. For example, with respect to such other components (e.g., software, processing components, etc.) and/or computer-readable media associated with or embodying the present invention, aspects of the innovations herein may be implemented consistently with many general-purpose or special-purpose computing systems or configurations. Various exemplary computing systems, environments, and/or configurations that may be suitable for use with the innovations herein may include, but are not limited to, software or other components embodied within or on a personal computer, server, or server computing device, such as routing/connectivity components, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, consumer electronic devices, network PCs, other existing computer platforms, distributed computing environments including one or more of the above-mentioned systems or devices, and the like.

In some cases, for example, aspects of the present systems and methods may be implemented or performed via logic and/or logic instructions comprising program modules executed in association with such components or circuits. Generally, program modules may include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific instructions herein. The present invention may also be implemented in the context of a distributed software, computer, or circuit configuration in which circuits are connected via a communication bus, circuit, or link. In a distributed configuration, control/instructions may occur from both local and remote computer storage media, including memory storage devices.

The software, circuits and components herein may also include and/or utilize one or more types of computer-readable media. Computer-readable media can be any available medium that resides on such circuits and/or computing components, can be associated therewith or can be accessed by them. By way of example and not limitation, computer-readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage device or any other medium that can be used to store desired information and can be accessed by computing components. Communication media may include computer-readable instructions, data structures, program modules and/or other components. In addition, communication media may include wired media, such as wired networks or direct wired connections, however, any such type of media herein does not include transient media. Any combination of the above is also included within the scope of computer-readable media.

In this description, term parts, modules, equipment etc. can refer to any type of logic or functional software element, circuit, block and/or process that can be realized in a variety of ways.For example, the functions of various circuits and/or blocks can be combined into the module of any other number.Each module can even be implemented as a software program stored in a tangible memory (for example, random access memory, read-only memory, CD-ROM memory, hard disk drive etc.) so that it can be read by a central processing unit to realize the innovative function herein.Or, module can include programming instructions that are transmitted to a general-purpose computer or processing/graphics hardware via a transmission carrier.And, module can be implemented as the hardware logic circuit that realizes the function covered by the innovation herein.Finally, module can be realized using special instructions (SIMD instructions), field programmable logic arrays or any hybrid thereof that provides the performance and cost of a desired level.

As disclosed herein, features consistent with the present disclosure can be implemented via computer hardware, software, and/or firmware. For example, the systems and methods disclosed herein can be embodied in various forms, including, for example, data processors, such as computers that also include databases, digital electronic circuits, firmware, software, or a combination thereof. In addition, although some of the disclosed embodiments describe specific hardware components, any combination of hardware, software, and/or firmware can be used to implement systems and methods consistent with the innovations herein. In addition, the above-mentioned adjustments and other aspects and principles of the innovations herein can be implemented in various environments. Such environments and related applications can be specifically configured to perform various routines, processes, and/or operations according to the present invention, or they can include general-purpose computers or computing platforms that are selectively activated or reconfigured by code to provide the desired functionality. The processes disclosed herein are not inherently related to any particular computer, network, architecture, environment, or other device, and can be implemented using appropriate combinations of hardware, software, and/or firmware. For example, various general-purpose machines can be used together with programs written according to the teachings of the present invention, or it may be more convenient to construct dedicated devices or systems to perform the desired methods and techniques.

Aspects of the methods and systems described herein (such as logic) can also be implemented as functionality programmed into any of a variety of circuits, including programmable logic devices ("PLDs"), such as field programmable gate arrays ("FPGAs"), programmable array logic ("PAL") devices, electrically programmable logic and memory devices, and standard memory cell-based devices, as well as application-specific integrated circuits. Some other possibilities for implementing various aspects include: memory devices, microcontrollers with memory (such as EEPROM), embedded microprocessors, firmware, software, etc. In addition, various aspects can be embodied in microprocessors with software-based circuit simulation, discrete logic (continuous and combinational), custom devices, fuzzy (neural) logic, quantum devices, and any hybrid of these types. The underlying device technology can be provided using a variety of component types (e.g., metal oxide semiconductor field effect transistor ("MOSFET") technology similar to complementary metal oxide semiconductor ("CMOS"), bipolar technology similar to emitter coupled logic ("ECL"), polymer technology (e.g., silicon conjugated polymer and metal conjugated polymer metal structures), analog and digital hybrids, etc.

It should also be noted that the various logic and/or functions disclosed herein may be implemented using any number of combinations of hardware, firmware, and/or as data and/or instructions embodied in various machine-readable or computer-readable media in terms of their behavior, register transfers, logic components, and/or other characteristics. Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, various forms of non-volatile storage media (e.g., optical, magnetic, or semiconductor storage media), but do not include transient media. Unless the context clearly requires otherwise, throughout this description, the words "include," "comprising," and the like should be understood in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is, in the sense of "including but not limited to." Words used in the singular or plural also include the plural or singular, respectively. In addition, the words "herein," "herein below," "above," "hereafter," and words of similar import refer to this application as a whole and not to any particular portion of this application. When the word "or" is used with reference to a list of two or more items, the word encompasses all of the following interpretations of the word: any item in the list, all items in the list, and any combination of items in the list.

Although certain presently preferred embodiments of the present invention have been described in detail herein, it will be apparent to those skilled in the art that changes and modifications of the various embodiments shown and described herein may be made without departing from the spirit and scope of the invention. Accordingly, it is intended that the present invention be limited only to the extent required by applicable legal rules.

Although the foregoing has been described with reference to specific embodiments of the present disclosure, those skilled in the art will recognize that changes may be made in these embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the appended claims.

Appendix A

This appendix briefly covers Subsequence Dynamic Time Warping ("SDTW") with a modified graph from Muller (2007). SDTW is an extension of DTW in which one of the data sequences, here called the "source," is much smaller than the data sequence to be compared against (the "target"). A smaller subsequence of the source data is found in a larger target data sequence. Many possible best matches for the subsequence can be found in the larger target data sequence. The goal is to find the best subsequence match in the larger data sequence. Once the best match is found, the best path for the subsequence can be determined on a sample-by-sample basis and mapped to the target sequence. This is the best matching function we seek to map well selections from one well to well selections from another well.

a) Calculate an initial cost matrix ('C') between the 'source data sequence (size M+1) and the 'target data sequence' (size N). The source data sequence is much smaller because it is picked around the well pick locations shown in red by a user-set window (shown in blue). This window can be expanded to increase the potential for matches at the expense of longer computation time and increased memory usage. The initial cost matrix is first calculated using a double loop of size (M+1) x N and calculating the 'distance' between each source and target pair as described in step 8, or the following formula:

For i = 1, M+1, j = 1, N

The main difference between SDTW and DTW is that one or more rows are added to the cost matrix and set to 0. This allows the subsequence to terminate at the first row of M in the accumulation and backtracking steps described below.

b) shows the distance function along the last row of M. The three red dots show the local minima found (b*, _b2 *, and _b3 *). These minima will define the ending boundaries of the subsequence that is best found in the larger target sequence of size N.

c) The cumulative cost matrix of the matrix calculated in step a above. This matrix is calculated by looping over the columns of size N, starting at the 0th element for the M+1 dimension, and then accumulating the distances calculated in step a. This accumulation is constrained in that it only considers strides of 1, and the accumulation must always be monotonic with respect to M+1 and N. For example, if nodes m and n are being evaluated in the loop, the only nodes considered are:

That is, considering only the three previous node configurations:

When it reaches M, the accumulation stops. When it reaches N, the accumulation stops at M, M.

d) Once the cumulative cost matrix according to step c is calculated, the local minimum of step b is traced back from M to the position where the cost matrix reaches zero on the y-axis, always finding the minimum in the backtracking. This will usually be along the index between 1 and N, because we set the first row of M to 0. Keep each 'i' and 'j' node index in the backtracking, and the final result is the best regular path. These subsequences or "patterns" are the best alignments of the source data sequence in the target data sequence. These subsequences are shown in the gray area above. It is important to select neighbors around the local minimum (b*) because there may be many local optima around b* that differ only by small shifts.

Claims (34)

1. A method for automatically correlated geological tops using at least one processor, the method comprising: Receive the first logging record from the first wellbore and at least the second logging record from the second wellbore; Receive at least one seed selection that designates a specific data sequence in the first logging record as the top of the well; Determine at least one neighboring point for each logging record; and The following steps define the highest confidence series for well logging record selection: retrieving the highest confidence selection, wherein the confidence of the selection is a non-increasing function of the path length from the at least one seed and a non-increasing function of the selection quality; determining a new selection by performing correlation on each neighbor of the well logging record; assigning a selection quality value to the new selection; adding the new selection to a confidence priority queue; and generating the highest confidence series selected at the top of the well. Adding the new selection to the confidence priority queue also includes: Search the confidence priority queue for the element corresponding to the new selection; When the confidence priority queue contains an element corresponding to the new selection and the confidence value of the selection exceeds the element confidence value that defines the confidence value of the element, update the element corresponding to the new selection; and When the confidence priority queue does not contain an element corresponding to the new selection, the new selection is added to the confidence priority queue. 2. The method of claim 1, wherein performing correlation further includes performing dynamic time warping on each neighboring point of the logging record. 3. The method of claim 1, further comprising displaying the first logging record and the at least second logging record, wherein a user selects a seed for displaying the first logging record and the at least second logging record. 4. The method of claim 1, wherein receiving at least one seed selection further includes assigning a high confidence value to the at least one seed selection. 5. The method of claim 3, wherein selecting the seed selection further includes assigning a high confidence value to the at least one seed selection. 6. The method of claim 1, further comprising displaying the highest confidence series selected at the top of the well. 7. The method of claim 1, wherein determining at least one neighbor for each logging record comprises adding each logging record to a complete weighted graph, wherein each logging record is represented by a vertex, and each edge weight represents the distance between a first logging record and a second logging record, and determining at least one neighbor for each vertex. 8. The method of claim 1, wherein receiving the first logging record and the at least second logging record further includes the user selecting the first logging record and the at least second logging record. 9. The method of claim 7, wherein determining at least one neighboring point for each logging record further includes using Delaunay triangulation. 10. A method for automatically correlated geological tops using at least one processor, the method comprising: Receive the first logging record and at least the second logging record; Receive at least one seed selection that designates a specific data sequence in the first logging record as the top of the well; The at least one seed selection is added to a confidence priority queue, wherein the confidence priority queue is configured to assign priorities based on confidence values, and wherein the at least one seed selection is assigned the highest priority, and wherein the confidence value of the selection is a non-increasing function of the path length of the at least one seed and a non-increasing function of the quality of the selection. Determine at least one neighboring point for each logging record; and The highest confidence series for well top selection is defined by the following steps: traversing the confidence priority queue by retrieving and removing a first element from the queue until it is empty; determining a new selection by performing correlation on each neighbor of the first element; assigning a selection quality value to the new selection; adding the new selection to the confidence priority queue based on the confidence value; and generating the highest confidence series for well top selection. Adding the new selection to the confidence priority queue includes: Search the confidence priority queue for the element corresponding to the new selection; When the confidence priority queue contains an element corresponding to the new selection and the confidence value of the selection exceeds the element confidence value that defines the confidence value of the element, update the element corresponding to the new selection; and When the confidence priority queue does not contain an element corresponding to the new selection, the new selection is added to the confidence priority queue. 11. The method of claim 10, wherein adding the new selection to the confidence priority queue further comprises: adding the new selection to the confidence priority queue when the quality value of the selection exceeds at least one of a quality threshold and a cumulative confidence threshold. 12. The method of claim 11, wherein the selected quality value is determined using the following formula: Here, X and Y represent the relevant data sequences input into the regularization function. 13. The method of claim 10, wherein determining at least one neighboring point for each logging record comprises: Each logging record is added to the complete weighted graph, where each logging record is represented by a vertex, and each edge weight represents the distance between the first and second logging records; and Delaunay triangulation is used to determine at least one neighboring point for each vertex. 14. The method of claim 10, wherein the mass value is determined using a Pearson mass measurement. 15. The method of claim 14, wherein the new confidence value is a function of the Pearson quality measurement and the confidence value. 16. The method of claim 10, wherein performing the correlation further includes performing dynamic time warping on each neighboring point of the logging record. 17. The method of claim 10, further comprising: displaying the first logging record and the at least second logging record, and having a user select a seed for displaying the first logging record and the at least second logging record. 18. The method of claim 10, further comprising displaying the highest confidence series selected from the top of the well. 19. The method of claim 10, wherein receiving the first logging record and the at least second logging record further includes selecting the first logging record and the at least second logging record by a user. 20. An apparatus for automatically correlated geological tops using at least one processor, comprising: A computer system having at least one processor, wherein the processor is configured to: Receive the first logging record from the first wellbore and at least the second logging record from the second wellbore; Receive at least one seed selection that designates a specific data sequence in the first logging record as the top of the well; Determine at least one neighboring point for each logging record; and The following steps define the highest confidence series for welltop selection: retrieving the highest confidence selection, wherein the confidence of the selection is a non-increasing function of the path length from the at least one seed and a non-increasing function of the selection quality; determining a new selection by performing correlation on each neighboring point of the logging record; assigning a selection quality value to the new selection; and generating the highest confidence series for welltop selection. The processor is configured to: Search the confidence priority queue for the element corresponding to the new selection. When the confidence priority queue contains an element corresponding to the new selection and the confidence value of the selection exceeds the element confidence value that defines the confidence value of the element, the element corresponding to the new selection is updated, and When the confidence priority queue does not contain an element corresponding to the new selection, the new selection is added to the confidence priority queue. 21. The apparatus of claim 20, wherein the processor is configured to perform dynamic time warping on each neighboring point of the logging record. 22. The apparatus of claim 20, wherein the computer system has a display for displaying the first logging record and the at least second logging record. 23. The apparatus of claim 22, wherein the computer system has an input device configured to allow a user to select a seed for displaying the first logging record and the at least second logging record. 24. The apparatus of claim 20, wherein the processor is configured to assign a high confidence value to the at least one seed selection. 25. The apparatus of claim 22, wherein the display shows the highest confidence series selected from the top of the well. 26. The apparatus of claim 20, wherein the processor is configured to add each logging record to a complete weighted graph, wherein each logging record is represented by a vertex, and each edge weight represents the distance between a first logging record and a second logging record and determines at least one neighboring point for each vertex. 27. The apparatus of claim 20, wherein the processor is configured to add the at least one seed selection to a confidence priority sequence, wherein the confidence priority queue is configured to assign priorities based on confidence values, and wherein the at least one seed selection is assigned the highest priority. 28. The apparatus of claim 27, wherein the processor is configured to define a series of highest confidence scores for well top selections by: traversing the confidence priority queue by retrieving a first element from the queue and removing the first element from the queue until the queue is empty; determining a new selection by performing correlation on each neighbor of the first element; assigning a selection quality value to the new selection; adding the new selection to the confidence priority queue based on the confidence value; and generating the series of highest confidence scores for well top selections. 29. The apparatus of claim 20, wherein the processor is configured to add the new selection to the confidence priority queue when the selected quality value exceeds at least one of a quality threshold and a cumulative confidence threshold. 30. The apparatus of claim 29, wherein the selected mass value is determined by the following formula: Here, X and Y represent the relevant data sequences input into the regularization function. 31. The apparatus of claim 20, wherein the computer system further comprises: a computer and one of one or more computing resources. 32. The apparatus of claim 31, wherein the one or more computing resources are one of one or more server computing devices and one or more cloud computing resources. 33. The apparatus of claim 32, further comprising a computing device used by a user to access the one or more server computing devices. 34. The apparatus of claim 26, wherein the processor is configured to use Delaunay triangulation to determine the at least one neighboring point for each vertex.