Method and device for predicting organic carbon content of reservoir and electronic equipment

Abstract

The invention discloses a method and device for predicting the organic carbon content of a reservoir and electronic equipment, and the method comprises the steps: carrying out the strengthening operation of logging information of a sample reservoir through a neural network model, and determining an elastic parameter from the logging information after the strengthening operation; according to the sample reservoir organic carbon content and the elastic parameter of the logging information, establishing a mapping relation between the reservoir organic carbon content and the elastic parameter; and determining a target elastic parameter of the target reservoir according to logging information of the target reservoir, and predicting the organic carbon content of the target reservoir according to the mapping relation and the target elastic parameter. According to the method, by establishing the mapping relation between the organic carbon content and the elastic parameter and accurately obtaining the target elastic parameter of the target reservoir, the accuracy of predicting the organic carbon content of the reservoir can be improved.

Description

Technical Field

[0001] The invention relates to the field of petroleum reservoir development, and in particular to a method, a device, an electronic device and a medium for predicting the organic carbon content of a reservoir. Background Art

[0002] Deep shale gas resources have great potential. Among them, the exploitable area of ​​high-quality shale in the first-class area of ​​southern Sichuan accounts for a large proportion and is the core area of ​​sustained and rapid growth in natural gas production. However, the thickness of high-quality shale in southern Sichuan is relatively thin and the ideal box of shale gas horizontal wells is 10-15 meters away from the bottom of Longma Creek. If the organic carbon content can be accurately predicted, reliable data support can be provided for the production prediction of deep high-quality shale. However, the seismic response of high-quality shale is easily submerged in the strong amplitude reflection, which brings challenges to the prediction of organic carbon content.

[0003] At present, in order to successfully obtain the value of organic carbon content, the post-stack seismic logging inversion method or the well-controlled seismic pre-stack inversion method can be used. Both methods can determine the organic carbon content value by obtaining intermediate quantities. However, if only the post-stack seismic logging inversion method is used, the method will be more modeled and the advantages of logging data will not be highlighted; if only the well-controlled seismic pre-stack inversion method is used, the prediction results will be low in accuracy due to the influence of resolution. Summary of the invention

[0004] The present invention provides a method, device, electronic equipment and medium for predicting the organic carbon content of a reservoir, so as to solve the problem of low accuracy in predicting the organic carbon content of a reservoir.

[0005] According to one aspect of the present invention, a method for predicting reservoir organic carbon content is provided, the method comprising:

[0006] Performing an enhancement operation on the well logging data of the sample reservoir through a neural network model and determining elastic parameters from the well logging data after the enhancement operation;

[0007] Performing an enhancement operation on the well logging data of the sample reservoir through a neural network model and determining elastic parameters from the well logging data after the enhancement operation;

[0008] According to the reservoir organic carbon content and elastic parameters of the sample logging data, a mapping relationship between the reservoir organic carbon content and the elastic parameters is established;

[0009] The target elasticity parameter of the target reservoir is determined according to the well logging data of the target reservoir, and the organic carbon content of the target reservoir is predicted according to the mapping relationship and the target elasticity parameter.

[0010] According to another aspect of the present invention, there is provided a device for predicting the organic carbon content of a reservoir, the device comprising:

[0011] An elastic parameter determination module is used to perform an enhancement operation on the well logging data of the sample reservoir through a neural network model, and determine the elastic parameters from the well logging data after the enhancement operation;

[0012] A mapping relationship establishment module is used to establish a mapping relationship between reservoir organic carbon content and elastic parameters based on the sample reservoir organic carbon content and elastic parameters of the logging data;

[0013] The organic carbon content prediction module is used to determine the target elasticity parameter of the target reservoir according to the well logging data of the target reservoir, and predict the organic carbon content of the target reservoir according to the mapping relationship and the target elasticity parameter.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting reservoir organic carbon content as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting reservoir organic carbon content as described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention performs an enhancement operation on the logging data of the sample reservoir through a neural network model, so as to facilitate the extraction of elastic parameters of the sample reservoir from the logging data after the enhancement operation, determine the mapping relationship between the organic carbon content of the reservoir and the elastic parameter according to the organic carbon content and elastic parameter value of the sample reservoir in the logging data, obtain the target elastic parameter of the target reservoir, and determine the organic carbon content of the target reservoir according to the target elastic parameter and the mapping relationship. This method can improve the accuracy of predicting the organic carbon content of the reservoir by establishing a mapping relationship between the organic carbon content and the elastic parameter and accurately obtaining the target elastic parameter of the target reservoir.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for predicting reservoir organic carbon content provided according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a neural network structure diagram of a method for predicting reservoir organic carbon content provided by Example 1 of the present invention;

[0024] Figure 3 is a flow chart of a method for predicting reservoir organic carbon content provided according to Embodiment 2 of the present invention;

[0025] Figure 4 It is a seismic profile diagram before the neural network processes the logging data according to a method for predicting the organic carbon content of a reservoir provided by an embodiment of the present invention;

[0026] Figure 5 It is a seismic profile after the neural network processes the logging data according to a method for predicting the organic carbon content of a reservoir provided by an embodiment of the present invention;

[0027] Figure 6 is a mapping relationship diagram of a method for predicting reservoir organic carbon content provided by an embodiment of the present invention;

[0028] Figure 7 It is a cross-sectional diagram of a reservoir after post-stack high-resolution waveform inversion according to a method for predicting organic carbon content in a reservoir provided by an embodiment of the present invention;

[0029] Figure 8 It is a cross-sectional diagram of a reservoir after prestack inversion according to a method for predicting organic carbon content in a reservoir provided by an embodiment of the present invention;

[0030] Fig. 9 is a target reservoir organic carbon content prediction diagram of a method for predicting reservoir organic carbon content provided by an embodiment of the present invention;

[0031] Fig.10 is a schematic structural diagram of a device for predicting reservoir organic carbon content provided according to Embodiment 3 of the present invention;

[0032] Fig.11 It is a schematic diagram of the structure of an electronic device for implementing the method for predicting the organic carbon content of a reservoir according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] Figure 1 A flowchart of a method for predicting reservoir organic carbon content is provided for the first embodiment of the present invention. This embodiment is applicable to the case of predicting reservoir organic carbon content. The method can be executed by a device for predicting reservoir organic carbon content. The device for predicting reservoir organic carbon content can be implemented in the form of hardware and / or software. The device for predicting reservoir organic carbon content can be configured in any electronic device with network communication function. Figure 1 As shown, the method may include:

[0037] S110, performing an enhancement operation on the logging data of the sample reservoir through a neural network model, and determining elastic parameters from the logging data after the enhancement operation.

[0038] The elastic parameter may be a parameter having a corresponding relationship with the organic carbon content of the reservoir.

[0039] Specifically, the convolutional neural network model can enhance the proportion of sample reservoir data in the well logging data, thereby strengthening the data related to the predicted reservoir organic carbon content in the well logging data of the sample reservoir, and making the seismic characterization inside the sample reservoir clearer.

[0040] Optionally, the strengthening operation includes reducing the reflection intensity of the strong axis and enhancing the reflection intensity of the weak axis.

[0041] The strong axis may be the axis in the direction of the larger cross-sectional resistance moment in the logging data, and the weak axis may be the axis in the opposite direction to the strong axis in the logging data.

[0042] For example, since the thickness of high-quality shale reservoir is relatively thin, the difference in wave impedance between shale and underlying limestone forms strong reflection, which makes the high-quality shale logging data blurred due to the strong amplitude reflection. Therefore, the neural network model is used to reduce the reflection intensity of the strong axis and enhance the weak reflection intensity of the weak axis to enhance the logging data of the sample reservoir. The neural network model can be found in Figure 2 .

[0043] S120. Establish a mapping relationship between the reservoir organic carbon content and the elastic parameters based on the sample reservoir organic carbon content and the elastic parameters of the logging data.

[0044] Specifically, the data of sample reservoir organic carbon content and elastic parameters from the logging data are used to draw a scatter plot of organic carbon content and elastic parameters, find the changing pattern of sample reservoir organic carbon content and elastic parameters, and then determine the mapping relationship between reservoir organic carbon content and elastic parameters.

[0045] Optionally, the elastic parameters include at least longitudinal wave impedance and / or transverse wave impedance.

[0046] Among them, the longitudinal wave impedance can be a parameter determined according to the product of the longitudinal wave velocity and the longitudinal wave density, and the shear wave impedance can be a parameter determined according to the product of the shear wave velocity and the shear wave density.

[0047] For example, a scatter plot is drawn based on the organic carbon content and elastic parameters of the sample reservoir of the logging data, where the elastic parameters are the longitudinal wave impedance or the shear wave impedance, such as Figure 2 As shown, when the value of the longitudinal wave impedance or the shear wave impedance increases, the organic carbon content of the sample reservoir will decrease. Therefore, there is an inverse relationship between the organic carbon content of the sample reservoir and the elastic parameters. An inverse proportional function of the organic carbon content of the sample reservoir and the elastic parameters can be established.

[0048] S130, determining a target elastic parameter of the target reservoir according to the well logging data of the target reservoir, and predicting the organic carbon content of the target reservoir according to the mapping relationship and the target elastic parameter.

[0049] Specifically, the target reservoir whose organic carbon content is to be predicted is determined, the target elastic parameters of the target reservoir are determined based on the logging data of the target reservoir through pre-stack inversion and / or post-stack inversion, the target elastic parameters are substituted into the mapping relationship between the organic carbon content and the elastic parameters, and then the organic carbon content of the target reservoir is predicted.

[0050] The embodiment of the present application proposes a method for predicting the organic carbon content of a reservoir, which performs an enhancement operation on the logging data of a sample reservoir through a neural network model, so as to facilitate the extraction of elastic parameters of the sample reservoir from the logging data after the enhancement operation, and determines the mapping relationship between the organic carbon content of the reservoir and the elastic parameter according to the organic carbon content and elastic parameter value of the sample reservoir in the logging data, obtains the target elastic parameter of the target reservoir, and determines the organic carbon content of the target reservoir according to the target elastic parameter and the mapping relationship. This method can improve the accuracy of predicting the organic carbon content of a reservoir by establishing a mapping relationship between the organic carbon content and the elastic parameter and accurately obtaining the target elastic parameter of the target reservoir.

[0051] Embodiment 2

[0052] Figure 3 This is a flow chart of a method for predicting the organic carbon content of a reservoir provided in Example 2 of the present invention. This embodiment describes in detail how to predict the organic carbon content of a target reservoir based on the above embodiments. Figure 3 As shown, the method may include:

[0053] S210, performing an enhancement operation on the logging data of the sample reservoir through a neural network model, and determining elastic parameters from the logging data after the enhancement operation.

[0054] Specifically, the well logging data of the sample reservoir is enhanced by reducing the reflection intensity of the strong axis and enhancing the weak reflection intensity of the weak axis through the neural network model, and the elastic parameters of the sample reservoir are determined from the well logging data after the enhancement operation.

[0055] As an optional but not limiting implementation, determining elastic parameters from logging data after the enhancement operation may include steps A1-A2:

[0056] Step A1, determining intermediate parameters from the logging data after the enhancement operation; wherein the intermediate parameters include elastic parameters and other parameters;

[0057] Specifically, the logging data after the enhancement operation includes elastic parameters of the sample reservoir and intermediate parameters associated with the elastic parameters of the sample reservoir. The intermediate parameters are included in other parameters, and the intermediate parameters are extracted from the logging data after the enhancement operation.

[0058] Step A2: determining elastic parameters according to the intermediate parameters.

[0059] Specifically, the elasticity parameter is determined according to the intermediate parameter extracted in step A1 and according to the mapping relationship between the intermediate parameter and the elasticity parameter.

[0060] As an optional but not limiting implementation, obtaining the elasticity parameter according to the intermediate parameter may include steps A11-A12:

[0061] Step A11, filtering out a target parameter associated with the elasticity parameter from other parameters;

[0062] Specifically, intermediate parameters are obtained according to logging data of the sample reservoir, and target parameters are screened from the intermediate parameters of the logging data of the sample reservoir, wherein the target parameters may be parameters of elastic parameters determined according to a mapping relationship between the target parameters and the elastic parameters.

[0063] Optionally, the target parameters include at least shear wave velocity, longitudinal wave velocity, shear wave density and longitudinal wave density.

[0064] Specifically, the target parameters include transverse and longitudinal wave velocities and transverse and longitudinal wave densities. The value of the transverse wave impedance is determined according to the product of the transverse wave velocity and the transverse wave density, and the value of the longitudinal wave impedance is determined according to the product of the longitudinal wave velocity and the longitudinal wave density.

[0065] Step A12: Based on the association between the target parameter and the elastic parameter, determine the elastic parameter according to the target parameter.

[0066] Specifically, according to the target parameters of the sample reservoir and the correlation between the target parameters and the elastic parameters, the elastic parameters corresponding to the target parameters of the sample reservoir are determined, and the elastic parameters can establish a mapping relationship between the elastic parameters and the organic content of the sample reservoir.

[0067] For example, before the neural network model is used to reduce the reflection intensity of the strong axis and enhance the weak reflection intensity of the weak axis, the seismic profile in the logging data is as follows: Figure 4 As shown in the figure, after the neural network model reduces the reflection intensity of the strong axis and enhances the weak reflection intensity of the weak axis, the seismic profile in the logging data is as follows: Figure 5 As shown, the characteristics of high-quality shale reservoirs are highlighted, and intermediate parameters of the sample reservoir can be obtained therefrom, wherein the intermediate parameters include elastic parameters and other parameters, and the elastic parameters are determined according to target parameters associated with the elastic parameters in other parameters and according to the mapping relationship between the target parameters and the elastic parameters.

[0068] S220. Establish a mapping relationship between the reservoir organic carbon content and the elastic parameters based on the sample reservoir organic carbon content and the elastic parameters of the logging data.

[0069] Specifically, based on the data of organic carbon content and elastic parameters of the logging data sample reservoir, a scatter plot of organic carbon content and elastic parameters is drawn, the effect of changes in elastic parameters on organic carbon content is observed, and a mapping relationship between elastic parameters and organic carbon content is established.

[0070] For example, a scatter plot of the organic carbon content and elastic parameters of the sample reservoir is drawn, such as Figure 6 As shown, the relationship between the organic carbon content and the elastic parameter is fitted into an inverse proportional mapping relationship according to the scatter plot, and the mapping relationship is determined to determine the organic carbon content of the target reservoir.

[0071] S230, determining a target elastic parameter of the target reservoir according to the well logging data of the target reservoir, and predicting the organic carbon content of the target reservoir according to the mapping relationship and the target elastic parameter.

[0072] Specifically, the value of the target elastic parameter is determined, and the organic carbon content of the target reservoir is predicted based on the mapping relationship between the elastic parameter and the organic carbon content determined in S220.

[0073] As an optional but not limiting implementation, determining the target elastic parameters of the target reservoir according to the well logging data of the target reservoir may include steps B1-B2:

[0074] Step B1: determine the time window of the target reservoir based on post-stack high-resolution waveform inversion.

[0075] Among them, post-stack high-resolution waveform inversion can be a tool to convert reflection interface type logging data into bottom layer logging data. This method can perform geological interpretation based on strata as units, thereby mining the lithology information and other information of the reservoir.

[0076] Specifically, the time window for extracting target reservoir information is determined based on post-stack high-resolution waveform inversion.

[0077] For example, Figure 7 As shown, according to the post-stack high-resolution waveform inversion, that is, making full use of the lateral changes of the seismic waveform to reflect the characteristics of the target reservoir, the high-frequency structural characteristics of the vertical lithology combination of the target reservoir can be analyzed based on this characteristic, and a time window for extracting the target reservoir information can be established.

[0078] Step B2: extracting target elastic parameters characterizing organic carbon content from the time window according to prestack inversion.

[0079] Among them, pre-stack inversion can be a method of obtaining target elastic information of the target reservoir according to the time window determined by post-stack high-resolution waveform inversion.

[0080] Specifically, step B1 determines the time window from which information of the target reservoir can be extracted. According to prestack inversion, the target elastic parameters of the target reservoir can be obtained from the time window, and the organic carbon content of the target reservoir is determined according to the target elastic parameters and the mapping relationship between the elastic parameters and the organic carbon content.

[0081] For example, Figure 8As shown, a parameter set is obtained based on the pre-stack inversion analysis of the target reservoir, and the organic carbon content of the target reservoir is extracted in the time window determined by the post-stack high-resolution waveform inversion.

[0082] Exemplarily, the Wufeng Formation-Longmaxi Formation I+II reservoir is set as the target reservoir, the target elastic parameter of the target reservoir is determined, and the target elastic parameter is substituted into the mapping relationship between the organic carbon content and the elastic parameter of the sample reservoir to determine the organic carbon content of the Wufeng Formation-Longmaxi Formation I+II target reservoir, such as Fig. 9 The predicted organic carbon content of the target reservoir is shown.

[0083] The embodiment of the present application proposes a method for predicting the organic carbon content of a reservoir, which performs an enhancement operation on the logging data of a sample reservoir through a neural network model, so as to facilitate the extraction of elastic parameters of the sample reservoir from the logging data after the enhancement operation, and determines the mapping relationship between the organic carbon content of the reservoir and the elastic parameters according to the organic carbon content and elastic parameter values ​​of the sample reservoir in the logging data, obtains the target elastic parameters of the target reservoir through post-stack high-resolution waveform inversion and pre-stack inversion, and determines the organic carbon content of the target reservoir according to the target elastic parameters and the mapping relationship. This method can improve the accuracy of predicting the organic carbon content of a reservoir by establishing a mapping relationship between the organic carbon content and the elastic parameters and accurately obtaining the target elastic parameters of the target reservoir.

[0084] Embodiment 3

[0085] Fig.10 This is a schematic diagram of the structure of a device for predicting the organic carbon content of a reservoir provided in the third embodiment of the present invention. The device can execute the method for predicting the organic carbon content of a reservoir provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. Fig.10 As shown, the device may include:

[0086] The elastic parameter determination module 310 is used to perform an enhancement operation on the logging data of the sample reservoir through a neural network model, and determine the elastic parameters from the logging data after the enhancement operation.

[0087] The mapping relationship establishing module 320 is used to establish a mapping relationship between the reservoir organic carbon content and the elastic parameters according to the sample reservoir organic carbon content and the elastic parameters of the logging data.

[0088] The organic carbon content prediction module 330 is used to determine the target elasticity parameter of the target reservoir according to the well logging data of the target reservoir, and predict the organic carbon content of the target reservoir according to the mapping relationship and the target elasticity parameter.

[0089] Furthermore, the strengthening operation includes reducing the reflection intensity of the strong axis and enhancing the reflection intensity of the weak axis.

[0090] Furthermore, the elastic parameter determination module 310 includes:

[0091] An intermediate parameter determination unit, used to determine intermediate parameters from the well logging data after the enhancement operation; wherein the intermediate parameters include elastic parameters and other parameters;

[0092] The elastic parameter determining unit is used to determine the elastic parameter according to the intermediate parameter.

[0093] Furthermore, the elastic parameters at least include longitudinal wave impedance and / or transverse wave impedance.

[0094] Furthermore, the elastic parameter determination unit is specifically used for:

[0095] Filter out a target parameter associated with the elasticity parameter from other parameters;

[0096] Based on the association relationship between the target parameter and the elastic parameter, the elastic parameter is determined according to the target parameter.

[0097] Furthermore, the target parameters include at least shear wave velocity, longitudinal wave velocity, shear wave density and longitudinal wave density.

[0098] Furthermore, the organic carbon content prediction module 330 includes:

[0099] The time window determination unit is used to determine the time window of the target reservoir according to the post-stack high-resolution waveform inversion.

[0100] The target elastic parameter determination unit is used to extract the target elastic parameter representing the organic carbon content from the time window according to prestack inversion.

[0101] The device for predicting reservoir organic carbon content provided in an embodiment of the present invention can execute the method for predicting reservoir organic carbon content provided in any of the above-mentioned embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the method for predicting reservoir organic carbon content. For detailed processes, please refer to the relevant operations of the method for predicting reservoir organic carbon content in the above-mentioned embodiments.

[0102] Embodiment 4

[0103] Fig.11A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0104] like Fig.11 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0106] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for predicting reservoir organic carbon content.

[0107] In some embodiments, the method for predicting reservoir organic carbon content may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting reservoir organic carbon content described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for predicting reservoir organic carbon content in any other appropriate manner (e.g., by means of firmware).

[0108] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0110] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0112] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0113] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0114] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0115] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting reservoir organic carbon content, characterized in that: include: Performing an enhancement operation on the well logging data of the sample reservoir through a neural network model and determining elastic parameters from the well logging data after the enhancement operation; According to the reservoir organic carbon content and elastic parameters of the sample logging data, a mapping relationship between the reservoir organic carbon content and the elastic parameters is established; The target elasticity parameter of the target reservoir is determined according to the well logging data of the target reservoir, and the organic carbon content of the target reservoir is predicted according to the mapping relationship and the target elasticity parameter.

2. The method according to claim 1, characterized in that: The strengthening operation includes reducing the reflection intensity of the strong axis and enhancing the reflection intensity of the weak axis.

3. The method according to claim 1, characterized in that Determination of elastic parameters from well log data after enhancement operations, including: Determining intermediate parameters from the logging data after the enhancement operation; wherein the intermediate parameters include elastic parameters and other parameters; An elasticity parameter is determined according to the intermediate parameter.

4. The method according to any one of claims 1 to 3, characterized in that: The elastic parameters include at least longitudinal wave impedance and / or shear wave impedance.

5. The method according to claim 3, characterized in that: Get elastic parameters based on intermediate parameters, including: Filter out a target parameter associated with the elasticity parameter from other parameters; Based on the association relationship between the target parameter and the elastic parameter, the elastic parameter is determined according to the target parameter.

6. The method according to claim 5, characterized in that The target parameters include at least shear wave velocity, longitudinal wave velocity, shear wave density and longitudinal wave density.

7. The method according to claim 1, characterized in that Determine the target elastic parameters of the target reservoir based on the well logging data of the target reservoir, including: Determine the time window of the target reservoir based on post-stack high-resolution waveform inversion; A target elastic parameter characterizing the organic carbon content is extracted from the time window according to prestack inversion.

8. A device for predicting the organic carbon content of a reservoir, characterized in that: include: An elastic parameter determination module is used to perform an enhancement operation on the well logging data of the sample reservoir through a neural network model, and determine the elastic parameters from the well logging data after the enhancement operation; A mapping relationship establishment module is used to establish a mapping relationship between reservoir organic carbon content and elastic parameters based on the sample reservoir organic carbon content and elastic parameters of the logging data; The organic carbon content prediction module is used to determine the target elasticity parameter of the target reservoir according to the well logging data of the target reservoir, and predict the organic carbon content of the target reservoir according to the mapping relationship and the target elasticity parameter.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for predicting reservoir organic carbon content according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting reservoir organic carbon content according to any one of claims 1 to 7 when executed.

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