Methods, apparatus, equipment, media, and computer programs for determining crack porosity

Abstract

This disclosure relates to the field of reservoir evaluation technology, and particularly to a method, apparatus, equipment, medium, and computer program for determining fracture porosity. The method includes: acquiring core logging data of a target area; determining the matrix porosity and fracture porosity at each logging depth based on logging data at each logging depth, and a matrix porosity assessment model and a fracture porosity assessment model; determining the total fracture porosity of the core at each logging depth based on the matrix porosity and fracture porosity of the core at each logging depth; and correcting the logging fracture porosity at each logging depth based on the total fracture porosity to obtain the corrected logging fracture porosity of the core at each logging depth. This disclosure can improve the scientific validity and accuracy of the determined fracture porosity.

Description

Technical Field

[0001] This disclosure relates to the field of reservoir evaluation technology, and in particular to a method, apparatus, device, medium and computer program for determining fracture porosity. Background Technology

[0002] Currently, more than half of the world's oil and gas production comes from natural fractured oil and gas reservoirs. In my country, fractured oil and gas reservoirs are also widely distributed and occupy a very important position in my country's oil and gas production. However, due to their low porosity, strong heterogeneity, and complex fracture distribution, the development of fractured oil and gas reservoirs has become a recognized challenge in the world's petroleum industry.

[0003] In related technologies, fracture porosity characteristics can be identified using post-stack seismic data. This involves extracting seismic attributes from the seismic data to qualitatively characterize the intensity of fracture development and to assess the state of fracture porosity.

[0004] However, the methods for assessing crack porosity in related technologies are mainly qualitative studies of crack porosity, resulting in low accuracy of the determined crack porosity assessment results. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, medium, and computer program for determining crack porosity, thereby improving the scientific validity and accuracy of the determined crack porosity.

[0006] In a first aspect, this disclosure provides a method for determining crack porosity, including:

[0007] Acquire core logging data for the target area, wherein the core logging data includes logging data at multiple logging depths in the core;

[0008] Based on the logging data at each logging depth and the matrix porosity assessment model, the matrix porosity of the core at each logging depth is assessed; and based on the logging data at each logging depth and the fracture porosity assessment model, the fracture porosity of the core at each logging depth is assessed.

[0009] Based on the matrix porosity and fracture porosity of the core at each logging depth, the total fracture porosity of the core at each logging depth is determined.

[0010] Based on the total fracture porosity at each logging depth, the logging fracture porosity at each logging depth is corrected to obtain the corrected logging fracture porosity of the core at each logging depth.

[0011] In some embodiments, the step of correcting the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth to obtain the corrected logging fracture porosity of the core at each logging depth includes:

[0012] Based on the total fracture porosity of the core at the multiple logging depths, and the logging fracture porosity of the core at the multiple logging depths, a regression model of the logging fracture porosity with respect to the total fracture porosity is established.

[0013] The total fracture porosity at each logging depth is input into the regression model to obtain the corrected logging fracture porosity of the core at each logging depth.

[0014] In some embodiments, evaluating the fracture porosity of the core sample at each logging depth based on logging data and a fracture porosity assessment model at each logging depth includes:

[0015] The lateral logging response data, fluid conductivity, and bedrock conductivity from the logging data at each logging depth are input into the fracture porosity evaluation model to obtain the fracture porosity of the core at each logging depth. The fracture porosity evaluation model is a mathematical model relating the lateral logging response to porosity, fluid conductivity, and bedrock conductivity.

[0016] In some embodiments, the fractures in the core sample within the target area include a network of fractures. The assessment of fracture porosity at each logging depth, based on logging data and a fracture porosity assessment model at each logging depth, includes:

[0017] The oil layer mud resistivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the oil layer fracture porosity prediction model of the network fracture to obtain the oil layer fracture porosity of the core at each logging depth.

[0018] The water layer mud resistivity, formation water conductivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the water layer fracture porosity prediction model of the network fracture to obtain the water layer fracture porosity of the core at each logging depth.

[0019] The oil layer fracture porosity and the water layer fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

[0020] In some embodiments, the fractures in the core sample within the target area comprise a single set of fractures. The assessment of fracture porosity at each logging depth, based on logging data and a fracture porosity assessment model, includes:

[0021] The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the vertical fracture porosity evaluation model to obtain the vertical fracture porosity of the core at each logging depth.

[0022] The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the low-angle fracture porosity evaluation model to obtain the low-angle fracture porosity of the core at each logging depth.

[0023] The vertical fracture porosity and the low-angle fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

[0024] In some embodiments, evaluating the matrix porosity of the core sample at each logging depth based on logging data and a matrix porosity evaluation model at each logging depth includes:

[0025] Based on the limestone and dolomite content in the logging data at each logging depth, as well as the sonic transit time of limestone and the sonic transit time of dolomite, the sonic transit time of the sonic rocks at each logging depth is determined.

[0026] The acoustic transit time of the rock at each logging depth, as well as the acoustic transit time of the clay content, pore fluid, and clay content at each logging depth, are input into the matrix porosity assessment model to obtain the matrix porosity of the core at each logging depth.

[0027] Secondly, this disclosure provides a device for determining crack porosity, comprising:

[0028] The evaluation module is configured to evaluate the matrix porosity of the core at each logging depth based on logging data and a matrix porosity evaluation model at each logging depth, and to evaluate the fracture porosity of the core at each logging depth based on logging data and a fracture porosity evaluation model at each logging depth.

[0029] The determination module is configured to determine the total fracture porosity of the core at each logging depth based on the matrix porosity and the fracture porosity of the core at each logging depth;

[0030] The correction module is configured to correct the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth, thereby obtaining the corrected logging fracture porosity of the core at each logging depth.

[0031] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0032] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0033] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0034] This disclosure provides a method, apparatus, equipment, medium, and computer program for determining fracture porosity. Using core logging data from a target area and an evaluation model, it determines the matrix porosity and fracture porosity of the core. Combining the matrix porosity and fracture porosity, it determines the total fracture porosity of the core. Furthermore, by evaluating the total fracture porosity of the core, it corrects the fracture porosity obtained from logging to obtain the final fracture porosity. This improves the scientific validity and accuracy of the obtained fracture porosity.

[0035] 1. Technical Features: Based on logging data and matrix porosity assessment models at each logging depth, the matrix porosity of the core sample at each logging depth is evaluated. Additionally, based on logging data and fracture porosity assessment models at each logging depth, the fracture porosity of the core sample at each logging depth is evaluated. The technical effect is to solve the problem of not being able to obtain fracture porosity that can scientifically characterize fractured oil and gas reservoirs, given their low porosity, strong heterogeneity, and complex fracture distribution.

[0036] 2. Technical features: Based on the total fracture porosity at each logging depth, the logging fracture porosity at each logging depth is corrected to obtain the corrected logging fracture porosity of the core at each logging depth. The technical effect is to solve the problem that the fracture porosity obtained from logging cannot accurately represent the true porosity of fractures. Attached Figure Description

[0037] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0038] Figure 1This is a flowchart illustrating a method for determining crack porosity according to an embodiment of the present disclosure.

[0039] Figure 2 This is a schematic diagram of the total crack porosity of a target area provided in an embodiment of this disclosure.

[0040] Figure 3 This is a schematic diagram illustrating the crack porosity of a target region determined by an FMI method according to an embodiment of this disclosure.

[0041] Figure 4 This is a cross-sectional diagram of total fracture porosity and logging fracture porosity in a target area, provided as an embodiment of the present disclosure.

[0042] Figure 5 This is a schematic diagram of a well logging data curve for a target area and a modified well logging fracture porosity curve provided in an embodiment of this disclosure.

[0043] Figure 6 A block diagram of a crack porosity determination device provided in an embodiment of this disclosure.

[0044] Figure 7 This is a schematic diagram of a computer program product provided in an embodiment of the present disclosure.

[0045] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0046] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure 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 the embodiments of this disclosure 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 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.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] Example 1

[0050] Figure 1 This is a flowchart illustrating a method for determining crack porosity according to an embodiment of this disclosure. This method can be applied to a terminal device, which may be a computer, laptop, tablet, or server, etc., and this disclosure does not limit the application to this type of device. Figure 1 As shown, the method for determining crack porosity includes:

[0051] Step S101: Obtain core logging data for the target area;

[0052] Among them, the core logging data includes logging data at multiple logging depths in the core;

[0053] Step S102: Based on the logging data at each logging depth and the matrix porosity assessment model, evaluate the matrix porosity of the core at each logging depth; and based on the logging data at each logging depth and the fracture porosity assessment model, evaluate the fracture porosity of the core at each logging depth.

[0054] Step S103: Based on the matrix porosity and fracture porosity of the core at each logging depth, determine the total fracture porosity of the core at each logging depth;

[0055] Step S104: Based on the total fracture porosity at each logging depth, correct the logging fracture porosity at each logging depth to obtain the corrected logging fracture porosity of the core at each logging depth.

[0056] In summary, the fracture porosity determination method provided in this disclosure, on the one hand, considers the characteristics of fractured oil and gas reservoirs, such as low porosity, strong heterogeneity, and complex fracture distribution. It determines the matrix porosity and fracture porosity of the core through well logging data and an evaluation model, and combines the matrix porosity and fracture porosity of the core to determine the total fracture porosity of the core. This improves the matching degree between the obtained fracture porosity evaluation results and the actual characteristics of the core, thus facilitating the acquisition of a scientifically accurate fracture porosity. On the other hand, by evaluating the total fracture porosity of the core, the fracture porosity obtained from well logging is corrected. This allows for the correction of actual measurement data based on theoretical foundations, improving the accuracy of fracture porosity characterized by complex features based on well logging data.

[0057] Example 2

[0058] Based on the above embodiments, the process of the terminal device acquiring core logging data of the target area may include: the terminal device reading the logging data of the target area in response to receiving a fracture porosity assessment command for the target area; wherein, the target area is the oil and gas production area that needs to be calculated for fracture porosity, and the specific area can be determined based on actual needs. This embodiment of the present disclosure does not limit this. The terminal device can store logging data of multiple areas to facilitate the calculation of fracture porosity in the target area.

[0059] It is understood that, in this embodiment of the disclosure, the core logging data includes logging data at multiple logging depths in the core. The logging data may include sonic transit time logging data and dual lateral logging data. The sonic transit time logging data includes: limestone content, dolomite content, sonic transit time of limestone, sonic transit time of dolomite, clay content, sonic transit time of pore fluid, sonic transit time of clay, and clay content. The dual lateral logging response data includes: deep lateral conductivity, shallow lateral conductivity, deep lateral resistivity, shallow lateral resistivity, fluid conductivity, bedrock conductivity, oil layer mud resistivity, water layer mud resistivity, formation water conductivity, fracture opening, vertical distance between fractures, limestone content, dolomite content, sonic transit time of limestone, sonic transit time of dolomite, clay content, sonic transit time of pore fluid, sonic transit time of clay, and clay content, etc.

[0060] Example 3

[0061] Based on the above embodiments, the process by which the terminal device evaluates the matrix porosity of the core at each logging depth based on the logging data and matrix porosity evaluation model at each logging depth includes: determining the acoustic transit time of the sonic rock at each logging depth based on the limestone and dolomite content in the logging data at each logging depth, as well as the acoustic transit time of the limestone and dolomite; then, inputting the acoustic transit time of the sonic rock at each logging depth, as well as the clay content, the acoustic transit time of the pore fluid, the acoustic transit time of the clay, and the clay content at each logging depth into the matrix porosity evaluation model to obtain the matrix porosity of the core at each logging depth. When the core is composed of limestone and dolomite, the acoustic transit time at each logging depth can be determined based on the limestone and dolomite content, as well as the acoustic transit time of limestone and dolomite. Then, combined with other acoustic logging data, the matrix porosity of the core at each logging depth can be determined, thereby improving the accuracy of the matrix porosity of the core composed of limestone and dolomite.

[0062] The process of determining the acoustic transit time of the sonic rock at each logging depth, based on the limestone and dolomite content, and the sonic transit time of limestone and dolomite in the logging data at each logging depth, may include: determining the acoustic transit time of the sonic rock at each logging depth based on the limestone and dolomite content, the sonic transit time of limestone and dolomite in the logging data at each logging depth, and a first formula, wherein the first formula is:

[0063]

[0064] In Formula 1, Δt ma The acoustic wave travel time is Δt. ma2 V2 is the time difference of sound waves in limestone, V2 is the limestone content, and Δt is the time difference of sound waves in limestone. ma3 V3 represents the acoustic transit time of dolomite, and V3 represents the dolomite content.

[0065] The matrix porosity assessment model is as follows:

[0066]

[0067] In Formula 2, φ1 is the matrix porosity, Δt is the acoustic transit time, and Δt f C represents the acoustic transit time of the pore fluid. p V is the compaction correction factor. sh The mud content, △t sh The sound wave transit time of the mud is denoted as ; wherein, the compaction correction coefficient can be determined based on actual needs, and this embodiment does not limit it. For example, the compaction correction coefficient can be 1.

[0068] Example 4

[0069] Based on the above embodiments, the process by which the terminal device evaluates the fracture porosity of the core at each logging depth, based on logging data and a fracture porosity evaluation model at each logging depth, may include: inputting the bilateral lateral logging response data, fluid conductivity, and bedrock conductivity from the logging data at each logging depth into the fracture porosity evaluation model to obtain the fracture porosity of the core at each logging depth. The fracture porosity evaluation model is a mathematical model relating the bilateral lateral logging response to porosity, fluid conductivity, and bedrock conductivity. A fracture porosity evaluation model relating the bilateral lateral logging response to porosity, fluid conductivity, and bedrock conductivity can be constructed using numerical simulation methods. Combined with the actually measured bilateral lateral logging response data, fluid conductivity, and bedrock conductivity, the fracture porosity of the core at each logging depth can be determined efficiently.

[0070] The dual-lateral logging response data can include: deep lateral conductivity, shallow lateral conductivity, deep lateral resistivity, and shallow lateral resistivity.

[0071] Example 5

[0072] Based on the above embodiments, the fractures in the core sample within the target area include a network of fractures. The terminal equipment evaluates the fracture porosity of the core sample at each logging depth using logging data and a fracture porosity assessment model. This process includes: inputting the oil layer mud resistivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth into the network of fracture porosity prediction model to obtain the oil layer fracture porosity of the core sample at each logging depth; and inputting the water layer mud resistivity, formation water conductivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth into the network of fracture porosity prediction model to obtain the water layer fracture porosity of the core sample at each logging depth; further, the oil layer fracture porosity and the water layer fracture porosity of the core sample at each logging depth are determined as the fracture porosity of the core sample at each logging depth. Since network fractures can typically include both oil-bearing and water-bearing network fractures, when the fractures in the core within the target area include network fractures, the porosity of fractures that conform to the characteristics of network fractures can be determined based on well logging data and prediction models of oil-bearing and water-bearing network fracture porosity, thereby improving the accuracy of the determined network fracture porosity.

[0073] The oil reservoir fracture porosity prediction model is as follows:

[0074]

[0075] In Formula 3, φ2 represents the oil reservoir fracture porosity, and R...m C represents the resistivity of the oil reservoir mud. LLS For deep lateral conductivity, C LLD For shallow lateral conductivity, m f The bonding index is defined as follows: the bonding index can be determined based on actual needs, and this disclosure does not limit this.

[0076] The model for predicting the porosity of water-bearing fractures is as follows:

[0077]

[0078] In Formula 4, φ3 represents the porosity of the water layer fractures, and C m C represents the resistivity of the mud in the aquifer. w The electrical conductivity of the formation water.

[0079] Example 6

[0080] Based on the above embodiments, the fractures in the core sample within the target area include a single set of fractures. The terminal equipment evaluates the fracture porosity of the core sample at each logging depth based on logging data and a fracture porosity evaluation model. The process includes: inputting the fracture opening and vertical distance between fractures from the logging data at each logging depth into the vertical fracture porosity evaluation model to obtain the vertical fracture porosity of the core sample at each logging depth; and inputting the fracture opening and vertical distance between fractures from the logging data at each logging depth into the low-angle fracture porosity evaluation model to obtain the low-angle fracture porosity of the core sample at each logging depth; further, the vertical fracture porosity and low-angle fracture porosity of the core sample at each logging depth are determined as the fracture porosity of the core sample at each logging depth. Since the porosity characteristics of a single set of fractures can usually be characterized by vertical fracture porosity and low-angle fracture porosity, when the fractures in the core within the target area include a single set of fractures, the fracture porosity that conforms to the characteristics of a single set of fractures can be determined based on well logging data and vertical fracture porosity assessment models and low-angle fracture porosity assessment models, thereby improving the accuracy of the determined fracture porosity of a single set of fractures.

[0081] The vertical crack porosity assessment model is as follows:

[0082]

[0083] In formula 5, φ fv d represents the vertical crack porosity, d represents the crack opening, and h represents the vertical distance between cracks.

[0084] The porosity assessment model for low-angle cracks is as follows:

[0085]

[0086] In formula 6, φ fh Porosity of low-angle cracks.

[0087] Example 7

[0088] Based on the above embodiments, the process by which the terminal device determines the total fracture porosity of the core at each logging depth based on the matrix porosity and fracture porosity at each logging depth may include: determining the sum of the matrix porosity and fracture porosity of the core at each logging depth as the total fracture porosity of the core at each logging depth; or, determining the weighted sum of the matrix porosity and fracture porosity of the core at each logging depth as the total fracture porosity of the core at each logging depth, thereby improving the matching degree between the determined total fracture porosity and the actual situation.

[0089] The weight values ​​of matrix porosity and fracture porosity at each logging depth in the core can be determined based on the actual situation of fractures in the core of the target area, and this disclosure does not limit this.

[0090] Example 8

[0091] Based on the above embodiments, the terminal equipment corrects the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth to obtain the corrected logging fracture porosity of the core at each logging depth. This process includes: establishing a regression model of logging fracture porosity with respect to total fracture porosity based on the total fracture porosity of the core at multiple logging depths; further, inputting the total fracture porosity at each logging depth into the regression model to obtain the corrected logging fracture porosity of the core at each logging depth. A relationship model between logging fracture porosity and total fracture porosity can be fitted using the total fracture porosity and logging fracture porosity at multiple logging depths. Based on this relationship model, and combined with the total fracture porosity at each logging depth, the logging fracture porosity at each logging depth is corrected. This achieves the goal of correcting measured data with theoretical data, improving the accuracy of the determined measured data.

[0092] Optionally, the terminal equipment can also invert the state of each fracture based on the deep and shallow lateral resistivity in the well logging data, where the inversion formula is:

[0093]

[0094] In Formula 7, Y is the crack state index, and R... LLD For deep lateral resistivity, R LLSis the shallow lateral resistivity.

[0095] Among them, when Y > 0.1, it is a high-angle crack; when 0 < Y < 0.1, it is an inclined crack; when Y < 0, it is a low-angle crack, and when y < 0, it is a low-angle crack or the crack is not developed.

[0096] Example 9

[0097] On the basis of the above embodiments, this embodiment provides an application example.

[0098] Taking the oilfield in target area A as an example, based on the logging data of 21 wells in the target area A, the total fracture porosity at each logging depth determined is as Figure 2 shown, where the horizontal axis is the total fracture porosity and the vertical axis is the logging depth; at the same time, the fracture porosity of 21 wells in the target area A determined by Formation MicroScanner Image (FMI) is as Figure 3 shown. Combining Figure 2 and Figure 3 shown, the total fracture porosity of the core at each logging depth determined in the embodiments of the present disclosure is basically consistent with the fracture porosity determined by the FMI method, indicating that the total fracture porosity of the core at each logging depth determined in the embodiments of the present disclosure has high accuracy.

[0099] Furthermore, as Figure 4 shown, a crossplot of the total fracture porosity at each logging depth in the target area A and the logging fracture porosity at each logging depth in the target area A can be established, where the horizontal axis is the logging fracture porosity and the vertical axis is the total fracture porosity, and a regression is performed on the total fracture porosity of the core in the target area A at multiple logging depths and the logging fracture porosity of the core in multiple logging depths, and the regression model of the logging fracture porosity with respect to the total fracture porosity is obtained as:

[0100]

[0101] In formula 8, φ f is the total fracture porosity, and φ fc is the logging fracture porosity.

[0102] It can be understood that the terminal device can input the total fracture porosity at each logging depth in the target area A into the regression model shown in formula 8 to obtain the corrected logging fracture porosity of the core at each logging depth; as Figure 5 shown, Figure 5The curves of shallow lateral conductivity, deep lateral conductivity, and well logging fracture porosity in the well logging data of target area A are shown, as well as the curve of corrected well logging fracture porosity, where the vertical axis represents the well logging depth.

[0103] Example 10

[0104] Based on the above embodiments, this embodiment provides a device for determining crack porosity, such as... Figure 6 As shown, Figure 6 A block diagram of a crack porosity determination apparatus 600 provided in an embodiment of this disclosure is shown. The crack porosity determination apparatus 600 includes:

[0105] The acquisition module 601 is configured to acquire core logging data of a target area, wherein the core logging data includes logging data at multiple logging depths of the core.

[0106] Evaluation module 602 is configured to evaluate the matrix porosity of the core at each logging depth based on logging data and a matrix porosity evaluation model at each logging depth, and to evaluate the fracture porosity of the core at each logging depth based on logging data and a fracture porosity evaluation model at each logging depth.

[0107] The determination module 603 is configured to determine the total fracture porosity of the core at each logging depth based on the matrix porosity and fracture porosity of the core at each logging depth;

[0108] The correction module 604 is configured to correct the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth, so as to obtain the corrected logging fracture porosity of the core at each logging depth.

[0109] Optionally, the correction module 604 is configured to:

[0110] Based on the total fracture porosity of the core at the multiple logging depths, and the logging fracture porosity of the core at the multiple logging depths, a regression model of the logging fracture porosity with respect to the total fracture porosity is established.

[0111] The total fracture porosity at each logging depth is input into the regression model to obtain the corrected logging fracture porosity of the core at each logging depth.

[0112] Optionally, the evaluation module 602 is configured to:

[0113] The lateral logging response data, fluid conductivity, and bedrock conductivity from the logging data at each logging depth are input into the fracture porosity evaluation model to obtain the fracture porosity of the core at each logging depth. The fracture porosity evaluation model is a mathematical model relating the lateral logging response to porosity, fluid conductivity, and bedrock conductivity.

[0114] Optionally, the fractures in the core sample within the target area include network fractures, and the evaluation module 602 is configured to:

[0115] The oil layer mud resistivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the oil layer fracture porosity prediction model of the network fracture to obtain the oil layer fracture porosity of the core at each logging depth.

[0116] The water layer mud resistivity, formation water conductivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the water layer fracture porosity prediction model of the network fracture to obtain the water layer fracture porosity of the core at each logging depth.

[0117] The oil layer fracture porosity and the water layer fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

[0118] Optionally, the fractures in the core sample within the target area include a single set of fractures, and the evaluation module 602 is configured to:

[0119] The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the vertical fracture porosity evaluation model to obtain the vertical fracture porosity of the core at each logging depth.

[0120] The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the low-angle fracture porosity evaluation model to obtain the low-angle fracture porosity of the core at each logging depth.

[0121] The vertical fracture porosity and the low-angle fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

[0122] Optionally, the evaluation module 602 is configured to:

[0123] Based on the limestone and dolomite content in the logging data at each logging depth, as well as the sonic transit time of limestone and the sonic transit time of dolomite, the sonic transit time of the sonic rocks at each logging depth is determined.

[0124] The acoustic transit time of the rock at each logging depth, as well as the acoustic transit time of the clay content, pore fluid, and clay content at each logging depth, are input into the matrix porosity assessment model to obtain the matrix porosity of the core at each logging depth.

[0125] Example 11

[0126] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0127] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0128] In some implementations of this embodiment, such as Figure 7 As shown, a computer program product 700 is provided, including a computer program 701, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0129] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0130] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0131] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0132] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0133] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0134] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0135] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0136] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0137] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A fracture porosity determination method characterized by, include: Acquire core logging data for the target area, wherein the core logging data includes logging data at multiple logging depths in the core; Based on the logging data at each logging depth and the matrix porosity assessment model, the matrix porosity of the core at each logging depth is assessed; and based on the logging data at each logging depth and the fracture porosity assessment model, the fracture porosity of the core at each logging depth is assessed. Based on the matrix porosity and fracture porosity of the core at each logging depth, the total fracture porosity of the core at each logging depth is determined. Based on the total fracture porosity at each logging depth, the logging fracture porosity at each logging depth is corrected to obtain the corrected logging fracture porosity of the core at each logging depth.

2. The method of claim 1, wherein, The step of correcting the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth to obtain the corrected logging fracture porosity of the core at each logging depth includes: Based on the total fracture porosity of the core at the multiple logging depths, and the logging fracture porosity of the core at the multiple logging depths, a regression model of the logging fracture porosity with respect to the total fracture porosity is established. The total fracture porosity at each logging depth is input into the regression model to obtain the corrected logging fracture porosity of the core at each logging depth.

3. The method of claim 1, wherein, The evaluation of fracture porosity in the core sample at each logging depth, based on logging data and a fracture porosity assessment model at each logging depth, includes: The lateral logging response data, fluid conductivity, and bedrock conductivity from the logging data at each logging depth are input into the fracture porosity evaluation model to obtain the fracture porosity of the core at each logging depth. The fracture porosity evaluation model is a mathematical model relating the lateral logging response to porosity, fluid conductivity, and bedrock conductivity.

4. The method of claim 1, wherein, The fractures in the core sample within the target area include a network of fractures. The assessment of fracture porosity at each logging depth, based on logging data and a fracture porosity assessment model, includes: The oil layer mud resistivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the oil layer fracture porosity prediction model of the network fracture to obtain the oil layer fracture porosity of the core at each logging depth. The water layer mud resistivity, formation water conductivity, deep lateral conductivity, and shallow lateral conductivity from the logging data at each logging depth are input into the water layer fracture porosity prediction model of the network fracture to obtain the water layer fracture porosity of the core at each logging depth. The oil layer fracture porosity and the water layer fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

5. The method of claim 1, wherein, The fractures in the core sample within the target area include a single set of fractures. The assessment of fracture porosity at each logging depth, based on logging data and a fracture porosity assessment model, includes: The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the vertical fracture porosity evaluation model to obtain the vertical fracture porosity of the core at each logging depth. The fracture opening and vertical distance between fractures in the logging data at each logging depth are input into the low-angle fracture porosity evaluation model to obtain the low-angle fracture porosity of the core at each logging depth. The vertical fracture porosity and the low-angle fracture porosity of the core at each logging depth are determined as the fracture porosity of the core at each logging depth.

6. The method of claim 1, wherein, The assessment of core matrix porosity at each logging depth, based on logging data and a matrix porosity assessment model, includes: Based on the limestone and dolomite content in the logging data at each logging depth, as well as the sonic transit time of limestone and the sonic transit time of dolomite, the sonic transit time of the sonic rocks at each logging depth is determined. The acoustic transit time of the rock at each logging depth, as well as the acoustic transit time of the clay content, pore fluid, and clay content at each logging depth, are input into the matrix porosity assessment model to obtain the matrix porosity of the core at each logging depth.

7. A fracture porosity determination apparatus characterized by, include: The acquisition module is configured to acquire core logging data of a target area, the core logging data including logging data at multiple logging depths in the core. The evaluation module is configured to evaluate the matrix porosity of the core at each logging depth based on logging data and a matrix porosity evaluation model at each logging depth, and to evaluate the fracture porosity of the core at each logging depth based on logging data and a fracture porosity evaluation model at each logging depth. The determination module is configured to determine the total fracture porosity of the core at each logging depth based on the matrix porosity and the fracture porosity of the core at each logging depth; The correction module is configured to correct the logging fracture porosity at each logging depth based on the total fracture porosity at each logging depth, thereby obtaining the corrected logging fracture porosity of the core at each logging depth.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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