A well logging curve standardization method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for standardizing well logging curves. The method includes: acquiring well logging curves and data from wells drilling in the target formation of the study area; identifying and determining the tight carbonate layers in the target formation based on the data and well logging curves; using multiple sets of tight layers from a single well as standard layers, determining the frequency distribution histogram of the standard layers, and determining the curve characteristic values ​​of the standard layers based on the frequency distribution histogram; calculating the average of the curve characteristic values ​​to obtain the standard value of the standard layer; and standardizing the well logging curves of all wells using a translation method based on the standard value to obtain the target well logging curve. This technical solution can perform fine standardization processing on formations with strong heterogeneity and no stable, continuous distribution of standard layers, obtaining high-quality well logging curves that meet the requirements of reservoir inversion, improving the accuracy of reservoir prediction using multi-well well logging curves, and is of great significance for reservoir evaluation of heterogeneous formations.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a method, apparatus, electronic device and storage medium for standardizing well logging curves. Background Technology

[0002] During the acquisition of well logging curves, well logging curves for a study block are often collected at different times by different personnel using different logging instruments. Therefore, systematic errors inevitably exist between well logging curves from different wells. Well logging curve standardization can eliminate systematic errors between well logging curves and increase the comparability of well logging curves from multiple wells. Therefore, well logging curve standardization is the foundation for conducting comprehensive reservoir logging evaluation and well-seismic joint inversion.

[0003] Currently, commonly used well logging curve standardization methods include the mean method, histogram method, and trend surface method. Different standardization methods are adopted depending on the specific geological conditions and data of the study area. The theoretical basis for well logging curve standardization is that strata with similar lithology in the same sedimentary environment exhibit similar well logging response characteristics. Therefore, regardless of the method used, selecting a stable standard layer with minimal vertical thickness variation and continuous horizontal distribution in the study area is crucial for well logging curve standardization.

[0004] Due to the complexity of terrestrial sedimentary environments, strata exhibit strong heterogeneity, making it difficult to find stable standard layers. Marine strata, influenced by diverse rock types, variable sedimentary environments, and various factors such as tectonics and diagenesis, exhibit even stronger heterogeneity in carbonate rocks, making the construction of standard layers even more challenging, thus affecting the evaluation and study of carbonate reservoirs. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for standardizing well logging curves. It can perform fine standardization processing on formations with strong heterogeneity and no stable and continuous distribution of standard layers to obtain high-quality well logging curves that meet the requirements of reservoir inversion, improve the accuracy of reservoir prediction using multi-well logging curves, and is of great significance for reservoir evaluation of heterogeneous formations.

[0006] According to one aspect of the present invention, a method for standardizing well logging curves is provided, the method comprising:

[0007] Obtain well logging curves and data from wells drilled in the target formation of the study area; wherein, the well logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data include core sampling, logging lithology, geological stratification, and oil testing data;

[0008] Based on the aforementioned data and well logging curves, the tight carbonate rock layers in the target formation are identified and determined.

[0009] Multiple tight layers in a single well are used as standard layers. The frequency distribution histogram of the standard layers is determined, and the curve characteristic values ​​of the standard layers are determined based on the frequency distribution histogram.

[0010] The standard value of the standard layer is obtained by averaging the characteristic values ​​of the curve.

[0011] Based on the standard values, the logging curves of all wells are standardized using the translation method to obtain the target logging curves.

[0012] According to another aspect of the present invention, a logging curve standardization device is provided, the device comprising:

[0013] The logging curve and data acquisition module is used to acquire logging curves and data from wells drilled in the target formation of the study area; wherein, the logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data includes core sampling, logging lithology, geological stratification, and oil testing data;

[0014] The tight layer identification module is used to identify and determine the tight layer of carbonate rock in the target section based on the data and logging curves.

[0015] The curve feature value determination module is used to take multiple sets of tight layers in a single well as standard layers, determine the frequency distribution histogram of the standard layers, and determine the curve feature values ​​of the standard layers based on the frequency distribution histogram.

[0016] The standard value acquisition module is used to calculate the average value of the curve feature values ​​to obtain the standard value of the standard layer.

[0017] The logging curve standardization module is used to standardize the logging curves of all wells according to the standard values ​​using the translation method to obtain the target logging curve.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a well logging curve standardization method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement a well logging curve standardization method according to any embodiment of the present invention.

[0023] The technical solution of this invention involves acquiring well logging curves and data from wells drilling in the target formation of the study area. Based on the data and well logging curves, the tight carbonate layers in the target formation are identified, and the tight layers are determined. Then, multiple sets of tight layers from a single well are used as standard layers. A frequency distribution histogram of the standard layers is determined, and the curve characteristic values ​​of the standard layers are determined based on the frequency distribution histogram. The average value of the curve characteristic values ​​is calculated to obtain the standard value of the standard layer. Based on the standard value, the well logging curves of all wells are standardized using a translation method to obtain the target well logging curve. This technical solution can perform fine standardization processing on formations with strong heterogeneity and no stable, continuous distribution of standard layers, obtaining high-quality well logging curves that meet the requirements of reservoir inversion. This improves the accuracy of reservoir prediction using multi-well well logging curves and is of great significance for reservoir evaluation of heterogeneous formations.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a well logging curve standardization method provided in Embodiment 1 of the present invention;

[0027] Figure 2 This is a schematic diagram of the identification of a single-well carbonate rock tight layer provided in Embodiment 1 of this application;

[0028] Figure 3 This is a diagram showing the intersection of acoustic transit time and compensated neutrons in the reservoir and tight layer before the well logging curve standardization provided in Embodiment 1 of this application;

[0029] Figure 4 This is a diagram showing the intersection of acoustic transit time and compensated neutrons in the reservoir and tight layer after standardization of the logging curves provided in Embodiment 1 of this application;

[0030] Figure 5 This is a schematic diagram of the structure of a well logging curve standardization device provided in Embodiment 2 of the present invention;

[0031] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing a well logging curve standardization method according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a well logging curve standardization method according to Embodiment 1 of the present invention. This embodiment is applicable to the standardization of well logging curves. The method can be executed by a well logging curve standardization device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0036] S110. Obtain logging curves and data from wells drilled in the target formation of the study area; wherein, the logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data include core sampling, logging lithology, geological stratification, and oil testing data.

[0037] In this scheme, the target stratum can refer to the homogeneous carbonate rock strata in the study area.

[0038] Among them, sonic time-of-flight logging is a logging method developed based on the acoustic physical properties of rocks, measuring the acoustic velocity of the formation; compensated neutron logging is a neutron logging method that overcomes the influence of irregular wellbore changes after pattern correction based on neutron logging. It simultaneously records two neutron curves with different source spacings, used to determine porosity in open-hole or cased wells; because density logging has a limited detection depth, generally confined to the flushed zone, the mud cake and other media between the instrument and the wellbore have a significant impact on the logging results and must be corrected. Therefore, density logging often uses dual detector devices with long and short source spacings to correct for the influence of mud cake and other media. This dual-source-spacing density logging is also called compensated density logging.

[0039] In this embodiment, coring involves using a logging cable to lower a coring device into the well, using explosives to drive the coring device into the well wall, and removing small pieces of rock to understand the properties of the rock and the fluids within it; logging involves interpreting and relocating lithology based on comprehensive on-site geological data, on-site logging data, and comprehensive analysis and testing data to determine the oil, gas, and water production; geological stratification refers to dividing the rock strata in a stratigraphic profile of a region; and oil testing is mainly aimed at testing whether the fluid in a certain stratum is oil or water.

[0040] In this scheme, well logging curves and data of the target formation in the study area can be obtained through exploration drilling, namely, the sonic transit time, compensated neutron and compensated density well logging curves of the target formation in the study area, as well as core sampling, logging lithology, geological stratification, and oil testing data.

[0041] S120. Identify the tight carbonate rock layer in the target section based on the data and logging curves, and determine the tight layer.

[0042] In this embodiment, the spontaneous potential logging curve of a large section of mudstone layer is generally used as the mudstone baseline, and well sections deviating from the mudstone baseline can be considered permeable rock layers. Formations with very poor permeability are often called tight layers, and their spontaneous potential logging curves are close to the mudstone baseline or have very small amplitude anomalies.

[0043] In this approach, tight layers in carbonate formations can be identified by combining well test data and lithological data with three-porosity logging curves. Specifically, tight layers in carbonate formations can be identified using sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves.

[0044] Optionally, based on the data and well logging curves, the tight carbonate rock layers in the target formation are identified, and the tight layers are determined, including:

[0045] Based on the aforementioned data and logging curves, determine the tight layer discrimination value;

[0046] Based on the dense layer discrimination value, the dense layer of carbonate rock in the target section is identified and the dense layer is determined.

[0047] In this scheme, two porosity logging curves that are most sensitive to reservoir properties can be selected, such as sonic transit time and compensated neutron curve. That is, a tight layer discrimination formula is established by using sonic transit time and compensated neutron curve to identify the tight layer in the target section of each well.

[0048] Specifically, the density discrimination value of the dense layer can be determined by the established density discrimination formula, and the density of the carbonate rock in the target section can be identified based on the density discrimination value.

[0049] Optionally, identifying and determining the tight layers of carbonate rocks in the target section based on the tight layer discrimination value includes:

[0050] When the density layer discrimination value is less than the preset segment identification threshold, the target segment is identified as a density layer.

[0051] The segment identification threshold can be set according to the requirements for dense layer identification. For example, the segment identification threshold can be set to -20.

[0052] In this scheme, two porosity logging curves that are most sensitive to reservoir properties can be selected to construct a tight layer discrimination quantity. The tight layer discrimination quantity is then compared with the layer identification threshold to determine whether the target layer is a tight layer.

[0053] By identifying the dense layer, a standard layer can be constructed based on the dense layer, thereby standardizing the logging curves based on the standard layer.

[0054] Optionally, based on the data and logging curves, a tight layer discrimination factor is determined, including:

[0055] The dense layer discrimination factor is determined as follows;

[0056] A = CNLp - ACp;

[0057] Where A represents the dense layer discrimination quantity, CNLp represents the normalized curve value of the compensated neutron logging curve, and ACp represents the inverse normalized curve value of the sonic transit time logging curve.

[0058] In this scheme, the acoustic time difference curve is inversely normalized, that is, the maximum scale is normalized to 0 and the minimum scale is normalized to 100. Specifically, the acoustic time difference curve is inversely normalized using the following formula;

[0059] AC p =(AC max -AC) / (AC max -AC min )×100;

[0060] Among them, AC p The AC value is the inverse normalized value of the acoustic time difference curve, dimensionless; max The maximum value of the acoustic transit time curve for the target layer segment, in μs·m. -1 AC min The minimum value of the acoustic transit time curve for the target layer segment, in μs·m. -1 AC represents the logging value of the sonic transit time curve of the target formation, in μs·m. -1 .

[0061] In this embodiment, the normalization process for the compensated neutron curve involves normalizing the maximum scale to 100 and the minimum scale to 0. Specifically, the compensated neutron curve is normalized using the following formula;

[0062] CNL p =(CNL-CNL) min ) / (CNL max -CNL min )×100;

[0063] Among them, CNL p To compensate for the normalized neutron curve values, dimensionless; CNL max The maximum value of the compensation neutron curve for the target segment, in %; CNL min is the minimum value of the compensated neutron curve for the target layer, in %; CNL is the logging value of the compensated neutron curve for the target layer, in %.

[0064] Furthermore, a formula for identifying tight layers in each well target section is established: A = CNLp - ACp, where A < -20 indicates a tight layer.

[0065] By identifying the dense layer, a standard layer can be constructed based on the dense layer, thereby standardizing the logging curves based on the standard layer.

[0066] In this embodiment, Figure 2 This is a schematic diagram of the identification of tight carbonate rock layers in a single well, as provided in Embodiment 1 of this application. Figure 2 As shown, the dense layer discrimination formula was used to identify the dense layer of carbonate rock in the target section. The identification result (A) is consistent with the qualitative identification result (AC-CNL curve overlap method).

[0067] S130. Take multiple sets of tight layers in a single well as standard layers, determine the frequency distribution histogram of the standard layers, and determine the curve characteristic values ​​of the standard layers based on the frequency distribution histogram.

[0068] In this scheme, multiple tight layers in a single well can be used as a standard layer. Frequency distribution histograms are plotted for the standard layers of each well, and the curve characteristic values ​​of the standard layers are determined based on the frequency distribution histograms.

[0069] Optionally, determining the curve characteristic values ​​of the standard layer based on the frequency distribution histogram includes:

[0070] The main peak value of the frequency distribution histogram is used as the curve feature value of the standard layer.

[0071] In this embodiment, multiple tight layers in a single well can be used as a standard layer. A frequency distribution histogram is plotted for the standard layers of each well, and the main peak value of the frequency distribution histogram is used as the curve characteristic value of the standard layer.

[0072] By determining the characteristic values ​​of the curve, standard values ​​can be constructed based on these characteristic values. This allows for fine standardization of formations with strong heterogeneity and no stable, continuous distribution of standard layers, resulting in high-quality logging curves that meet the requirements of reservoir inversion. This improves the accuracy of reservoir prediction using multi-well logging curves and is of great significance for reservoir evaluation of heterogeneous formations.

[0073] S140. Calculate the average value of the curve feature values ​​to obtain the standard value of the standard layer.

[0074] Specifically, wells with complete, reliable logging data and oil testing data are selected, and the arithmetic mean of the characteristic values ​​of their standard layers is calculated. This average value is then used as the standard value for the critical well's standard layer. Selecting the average of multiple wells with high logging quality as a critical well avoids situations where selecting a single well as the critical well might result in an excessively high or low logging curve scale that does not match the actual formation logging values.

[0075] S150. Based on the standard value, the logging curves of all wells are standardized using the translation method to obtain the target logging curve.

[0076] Translation refers to moving all points on a figure along a straight line within the same plane by the same distance.

[0077] In this scheme, the standard value is used as a benchmark, and the translation method is used to standardize the logging curves of all wells.

[0078] Optionally, based on the aforementioned standard value, the logging curves of all wells are standardized using a translation method to obtain the target logging curve, including:

[0079] Subtracting the standard value from the curve characteristic value yields the correction amount for the standard layer;

[0080] The logging curves of all wells are standardized based on the correction amount to obtain the target logging curve.

[0081] In this scheme, the standard value can be subtracted from the curve characteristic value of each well's standard layer to obtain the correction amount for the standard layer.

[0082] Furthermore, the logging curves of all wells are corrected based on the correction amount, thereby standardizing the logging curves and obtaining the standardized target logging curve.

[0083] For formations with strong heterogeneity and no stable and continuous standard layer distribution, fine standardization processing can be performed to obtain high-quality logging curves that meet the requirements of reservoir inversion, improve the accuracy of reservoir prediction using multi-well logging curves, and is of great significance for reservoir evaluation of heterogeneous formations.

[0084] Optionally, after obtaining the target logging curve, the method further includes:

[0085] Construct target logging curves and intersection plots of logging curves for reservoirs and tight layers, and compare reservoirs and tight layers based on the intersection plots.

[0086] In this plan, Figure 3 This is a diagram showing the intersection of acoustic transit time and compensated neutrons in the reservoir and tight layer before the well logging curve standardization provided in Embodiment 1 of this application; Figure 4 This is a diagram showing the intersection of acoustic transit time and compensated neutrons in the reservoir and tight layer after standardization of the logging curves provided in Embodiment 1 of this application. Figure 3 and Figure 4 As shown, cross-plots of well logging curves before and after standardization of reservoir and tight layer were created to compare the differentiation effects of well logging curves before and after standardization on reservoir and tight layer.

[0087] The technical solution of this invention involves acquiring well logging curves and data from wells drilling in the target formation of the study area. Based on the data and well logging curves, the tight carbonate layers in the target formation are identified, and the tight layers are determined. Then, multiple sets of tight layers from a single well are used as standard layers. A frequency distribution histogram of the standard layers is determined, and the curve characteristic values ​​of the standard layers are determined based on the frequency distribution histogram. The average value of the curve characteristic values ​​is calculated to obtain the standard value of the standard layer. Based on the standard value, the well logging curves of all wells are standardized using a translation method to obtain the target well logging curve. By implementing this technical solution, fine standardization processing can be performed on formations with strong heterogeneity and no stable, continuous distribution of standard layers, resulting in high-quality well logging curves that meet the requirements of reservoir inversion. This improves the accuracy of reservoir prediction using multi-well well logging curves and is of great significance for reservoir evaluation in heterogeneous formations.

[0088] Example 2

[0089] Figure 5This is a schematic diagram of a well logging curve standardization device provided in Embodiment 2 of the present invention. Figure 5 As shown, the device includes:

[0090] The logging curve and data acquisition module 510 is used to acquire logging curves and data of wells drilled in the target formation of the study area; wherein, the logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data includes core sampling, logging lithology, geological stratification, and oil testing data;

[0091] The tight layer identification module 520 is used to identify the tight layer of carbonate rock in the target section based on the data and logging curves, and to determine the tight layer.

[0092] The curve feature value determination module 530 is used to take multiple sets of tight layers in a single well as standard layers, determine the frequency distribution histogram of the standard layers, and determine the curve feature values ​​of the standard layers based on the frequency distribution histogram.

[0093] The standard value acquisition module 540 is used to calculate the average value of the curve feature values ​​to obtain the standard value of the standard layer.

[0094] The logging curve standardization module 550 is used to standardize the logging curves of all wells according to the standard value using the translation method to obtain the target logging curve.

[0095] Optionally, the dense layer identification module 520 includes:

[0096] The tight layer discrimination unit is used to determine the tight layer discrimination quantity based on the data and logging curves.

[0097] The dense layer determination unit is used to identify and determine the dense layer of carbonate rock in the target section based on the dense layer discrimination value.

[0098] Optional, the dense layer discrimination unit is specifically used for:

[0099] When the density layer discrimination value is less than the preset segment identification threshold, the target segment is identified as a density layer.

[0100] Optional, dense layer defining unit, specifically used for:

[0101] The dense layer discrimination factor is determined as follows;

[0102] A = CNLp - ACp;

[0103] Where A represents the dense layer discrimination quantity, CNLp represents the normalized curve value of the compensated neutron logging curve, and ACp represents the inverse normalized curve value of the sonic transit time logging curve.

[0104] Optionally, the curve characteristic value determination module 530 is specifically used for:

[0105] The main peak value of the frequency distribution histogram is used as the curve feature value of the standard layer.

[0106] Optional, the well logging curve standardization module 550, specifically used for:

[0107] Subtracting the standard value from the curve characteristic value yields the correction amount for the standard layer;

[0108] The logging curves of all wells are standardized based on the correction amount to obtain the target logging curve.

[0109] Optionally, the device further includes:

[0110] The comparison module is used to construct target logging curves and intersection plots of logging curves for reservoirs and tight layers, and to compare reservoirs and tight layers based on the intersection plots.

[0111] The logging curve standardization device provided in this embodiment of the invention can execute a logging curve standardization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0112] Example 3

[0113] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0114] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0115] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a well logging curve normalization method.

[0117] In some embodiments, a well logging curve standardization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the well logging curve standardization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a well logging curve standardization method by any other suitable means (e.g., by means of firmware).

[0118] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0121] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0122] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0123] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0124] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for standardizing well logging curves, characterized in that, include: Obtain well logging curves and data from wells drilled in the target formation of the study area; wherein, the well logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data include core sampling, logging lithology, geological stratification, and oil testing data; Based on the aforementioned data and well logging curves, the tight carbonate rock layers in the target formation are identified and determined. Multiple tight layers in a single well are used as standard layers. The frequency distribution histogram of the standard layers is determined, and the curve characteristic values ​​of the standard layers are determined based on the frequency distribution histogram. The standard value of the standard layer is obtained by averaging the characteristic values ​​of the curve. Based on the standard values, the logging curves of all wells are standardized using the translation method to obtain the target logging curves; The tight layer is determined based on the inverse normalized curve value of the sonic transit time logging curve and the normalized curve value of the compensated neutron logging curve; The acoustic time difference curve is denormalized using the following formula; ； in, The value of the acoustic time difference curve after inverse normalization is dimensionless. The maximum value of the acoustic transit time curve for the target layer, in units. ; The minimum value of the acoustic transit time curve for the target layer segment, in units. ; The logging value of the sonic transit time curve of the target formation, in units. ; The determination of the curve feature values ​​of the standard layer based on the frequency distribution histogram includes: The main peak value of the frequency distribution histogram is used as the curve feature value of the standard layer; The standardization process, based on the standard value, involves using a translation method to standardize the logging curves of all wells to obtain the target logging curve, including: Subtracting the standard value from the curve characteristic value yields the correction amount for the standard layer; Based on the correction amount, the logging curves of all wells are standardized to obtain the target logging curve; After obtaining the target logging curve, the method further includes: Construct target logging curves and logging curve intersection diagrams for reservoirs and tight layers, and compare reservoirs and tight layers based on the intersection diagrams; Based on the aforementioned data and well logging curves, the tight layers of carbonate rocks in the target formation are identified, and the tight layers are determined, including: Based on the aforementioned data and logging curves, determine the tight layer discrimination value; Based on the dense layer discrimination value, the dense layer of carbonate rock in the target section is identified and the dense layer is determined; Based on the tight layer discrimination value, the tight layer of carbonate rock in the target section is identified, and the tight layer is determined, including: When the density layer discrimination value is less than the preset segment identification threshold, the target segment is identified as a density layer; Based on the aforementioned data and well logging curves, determine the tight layer discrimination parameters, including: The dense layer discrimination factor is determined as follows; ； in, Indicates the discriminant quantity of dense layers. This represents the normalized value of the compensated neutron logging curve. This represents the curve value after inverse normalization of the sonic transit time logging curve.

2. A logging curve standardization device, used to execute the logging curve standardization method as described in claim 1, characterized in that, include: The logging curve and data acquisition module is used to acquire logging curves and data from wells drilled in the target formation of the study area; wherein, the logging curves include sonic transit time logging curves, compensated neutron logging curves, and compensated density logging curves; the data includes core sampling, logging lithology, geological stratification, and oil testing data; The tight layer identification module is used to identify and determine the tight layer of carbonate rock in the target section based on the data and logging curves. The curve feature value determination module is used to take multiple sets of tight layers in a single well as standard layers, determine the frequency distribution histogram of the standard layers, and determine the curve feature values ​​of the standard layers based on the frequency distribution histogram. The standard value acquisition module is used to calculate the average value of the curve feature values ​​to obtain the standard value of the standard layer. The logging curve standardization module is used to standardize the logging curves of all wells according to the standard values ​​using the translation method to obtain the target logging curve. The tight layer is determined based on the inverse normalized curve value of the sonic transit time logging curve and the normalized curve value of the compensated neutron logging curve; The curve feature value determination module is specifically used for: The main peak value of the frequency distribution histogram is used as the curve feature value of the standard layer; The well logging curve standardization module is specifically used for: Subtracting the standard value from the curve characteristic value yields the correction amount for the standard layer; Based on the correction amount, the logging curves of all wells are standardized to obtain the target logging curve; The device further includes: The comparison module is used to construct target logging curves and intersection plots of logging curves for reservoirs and tight layers, and to compare reservoirs and tight layers based on the intersection plots.

3. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the well logging curve standardization method according to claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the well logging curve standardization method of claim 1.

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