Stratum dip angle calculation method and device, electronic equipment and storage medium

Abstract

The invention discloses a stratigraphic dip angle calculation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring measurement-while-drilling recording and guiding big data of previous well drilling and a corresponding geological judgment result; on the basis of the measurement-while-drilling logging big data and the corresponding geological judgment result, well drilling logging data to be calculated are compared with the measurement-while-drilling logging big data, and an optimal stratigraphic dip angle calculation data point in the well drilling logging data to be calculated is determined; and calculating the stratigraphic dip angle of the to-be-calculated drilling well logging data according to the optimal stratigraphic dip angle calculation data point. According to the scheme provided by the invention, the stratigraphic dip can be automatically calculated without depending on manual calculation, the number of personnel required by geosteering work can be effectively reduced, the calculation speed is increased, and the drilling rate of a high-quality shale reservoir is increased.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of drilling engineering geological steering, and in particular, to a method, device, electronic device and storage medium for calculating formation dip angle. Background Art

[0002] With the increasing demand for oil and gas resources and the reduction of remaining resources, the focus of oilfield development has gradually shifted to harsh environments such as offshore and desert areas, or unconventional oil and gas reservoirs such as low-porosity and low-permeability reservoirs, thin layers, marginal reservoirs, shale oil and gas. The form of reservoir development and geological conditions have led to an increasing number of high-angle deviated wells, extended-reach wells and horizontal wells in drilling operations. Under increasingly complex geological conditions, the measurement-while-drilling (MWD) and logging-while-drilling (LWD) control technology, also known as precision drilling technology, plays an increasingly important role. It provides measurement and control technology services for drilling projects such as horizontal wells to achieve high-quality and efficient oilfield development around geological engineering objectives.

[0003] Geological steering drilling is the most important link in precision drilling. Geological steering drilling is a process technology that can identify geological targets in real time during drilling and guide the drill bit to drill towards the geological targets. The control of the horizontal wellbore trajectory from traditional geometric control to geological steering target control is a landmark drilling technology, and it is an important technical means to improve the reservoir encounter rate and success rate of horizontal wells in complex oil and gas reservoirs.

[0004] However, at present, the related products and services of geological steering at home and abroad are all based on manual judgment and evaluation. The geological steering technology still relies heavily on manual experience judgment, with slow speed, easy mistakes, inconsistent steering instructions in multiple shifts, and the accuracy of steering instructions needs to be improved, which may even lead to serious accidents such as the instrument falling into the well, and the workload of geological steering personnel is too heavy. Summary of the Invention

[0005] To solve the technical problems that the calculation of formation dip angle seriously depends on manual calculation, with slow speed and easy mistakes, the embodiments of the present invention provide a method, device, electronic device and storage medium for calculating formation dip angle.

[0006] The technical solution of the embodiments of the present invention is realized as follows:

[0007] The embodiments of the present invention provide a method for calculating formation dip angle, which is characterized in that the method includes: obtaining the big data of measurement-while-drilling, logging-while-drilling and geological steering of previous wells and the corresponding geological judgment results; based on the big data of measurement-while-drilling, logging-while-drilling and geological steering and the corresponding geological judgment results, comparing the well logging data to be calculated with the big data of measurement-while-drilling, logging-while-drilling and geological steering to determine the optimal formation dip angle calculation data points in the well logging data to be calculated; and calculating the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data points.

[0008] In one embodiment, based on the big data of logging-while-drilling and the corresponding geological judgment results, comparing the well logging data to be calculated with the big data of logging-while-drilling to determine the optimal formation dip calculation data points in the well logging data to be calculated includes: annotating the big data of logging-while-drilling according to the geological judgment results corresponding to the big data of logging-while-drilling, and annotating the optimal formation dip calculation data points of the big data of logging-while-drilling; performing a maximum similarity attribute comparison between the well logging data to be calculated and the annotated big data of logging-while-drilling, and selecting the position with the largest correlation coefficient in the well logging data to be calculated as the optimal formation dip calculation data points in the well logging data to be calculated.

[0009] In one embodiment, before annotating the big data of logging-while-drilling according to the geological judgment results corresponding to the big data of logging-while-drilling, the method further includes: converting the big data of logging-while-drilling into a standard digital format, and cleaning and purifying the incomplete data, error data and redundant data in the big data of logging-while-drilling.

[0010] In one embodiment, calculating the formation dip of the well logging data to be calculated according to the optimal formation dip calculation data points includes: determining the vertical depth and sounding data of the well logging data to be calculated according to the optimal formation dip calculation data points; based on the vertical depth and the sounding data, calculating the horizontal distance difference and height difference of the well logging data to be calculated; according to the horizontal distance difference and the height difference, calculating the formation dip of the well logging data to be calculated.

[0011] In one embodiment, calculating the formation dip of the well logging data to be calculated according to the horizontal distance difference and the height difference includes: according to the horizontal distance difference and the height difference, calculating the formation dip of the well logging data to be calculated by using the following calculation formula (1):

[0012]

[0013] where α represents the formation dip, Δh1 represents the height difference, and Δd1 represents the horizontal distance difference.

[0014] In one embodiment, after calculating the formation dip of the well logging data to be calculated according to the optimal formation dip calculation data points, the method further includes: determining the target point position of the well to be calculated according to the formation dip of the well logging data to be calculated.

[0015] In one embodiment, determining the target position of the well to be calculated according to the formation dip angle of the well logging data to be calculated includes: according to the formation dip angle of the well logging data to be calculated, using the following calculation formula (2) to determine the target position of the well to be calculated:

[0016]

[0017] Δz = Δx / cos(α)

[0018] z A = z C + (Δz + Δh) Calculation formula (2)

[0019] where x A 、y A and z A are the position coordinates of the target point, x C 、y C and z C are the position coordinates of the optimal formation dip angle calculation data point, Δx and Δz are the distance coordinates between the target point and the optimal formation dip angle calculation data point, and Δh is the formation vertical thickness.

[0020] An embodiment of the present invention also provides a formation dip angle calculation device, which includes: an acquisition module for acquiring the large data of logging while drilling and the corresponding geological judgment results of previous wells; a determination module for comparing the well logging data to be calculated with the large data of logging while drilling based on the large data of logging while drilling and the corresponding geological judgment results to determine the optimal formation dip angle calculation data point in the well logging data to be calculated; a calculation module for calculating the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data point.

[0021] An embodiment of the present invention also provides an electronic device, including: a sensor, a processor, and a memory for storing a computer program that can run on the processor; wherein, when the processor is used to run the computer program, it executes the steps of any of the above methods.

[0022] An embodiment of the present invention also provides a storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0023] The formation dip angle calculation method, device, electronic device, and storage medium provided by the embodiments of the present invention obtain the big data of logging-while-drilling (LWD) of past wells and the corresponding geological judgment results; based on the big data of LWD and the corresponding geological judgment results, compare the well logging data to be calculated with the big data of LWD to determine the optimal formation dip angle calculation data points in the well logging data to be calculated; and calculate the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data points. The solution provided by the present invention can automatically calculate the formation dip angle without relying on manual calculation, effectively reducing the number of personnel required for geological steering work, improving the calculation speed, and increasing the drilling encounter rate of high-quality shale reservoirs. Description of the Drawings

[0024] Figure 1 It is a schematic flowchart of the formation dip angle calculation method according to the embodiment of the present invention;

[0025] Figure 2 It is a flowchart example of the real-time intelligent calculation method of the formation dip angle according to the embodiment of the present invention;

[0026] Figure 3 It is an example diagram of calculating the formation dip angle by the formation isopach method according to the embodiment of the present invention;

[0027] Figure 4 It is a schematic structural diagram of the formation dip angle calculation device according to the embodiment of the present invention;

[0028] Figure 5 It is an internal structure diagram of the computer device according to the embodiment of the present invention. Detailed Embodiments

[0029] The present invention will be further described in detail below in conjunction with the drawings and embodiments.

[0030] The embodiments of the present invention provide a formation dip angle calculation method, as Figure 1 shown, the method includes:

[0031] Step 101: Obtain the big data of logging-while-drilling (LWD) of past wells and the corresponding geological judgment results;

[0032] Step 102: Based on the big data of LWD and the corresponding geological judgment results, compare the well logging data to be calculated with the big data of LWD to determine the optimal formation dip angle calculation data points in the well logging data to be calculated;

[0033] Step 103: Calculate the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data points.

[0034] This embodiment can automatically complete the calculation of formation dip angle, effectively reduce the number of personnel required for geosteering work, improve work efficiency, and increase the drilling encounter rate of high-quality shale reservoirs.

[0035] Specifically, the big data of drilling, logging, and guiding in this application is optimized from the data obtained during past drilling operations. The data that is easy to mark and for which the formation dip angle can be calculated accurately manually is selected. Information such as the corresponding manually calculated formation dip angle and the optimal formation dip angle calculation data points is used as the corresponding geological judgment result.

[0036] In one embodiment, based on the big data of drilling, logging, and guiding while drilling and the corresponding geological judgment results, comparing the well logging data to be calculated with the big data of drilling, logging, and guiding while drilling to determine the optimal formation dip angle calculation data points in the well logging data to be calculated includes:

[0037] Mark the big data of drilling, logging, and guiding while drilling according to the geological judgment results corresponding to the big data of drilling, logging, and guiding while drilling, and mark the optimal formation dip angle calculation data points of the big data of drilling, logging, and guiding while drilling;

[0038] Perform a maximum similarity attribute comparison between the well logging data to be calculated and the marked big data of drilling, logging, and guiding while drilling, and select the position with the largest correlation coefficient in the well logging data to be calculated as the optimal formation dip angle calculation data points in the well logging data to be calculated.

[0039] Specifically, in one embodiment, before marking the big data of drilling, logging, and guiding while drilling according to the geological judgment results corresponding to the big data of drilling, logging, and guiding while drilling, the big data of drilling, logging, and guiding while drilling can also be preprocessed first. That is, convert the big data of drilling, logging, and guiding while drilling into a standard digital format, and clean and purify the incomplete data, error data, and redundant data in the big data of drilling, logging, and guiding while drilling. Through the above operations, the influence of interference data on the calculation results can be effectively reduced, the calculation accuracy rate can be provided, and the calculation workload can be reduced.

[0040] Since the geological judgment results corresponding to the past big data of drilling, logging, and guiding while drilling include information such as the optimal formation dip angle calculation data points and formation dip angles of the past data while drilling. Therefore, in this embodiment, the big data of drilling, logging, and guiding while drilling can be marked first according to the geological judgment results corresponding to the big data of drilling, logging, and guiding while drilling, and the optimal formation dip angle calculation data points of the big data of drilling, logging, and guiding while drilling are marked; then perform a maximum similarity attribute comparison between the well logging data to be calculated and the marked big data of drilling, logging, and guiding while drilling, select the position with the largest correlation coefficient in the well logging data to be calculated as the optimal formation dip angle calculation data points in the well logging data to be calculated, and then calculate the formation dip angle of the well logging data to be calculated based on the optimal formation dip angle calculation data points.

[0041] Here, a conventional maximum similarity attribute comparison method can be adopted, and the maximum similarity attribute comparison method will not be elaborated in detail in this embodiment.

[0042] After determining the optimal formation dip calculation data points, in one embodiment, the following process can be adopted to calculate the formation dip of the well logging data to be calculated according to the optimal formation dip calculation data points:

[0043] Determine the vertical depth and sounding data of the well logging data to be calculated according to the optimal formation dip calculation data points;

[0044] Based on the vertical depth and the sounding data, calculate the horizontal distance difference and height difference of the well logging data to be calculated;

[0045] Calculate the formation dip of the well logging data to be calculated according to the horizontal distance difference and the height difference.

[0046] In this embodiment, after obtaining the vertical depth and sounding data of the well logging data to be calculated, the horizontal distance difference and height difference required for the formation dip can be obtained by calculating the differences of the vertical depth and sounding data of the well logging data to be calculated respectively.

[0047] Here, according to the horizontal distance difference and the height difference, the formation dip of the well logging data to be calculated can be calculated using the following calculation formula (1):

[0048]

[0049] where α represents the formation dip, Δh1 represents the height difference, and Δd1 represents the horizontal distance difference.

[0050] After completing the calculation of the formation dip of the well logging data to be calculated, the target position of the well to be calculated can be determined according to the formation dip of the well logging data to be calculated, laying a foundation for the estimation of the build-up rate.

[0051] In one embodiment, the determining the target position of the well to be calculated according to the formation dip of the well logging data to be calculated includes:

[0052] Determine the target position of the well to be calculated using the following calculation formula (2) according to the formation dip of the well logging data to be calculated

[0053]

[0054] Δz = Δx / cos(α)

[0055] position: z A = z C+(Δz + Δh) calculation formula (2)

[0056] Wherein, x A , y A and z A are the position coordinates of the target point, x C , y C and z C are the position coordinates of the optimal formation dip calculation data points, Δx and Δz are the distance coordinates between the target point and the optimal formation dip calculation data points, and Δh is the formation vertical thickness.

[0057] This embodiment can automatically calculate the formation dip accurately and automatically update the target point position, effectively reducing the workload of geological steering, increasing the drilling encounter rate of high-quality shale reservoirs, and achieving the goal of cost reduction and efficiency improvement.

[0058] The formation dip calculation method provided by the embodiment of the present invention obtains the big data of logging while drilling and the corresponding geological judgment results of previous drillings; based on the big data of logging while drilling and the corresponding geological judgment results, compares the logging data to be calculated with the big data of logging while drilling, and determines the optimal formation dip calculation data points in the logging data to be calculated; calculates the formation dip of the logging data to be calculated according to the optimal formation dip calculation data points. The solution provided by the present invention can automatically calculate the formation dip without relying on manual calculation, effectively reducing the number of personnel required for geological steering work, improving the calculation speed, and increasing the drilling encounter rate of high-quality shale reservoirs.

[0059] Next, the present invention will be described in detail in combination with application embodiments.

[0060] The embodiment of the present invention provides a real-time intelligent calculation method for formation dip. This embodiment can automatically calculate the formation dip and automatically update the position of the A target point (i.e., the target point in the above embodiment), effectively reducing the number of personnel required for geological steering work, and initially forming an intelligent auxiliary steering technology for long horizontal wells in shale gas reservoirs.

[0061] See Figure 2 , the specific implementation process of the method in this embodiment is as follows:

[0062] The first step: Prepare the big data of logging while drilling

[0063] Select the big data of logging while drilling in the preferred work area, unify the standard digital format, clean and purify incomplete data, error data, redundant data, etc., label the data according to the previous geological judgment, label information such as formation information, engineering sweet spots and geological sweet spots, and establish a hierarchical gradient management mode.

[0064] The second step: Obtain the optimal formation dip calculation data points

[0065] Based on the intelligent identification conclusion of the formation while drilling in the work area, the logging GR data of the pilot well and the well to be drilled (i.e., the drilling logging data to be calculated in the above embodiments) are sorted and compared. The correlation analysis method is used to intelligently identify the formation characteristic points, and an intelligent state network that meets the recognition of the guiding time signal sequence is built. By solving the maximum similarity attribute with the data of the comparison well, the optimal formation dip calculation data points C and C' are selected, and the corresponding vertical depth and sounding data are obtained as the optimal formation dip calculation data points. That is, assuming that a formation characteristic point of the pilot well is C', the correlation analysis method is used to solve the maximum similarity attribute with the data of the comparison well, and the position with the largest correlation coefficient is selected as the optimal formation dip calculation data point C, and the corresponding vertical depth and sounding data are obtained as the optimal formation dip calculation data points.

[0066] Step 3: Automatic calculation of formation dip

[0067] Two necessary parameters are mainly required for the formation dip calculation: the horizontal distance difference between two points at different positions on the same formation and the height difference between them. Based on the corresponding vertical depth and sounding data of the optimal formation dip calculation data points obtained in the second step, the horizontal distance difference Δd1 and the height difference Δh1 required for calculating the formation dip can be obtained, and then the formation dip α can be calculated. That is, based on the vertical depth and sounding data of the optimal formation dip calculation data point C obtained in the second step and the formation characteristic point C' of the pilot well, the horizontal distance difference Δd1 and the height difference Δh1 required for calculating the formation dip are calculated by taking the difference respectively, and then the formation dip α can be calculated through the calculation formula (1). The calculation formula (1) is as follows:

[0068]

[0069] Among them, α represents the formation dip, Δh1 represents the height difference, and Δd1 represents the horizontal distance difference

[0070] Step 4: Automatically update the position of Target A

[0071] Following the principle of constant true thickness of the formation, refer to Figure 3 , and update the position of Target A by extrapolation according to the equal thickness method. The calculation formula is as follows:

[0072]

[0073] Δz = Δx / cos(α)

[0074] z A = z C +(Δz + Δh) Calculation formula (2)

[0075] Among them, x A , y A and z A are the position coordinates of the target point, x C , y Cand z C are the position coordinates of the optimal formation dip calculation data points, Δx and Δz are the distance coordinates between the target point and the optimal formation dip calculation data points, and Δh is the vertical thickness of the formation.

[0076] Use the current formation dip data to calculate and correct the position of Target A, laying a foundation for the build rate estimation.

[0077] This embodiment can automatically calculate the formation dip more accurately and automatically update the position of Target A. First, based on the previous artificial formation and sweet spot evaluation conclusions, label the relevant big data for steering; then, based on the correlation analysis method, solve the maximum similarity attributes of the formation identification conclusions of the pilot well and the data of the well to be drilled, and obtain the corresponding vertical depth and measured depth data as the optimal formation dip calculation data points; finally, extrapolate and update the position of Target A according to the equal thickness method, laying a foundation for the build rate estimation. The present invention can automatically calculate the formation dip and automatically update the position of Target A, effectively reducing the number of personnel required for geological steering work and improving work efficiency.

[0078] To implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a formation dip calculation device, as Figure 4 shown. The formation dip calculation device 400 includes: an acquisition module 401, a determination module 402, and a calculation module 403; wherein,

[0079] The acquisition module 401 is used to acquire the big data of logging while drilling and the corresponding geological judgment results of previous wells;

[0080] The determination module 402 is used to compare the logging data of the well to be calculated with the big data of logging while drilling based on the big data of logging while drilling and the corresponding geological judgment results, and determine the optimal formation dip calculation data points in the logging data of the well to be calculated;

[0081] The calculation module 403 is used to calculate the formation dip of the logging data of the well to be calculated according to the optimal formation dip calculation data points.

[0082] In one embodiment, the step of comparing the logging data of the well to be calculated with the big data of logging while drilling based on the big data of logging while drilling and the corresponding geological judgment results, and determining the optimal formation dip calculation data points in the logging data of the well to be calculated includes: labeling the big data of logging while drilling according to the corresponding geological judgment results of the big data of logging while drilling, and labeling the optimal formation dip calculation data points of the big data of logging while drilling; comparing the logging data of the well to be calculated with the labeled big data of logging while drilling for the maximum similarity attribute, and selecting the position with the largest correlation coefficient in the logging data of the well to be calculated as the optimal formation dip calculation data points in the logging data of the well to be calculated.

[0083] In one embodiment, before annotating the logging-whilst-drilling big data according to the geological judgment result corresponding to the logging-whilst-drilling big data, the method further includes: converting the logging-whilst-drilling big data into a standard digital format, and cleaning and purifying incomplete data, error data and redundant data in the logging-whilst-drilling big data.

[0084] In one embodiment, calculating the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data points includes: determining the vertical depth and sounding data of the well logging data to be calculated according to the optimal formation dip angle calculation data points; based on the vertical depth and the sounding data, calculating the horizontal distance difference and the height difference of the well logging data to be calculated; and calculating the formation dip angle of the well logging data to be calculated according to the horizontal distance difference and the height difference.

[0085] In one embodiment, calculating the formation dip angle of the well logging data to be calculated according to the horizontal distance difference and the height difference includes: calculating the formation dip angle of the well logging data to be calculated by using the following calculation formula (1) according to the horizontal distance difference and the height difference:

[0086]

[0087] where α represents the formation dip angle, Δh1 represents the height difference, and Δd1 represents the horizontal distance difference.

[0088] In one embodiment, after calculating the formation dip angle of the well logging data to be calculated according to the optimal formation dip angle calculation data points, the method further includes: determining the target position of the well to be calculated according to the formation dip angle of the well logging data to be calculated.

[0089] In one embodiment, determining the target position of the well to be calculated according to the formation dip angle of the well logging data to be calculated includes: determining the target position of the well to be calculated by using the following calculation formula (2) according to the formation dip angle of the well logging data to be calculated:

[0090]

[0091] Δz = Δx / cos(α)

[0092] z A = z C +(Δz + Δh) Calculation formula (2)

[0093] where x A , y A and z Aare the position coordinates of the target point, x C , y C and z C are the position coordinates of the optimal formation dip calculation data points, Δx and Δz are the distance coordinates between the target point and the optimal formation dip calculation data points, and Δh is the formation vertical thickness.

[0094] In practical applications, the acquisition module 401, the determination module 402, and the calculation module 403 can be implemented by a processor in a formation dip calculation device.

[0095] It should be noted that: when the above device provided in the above embodiment is executed, only the above division of each program module is used for illustration. In practical applications, the above processing can be allocated to different program modules according to needs, that is, the internal structure of the terminal is divided into different program modules to complete all or part of the above-described processing. In addition, the above device provided in the above embodiment and the above method embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0096] To implement the method of the embodiments of the present invention, the embodiments of the present invention also provide a computer program product. The computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps of the above method.

[0097] Based on the hardware implementation of the above program module, and to implement the method of the embodiments of the present invention, the embodiments of the present invention also provide an electronic device (computer device). Specifically, in one embodiment, the computer device may be a terminal, and its internal structure diagram may be as Figure 5As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, it implements the method of any one of the above embodiments. The display screen A04 of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device A05 of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0098] Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0099] The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the method of any one of the above embodiments.

[0100] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0102] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0104] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0105] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0106] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0107] It can be understood that the memory in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read-Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as a static random access memory (SRAM, Static Random Access Memory), a synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), a dynamic random access memory (DRAM, Dynamic Random Access Memory), a synchronous dynamic random access memory (SDRAM, Synchronous Dynamic Random Access Memory), a double data rate synchronous dynamic random access memory (DDR SDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), an enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), a sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), a direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0108] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising the element.

[0109] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for calculating formation dip angle, characterized in that, The method includes: Obtaining the big data of logging while drilling and the corresponding geological judgment results of previous drillings; Based on the big data of logging while drilling and the corresponding geological judgment results, comparing the logging data of the well to be calculated with the big data of logging while drilling to determine the optimal formation dip calculation data points in the logging data of the well to be calculated; Calculating the formation dip of the logging data of the well to be calculated according to the optimal formation dip calculation data points.

2. The method according to claim 1, wherein The step of, based on the big data of logging while drilling and the corresponding geological judgment results, comparing the logging data of the well to be calculated with the big data of logging while drilling to determine the optimal formation dip calculation data points in the logging data of the well to be calculated includes: Annotating the big data of logging while drilling according to the geological judgment results corresponding to the big data of logging while drilling, and annotating the optimal formation dip calculation data points of the big data of logging while drilling; Performing a maximum similarity attribute comparison between the logging data of the well to be calculated and the annotated big data of logging while drilling, and selecting the position with the largest correlation coefficient of the logging data of the well to be calculated as the optimal formation dip calculation data points in the logging data of the well to be calculated.

3. The method according to claim 2, characterized in that, Before annotating the big data of logging while drilling according to the geological judgment results corresponding to the big data of logging while drilling, the method further includes: Converting the big data of logging while drilling into a standard digital format, and cleaning and purifying the incomplete data, error data, and redundant data in the big data of logging while drilling.

4. The method according to claim 1, characterized in that, The step of calculating the formation dip of the logging data of the well to be calculated according to the optimal formation dip calculation data points includes: Determining the vertical depth and sounding data of the logging data of the well to be calculated according to the optimal formation dip calculation data points; Based on the vertical depth and the sounding data, calculating the horizontal distance difference and the height difference of the logging data of the well to be calculated; Calculating the formation dip of the logging data of the well to be calculated according to the horizontal distance difference and the height difference.

5. The method according to claim 4, wherein The step of calculating the formation dip of the logging data of the well to be calculated according to the horizontal distance difference and the height difference includes: According to the horizontal distance difference and the height difference, calculating the formation dip of the logging data of the well to be calculated by using the following calculation formula (1): Where, α represents the formation dip, Δh1 represents the height difference, and Δd1 represents the horizontal distance difference.

6. The method according to claim 1, characterized in that After calculating the formation dip of the logging data of the well to be calculated according to the optimal formation dip calculation data points, the method further includes: Determining the target position of the well to be calculated according to the formation dip of the logging data of the well to be calculated.

7. The method according to claim 6, wherein The step of determining the target position of the well to be calculated according to the formation dip of the logging data of the well to be calculated includes: According to the formation dip of the logging data of the well to be calculated, determining the target position of the well to be calculated by using the following calculation formula (2) Δz = Δx / cos(α) Position: z A = z C + (Δz + Δh) Calculation formula (2) Among them, x A , y A and z A are the position coordinates of the target point, x C , y C and z C are the position coordinates of the optimal formation dip calculation data points, Δx and Δz are the distance coordinates between the target point and the optimal formation dip calculation data points, and Δh is the formation vertical thickness.

8. A formation dip angle calculation device, characterized in that, The formation dip calculation device includes: An acquisition module for acquiring the big data of logging while drilling and the corresponding geological judgment results of previous drillings; A determination module, configured to compare the well logging data to be calculated with the big data of logging-while-drilling (LWD) according to the big data of LWD and the corresponding geological judgment results, and determine the optimal formation dip calculation data points in the well logging data to be calculated; A calculation module, configured to calculate the formation dip of the well logging data to be calculated according to the optimal formation dip calculation data points.

9. An electronic device, characterized in that, Comprising: A sensor, a processor, and a memory for storing a computer program that can run on the processor; wherein, When the processor is used to run the computer program, it executes the steps of the method according to any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the method according to any one of claims 1 to 7.

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