Reservoir nuclear magnetic porosity calculation method and device based on rock structure and composition

Abstract

The present application provides a reservoir nuclear magnetic porosity calculation method and device based on rock structure and composition, relates to the technical field of well interpretation and reservoir evaluation in oil exploration and development, and comprises the following steps: obtaining the area proportion of gravel and the area proportion of different particle size components based on micro-resistivity imaging logging data; constructing a basic correction formula of nuclear magnetic porosity based on a volume physical model, simplifying the basic correction formula according to the area proportion of gravel, and obtaining a basic correction formula based on the area proportion; introducing a volume correction term, improving the formula, and establishing a nonlinear volume correction model; introducing a magnetic mineral correction term to the nonlinear volume correction model to form a final correction model; the magnetic mineral correction term is determined based on the content of magnetic minerals in the formation or the area proportion of different particle size components; the model parameters of the final correction model are determined according to block data, the original nuclear magnetic logging porosity is corrected point by point by using the final correction model, and the corrected porosity is output.

Description

Technical Field

[0001] This invention relates to the field of well logging interpretation and reservoir evaluation technology in petroleum exploration and development, and particularly to a method and apparatus for calculating reservoir nuclear magnetic porosity based on rock structure and composition. Background Technology

[0002] Complex oil and gas reservoirs, as an important area of ​​oil and gas exploration, are characterized by complex lithology, extreme heterogeneity, and large variations in grain diameter. Nuclear magnetic resonance (NMR) logging technology can provide key parameters such as porosity and pore structure, making it a core tool for evaluating such complex reservoirs. However, NMR logging instruments typically operate based on sensitive areas of specific thickness. In conglomerate reservoirs, the presence of numerous large-diameter gravel particles often results in these dense, non-porous gravels occupying a significant portion of the sensitive area, leading to a lower detection rate of pore fluid signals. Furthermore, NMR logging is affected by the ferromagnetic minerals in the reservoir; the abundance of pyrite and rhodochrosite in conglomerate and shale oil reservoirs reduces porosity. Meanwhile, electrical imaging logging, with its high resolution and sensitivity to resistivity changes, can directly reflect the distribution characteristics of gravel particles and sedimentary structures around the wellbore. Combining high-resolution image information from electrical imaging logging with porosity calculations from nuclear magnetic resonance logging has significant geological and engineering value for improving the evaluation accuracy of complex reservoirs.

[0003] In existing technologies, a method for correcting porosity in nuclear magnetic resonance (NMR) logging has been proposed. This method acquires the wellbore electrical imaging logging image and NMR logging porosity corresponding to the target rock, and establishes a correction model for the NMR logging porosity based on the size of the target rock. However, this method does not fully consider the influence of rock composition variations on NMR porosity calculations. Another existing technology proposes a method for correcting porosity in gravelly sandstone reservoirs. Based on macroscopic detailing of full-diameter gravelly sandstone cores and porosity testing of corresponding standard core samples, a 7mm diameter is used as the upper limit diameter of the clastic particles that can be normally drilled from the standard core sample. This method corrects for the coarse clastic particle content in gravelly sandstone reservoir porosity. While this addresses the issue that un-drilled coarse clastic particles, though lacking internal pores, occupy sample volume, leading to an overestimation of the actual porosity of gravelly sandstone reservoirs using a 25mm diameter standard core sample, this method falls under the technical field of coarse clastic rock experimental measurement.

[0004] In summary, there is an urgent need for a technical solution that can overcome the shortcomings of existing technologies, establish a more accurate calibration model in the evaluation of complex reservoirs, and restore the true porosity of the formation. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method and apparatus for calculating reservoir porosity based on rock structure and composition using nuclear magnetic resonance (NMR). This overcomes the technical challenge in evaluating complex reservoirs where the sensitive area of ​​NMR logging instruments is occupied by gravel particles and influenced by magnetic mineral components (minerals that affect NMR measurement results, such as iron-bearing minerals and manganese-bearing minerals), leading to NMR-measured porosity being significantly lower than the true porosity analyzed in core samples. The overall solution of this invention establishes a more accurate calibration model to restore the true porosity of the formation.

[0006] In a first aspect of this invention, a method for calculating reservoir nuclear magnetic porosity based on rock structure and composition is proposed, the method comprising: The area percentage of gravel and the area percentage of different particle size components were obtained based on microresistivity imaging logging data. A basic correction formula for nuclear magnetic resonance porosity based on a volume physical model is constructed. The basic correction formula is then simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. A volume correction term is introduced to improve the basic correction formula based on area ratio, and a nonlinear volume correction model is established. A magnetic mineral correction term is introduced into the nonlinear volume correction model to form the final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components; The model parameters of the final correction model are determined based on the block data. The original nuclear magnetic resonance logging porosity is then corrected point by point using the final correction model, and the corrected porosity is output.

[0007] In a second aspect of the present invention, a reservoir nuclear magnetic resonance porosity calculation device based on rock structure and composition is proposed, the device comprising: The area proportion analysis module is used to obtain the area proportion of gravel and the area proportion of different particle size components based on microresistivity imaging logging data. The basic correction formula construction module is used to construct a basic correction formula for nuclear magnetic porosity based on a volume physical model. The basic correction formula is simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. The volume correction module is used to introduce a volume correction term to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model. A magnetic mineral correction module is used to introduce a magnetic mineral correction term into the nonlinear volume correction model to form a final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components; The point-by-point correction module is used to determine the model parameters of the final correction model based on the block data, and to use the final correction model to perform point-by-point correction on the original nuclear magnetic resonance logging porosity, and output the corrected porosity.

[0008] In a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for calculating reservoir nuclear magnetic porosity based on rock structure and composition.

[0009] In a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for calculating reservoir nuclear magnetic porosity based on rock structure and composition.

[0010] In a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements a method for calculating reservoir nuclear magnetic porosity based on rock structure and composition.

[0011] The method and apparatus for calculating reservoir porosity based on rock structure and composition proposed in this invention integrates the high-resolution structural information from electro-imaging logging with the volumetric physical model from nuclear magnetic resonance logging, thereby achieving the correction of porosity from nuclear magnetic resonance logging. This significantly reduces the error in porosity calculation and lays a solid data foundation for reserve calculation, production prediction, and fluid property identification. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic flowchart of a reservoir nuclear magnetic porosity calculation method based on rock structure and composition according to an embodiment of the present invention.

[0014] Figure 2 This is a schematic diagram of the process for obtaining the area ratio of gravel based on microresistivity imaging logging data according to an embodiment of the present invention.

[0015] Figure 3 This is a schematic flowchart illustrating the process of constructing the basic correction formula for nuclear magnetic resonance porosity based on a volume physical model, according to an embodiment of the present invention.

[0016] Figure 4This is a schematic diagram of the process for forming the final correction model according to an embodiment of the present invention.

[0017] Figure 5 This is a schematic diagram of the process of using the final correction model to correct the porosity of the original nuclear magnetic resonance logging point by point according to an embodiment of the present invention.

[0018] Figure 6 This is a schematic diagram of the physical model of the nuclear magnetic resonance logging detection mechanism according to a specific embodiment of the present invention.

[0019] Figure 7 This is a schematic diagram illustrating the relationship between the proportion of fine conglomerate and the deviations between nuclear magnetic resonance porosity and core analysis porosity in a specific embodiment of the present invention.

[0020] Figure 8 This is a schematic diagram illustrating the relationship between the proportion of small conglomerate and the deviations between nuclear magnetic resonance porosity and core analysis porosity in a specific embodiment of the present invention.

[0021] Figure 9 This is a schematic diagram showing the relationship between the proportion of conglomerate and the deviation between nuclear magnetic resonance porosity and core analysis porosity in a specific embodiment of the present invention.

[0022] Figure 10 This is a schematic diagram illustrating the relationship between the proportion of coarse conglomerate and the deviations between nuclear magnetic resonance porosity and core analysis porosity in a specific embodiment of the present invention.

[0023] Figure 11 This is a schematic diagram illustrating the relationship between the deviation of nuclear magnetic porosity and core analysis porosity and the content of magnetic minerals in a specific embodiment of the present invention.

[0024] Figure 12 This is a schematic diagram of the architecture of a reservoir nuclear magnetic porosity calculation device based on rock structure and composition according to an embodiment of the present invention.

[0025] Figure 13 This is a schematic diagram of a computer device structure according to an embodiment of the present invention. Detailed Implementation

[0026] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0027] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0028] According to an embodiment of the present invention, a method and apparatus for calculating reservoir nuclear magnetic porosity based on rock structure and composition are proposed, which relates to the field of well logging interpretation and reservoir evaluation technology in oil exploration and development.

[0029] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0030] Figure 1 This is a schematic flowchart of a reservoir NMR porosity calculation method based on rock structure and composition, according to an embodiment of the present invention. Figure 1 As shown, the method includes: S101, based on microresistivity imaging logging data, obtains the area ratio of gravel and the area ratio of different particle size components. S102, Construct a basic correction formula for nuclear magnetic porosity based on a volume physical model, and simplify the basic correction formula according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. S103, Introduce a volume correction term to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model; S104, A magnetic mineral correction term is introduced into the nonlinear volume correction model to form the final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components; S105, determine the model parameters of the final correction model based on the block data, use the final correction model to correct the original nuclear magnetic resonance logging porosity point by point, and output the corrected porosity.

[0031] To provide a clearer explanation of the above method for calculating reservoir porosity based on rock structure and composition, each step will be explained in detail below.

[0032] In one embodiment, for S101, the area percentage of gravel and the area percentage of different particle size components are obtained based on microresistivity imaging logging data. (Reference) Figure 2 The specific process includes: S1011, using microresistivity imaging logging data, constructs a semantic segmentation dataset for rock gravel; S1012, Based on the semantic segmentation dataset, a convolutional neural network model in deep learning is used to intelligently identify and segment the wellbore electrical imaging image, extract the gravel area ratio of the entire well section, and further subdivide and extract the gravel area ratio of different particle sizes; wherein, at least the area ratio of fine gravel, small gravel, medium gravel, and coarse gravel is included.

[0033] For different grain sizes, the diameter of fine gravel is 2~8mm, the diameter of small gravel is 8~16mm, the diameter of medium gravel is 16~64mm, and the diameter of coarse gravel is 64~256mm.

[0034] In one embodiment, S102, a basic correction formula for NMR porosity based on a volume physical model is constructed. This basic correction formula is then simplified based on the area ratio of the gravel to obtain a basic correction formula based on area ratio. (See reference...) Figure 3 The specific process includes: S1021, establish the volume balance equation within the sensitive area of ​​the downhole nuclear magnetic resonance (NMR) logging instrument; wherein, based on the NMR logging detection mechanism, the signal response within the sensitive area of ​​the logging instrument is jointly determined by the gravel component and non-gravel components (matrix and pores).

[0035] The volume balance equation within the sensitive region of a downhole nuclear magnetic resonance (NMR) measuring instrument is: ; In the formula, , These represent the volume of gravel and the volume of non-gravel components within the detection area, respectively. Porosity of the gravel component; This represents the true porosity of the formation. For measuring porosity using nuclear magnetic resonance logging; S1022 uses the area ratio to approximate the volume ratio, simplifies the volume balance equation, and obtains the basic correction formula based on the area ratio after sorting.

[0036] Since electrical imaging logging can only obtain two-dimensional image information, the basic correction formula uses the area ratio to approximate the volume ratio (V≈S), which simplifies the volume balance equation.

[0037] Specifically, the volume ratio is approximated by the area ratio, simplifying the volume balance equation to: ; In the formula, , These represent the area percentage of gravel and the area percentage of non-gravel components within the detection area, respectively. After processing, the basic correction formula based on area ratio is obtained: .

[0038] In one embodiment, S103, a volume correction term is introduced to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model.

[0039] Considering that the two-dimensional area ratio cannot be completely equated to the volume ratio in the three-dimensional detection space, and that the impact of gravel volume on the measurement has a nonlinear coupling relationship with the logging porosity itself, a volume correction term jointly controlled by the gravel area ratio and logging NMR porosity is introduced to improve the basic correction formula in order to compensate for the error caused by approximating the volume ratio with the area ratio.

[0040] Specifically, a volume correction term jointly controlled by the area ratio of gravel and the porosity of logging nuclear magnetic resonance is introduced to compensate for the geometric error caused by approximating the volume ratio with the area ratio, and the basic correction formula based on the area ratio is improved into a nonlinear volume correction model.

[0041] For the aforementioned nonlinear volume correction model, the calculation formula is: ; In the formula, The true porosity of the formation, in % %. Porosity was measured using nuclear magnetic resonance logging, unit: % The percentage of gravel in the detection area, in decimal form; , These are the parameters for the volume correction model.

[0042] In one embodiment, for S104, a magnetic mineral correction term is introduced into the nonlinear volume correction model to form a final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area proportion of the different grain size components. (Reference) Figure 4 The specific process is as follows: S1041. Based on well logging and core test data, identify the types and contents of magnetic minerals in the formation, calculate the deviation between nuclear magnetic resonance logging porosity and core analysis porosity, and analyze the correlation between the deviation and the magnetic mineral content, as well as the correlation with the area ratio of different particle size components.

[0043] S1042 If elemental logging was performed on the target well, the magnetic mineral correction term is directly calculated based on the correlation between porosity deviation and core magnetic mineral content determined in S1041. If elemental logging was not performed, the area ratio of the particle size component with the strongest correlation to porosity deviation is selected as the characterization factor of magnetic mineral influence, and the magnetic mineral correction term is constructed to form the final correction model.

[0044] The calculation formula for the final correction model is as follows: ; In the formula, The true porosity of the formation, in % %. Porosity was measured using nuclear magnetic resonance logging, unit: % The percentage of gravel in the detection area, in decimal form; , These are the parameters for the volume correction model; For magnetic mineral correction terms; This is the correction factor for magnetic minerals.

[0045] In one embodiment, for S105, the model parameters of the final correction model are determined based on block data, and the original NMR logging porosity is corrected point-by-point using the final correction model to output the corrected porosity. (Reference) Figure 5 The specific process includes: S1051, Select well sections in the target block with core data, use the core NMR analysis porosity as the true value and the logging NMR porosity at the corresponding depth as the input value, and determine the optimal model parameters of the final correction model based on the deviation between the true value and the input value; The optimal model parameters include volume correction model parameters. , Magnetic mineral correction term ; S1052, the calibrated final correction model is applied to the entire block and the entire well section to correct the original nuclear magnetic logging porosity point by point, and output the corrected porosity that conforms to the actual formation.

[0046] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0047] The present invention will now be described with reference to a specific embodiment.

[0048] Combined with S101, the area ratio of gravel and the area ratio of different particle size components were obtained based on microresistivity imaging logging data.

[0049] Using microresistivity imaging logging data as the basic data, 665 electrical imaging image samples with a size of 360×360 were selected to construct a semantic segmentation dataset for rock gravel.

[0050] A convolutional neural network model from deep learning is used for intelligent recognition and segmentation of wellbore electrical imaging images. Through the model's computation, the total area percentage of gravel in the entire well section is extracted. Based on the particle diameter grading standard, the area percentage of gravel of different particle sizes is further subdivided and extracted, specifically including the area percentages of fine gravel, small gravel, medium gravel, and coarse gravel. This provides high-precision structural parameters for subsequent physical model calibration.

[0051] Combined with S102, a basic correction formula for nuclear magnetic porosity based on the volume physical model is constructed. The basic correction formula is simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio.

[0052] refer to Figure 6 This is a schematic diagram of the physical model of the nuclear magnetic resonance logging detection mechanism according to a specific embodiment of the present invention. The outer transparent cylinder represents the detection area of ​​the logging instrument (the boundary of the core column). The inner cylinder represents the actual rock (taking conglomerate as an example), where H is the height, R is the radius of the rock, and D is the radius of the boundary. The dark part is the gravel skeleton (the solid part of the rock, especially the large-particle gravel component). The light blue area represents the pore space and interstitial material (including matrix, cement, and pore fluid, which are the main parts of the logging signal response).

[0053] Based on the nuclear magnetic resonance (NMR) logging detection mechanism, the signal response within the sensitive area of ​​the logging instrument is jointly determined by gravel components and non-gravel components (matrix and porosity). The volume balance equation within the sensitive area of ​​the downhole NMR logging instrument is established as follows: ; Since electrical imaging logging can only obtain two-dimensional image information of the wellbore, the basic correction formula uses area ratio to approximate volume ratio (V≈S), simplifying the above formula to: ; After sorting, we obtain the basic correction formula based on area ratio: .

[0054] By combining S103, a volume correction term is introduced to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model.

[0055] Considering that the two-dimensional area ratio cannot be completely equated to the volume ratio in the three-dimensional detection space, and that the impact of gravel volume on the measurement has a nonlinear coupling relationship with the logging porosity itself, a volume correction term jointly controlled by the gravel area ratio and logging NMR porosity is introduced to compensate for the geometric error caused by approximating the volume ratio with the area ratio. This improves the basic formula in S102 into a nonlinear volume correction model. The improved formula is: ; In the formula, , These are the parameters for the volume correction model.

[0056] Based on S104, the magnetic mineral correction term is determined, and the final correction model is constructed.

[0057] To address the issue of underestimated porosity due to nuclear magnetic resonance (NMR) signal attenuation caused by magnetic minerals in rocks, a correlation analysis was conducted. Specifically, the deviation between NMR logging porosity and core analysis porosity was calculated, and the correlation between this deviation and the area proportions of the four gravel components (fine gravel, small gravel, medium gravel, and coarse gravel) obtained in S101 was analyzed.

[0058] The area ratio of gravel-sized components that has the strongest correlation with porosity deviation is denoted as . This is used as a characterizing factor of the influence of magnetic minerals to construct a magnetic mineral correction term, forming the final correction model, which is defined as follows:

[0059] In the formula, This is the correction factor for magnetic minerals.

[0060] Combined with S105, the model parameters of the final correction model are determined based on the block data. The final correction model is then used to correct the original nuclear magnetic resonance logging porosity point by point, and the corrected porosity is output.

[0061] A well section with 158 core samples was selected. Core NMR porosity was used as the true value, and logging NMR porosity at the corresponding depth was used as the input value. The optimal model parameters for this block were calculated using an optimization algorithm. a =-1.97, b=15.4. Based on the deviation between the true porosity value and the input value, elemental logging was not performed in this well. Therefore, the relationship between the deviation and the magnetic mineral content was determined by combining core experimental data. Figure 7 , Figure 8 , Figure 9 and Figure 10 The gravel-sized component with the strongest correlation to porosity deviation was identified as medium conglomerate. Core XRD analysis showed that the magnetic mineral content in this well was approximately 3.7%. Figure 11 The established relationship calculations show that the porosity deviation caused by magnetic minerals is 6.39%, i.e., c=6.39, leading to the final correction formula:

[0062] The calibrated model was applied to the entire block and all well sections, and the original nuclear magnetic resonance logging porosity was corrected point by point to output a corrected porosity curve that conforms to the actual formation.

[0063] This invention effectively solves the technical problem of underestimated porosity in complex reservoirs due to gravel components occupying the detection sensitive area and the influence of magnetic minerals during nuclear magnetic resonance (NMR) logging. By fusing high-resolution structural information from electro-imaging logging with the volumetric physical model from NMR logging, porosity correction in NMR logging is achieved. Existing methods have a porosity calculation error greater than 15%, while this method has a calculation error of less than 6%, laying a solid data foundation for subsequent reserve calculation, production prediction, and fluid property identification.

[0064] After introducing the method of exemplary embodiments of the present invention, the following references are made. Figure 12 An exemplary embodiment of the present invention is described, which is a reservoir nuclear magnetic porosity calculation device based on rock structure and composition.

[0065] The implementation of the reservoir nuclear magnetic resonance porosity calculation device based on rock structure and composition can refer to the implementation of the above method, and the repetition will not be repeated. The term "module" or "unit" used below can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0066] Based on the same inventive concept, this invention also proposes a reservoir nuclear magnetic resonance porosity calculation device based on rock structure and composition, such as... Figure 12 As shown, the device includes: The area proportion analysis module 1210 is used to obtain the area proportion of gravel and the area proportion of different particle size components based on microresistivity imaging logging data. The basic correction formula construction module 1220 is used to construct a basic correction formula for nuclear magnetic porosity based on a volume physical model. The basic correction formula is simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. The volume correction module 1230 is used to introduce a volume correction term to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model. The magnetic mineral correction module 1240 is used to introduce a magnetic mineral correction term into the nonlinear volume correction model to form a final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components. The point-by-point correction module 1250 is used to determine the model parameters of the final correction model based on the block data, and to use the final correction model to perform point-by-point correction on the original nuclear magnetic logging porosity, and output the corrected porosity.

[0067] It should be noted that although several modules of the reservoir nuclear magnetic porosity calculation device based on rock structure and composition have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0068] Based on the aforementioned inventive concept, such as Figure 13 As shown, the present invention also proposes a computer device 1300, including a memory 1310, a processor 1320, and a computer program 1330 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1330, it implements the aforementioned method for calculating reservoir nuclear magnetic porosity based on rock structure and composition.

[0069] Based on the aforementioned inventive concept, this invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for calculating reservoir porosity based on rock structure and composition.

[0070] Based on the aforementioned inventive concept, this invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements a method for calculating reservoir nuclear magnetic porosity based on rock structure and composition.

[0071] The method and apparatus for calculating reservoir porosity based on rock structure and composition proposed in this invention integrates the high-resolution structural information from electro-imaging logging with the volumetric physical model from nuclear magnetic resonance logging, thereby achieving the correction of porosity from nuclear magnetic resonance logging. This significantly reduces the error in porosity calculation and lays a solid data foundation for reserve calculation, production prediction, and fluid property identification.

[0072] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating reservoir porosity based on rock structure and composition using nuclear magnetic resonance (NMR) analysis, characterized in that, The method includes: The area percentage of gravel and the area percentage of different particle size components were obtained based on microresistivity imaging logging data. A basic correction formula for nuclear magnetic resonance porosity based on a volume physical model is constructed. The basic correction formula is then simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. A volume correction term is introduced to improve the basic correction formula based on area ratio, and a nonlinear volume correction model is established. A magnetic mineral correction term is introduced into the nonlinear volume correction model to form the final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components; The model parameters of the final correction model are determined based on the block data. The original nuclear magnetic resonance logging porosity is then corrected point by point using the final correction model, and the corrected porosity is output. The basic correction formula for nuclear magnetic resonance porosity based on a volume physical model is constructed. This basic correction formula is simplified based on the area ratio of the gravel, resulting in a basic correction formula based on area ratio, which includes: A volume balance equation is established within the sensitive area of ​​a downhole nuclear magnetic resonance (NMR) logging instrument. Based on the NMR logging detection mechanism, the signal response within the sensitive area of ​​the logging instrument is jointly determined by the gravel component and the non-gravel component. The volume balance equation within the sensitive region of a downhole nuclear magnetic resonance (NMR) measuring instrument is: ； In the formula, , These represent the volume of gravel and the volume of non-gravel components within the detection area, respectively. Porosity of the gravel component; This represents the true porosity of the formation. For measuring porosity using nuclear magnetic resonance logging; By using area proportion to approximate volume proportion, the volume balance equation is simplified to: ； In the formula, , These represent the area percentage of gravel and the area percentage of non-gravel components within the detection area, respectively. After processing, a basic correction formula based on area ratio is obtained, which is: ； Specifically, a volume correction term is introduced to improve the basic correction formula based on area ratio, establishing a nonlinear volume correction model, including: A volume correction term jointly controlled by the area ratio of gravel and the logging NMR porosity is introduced to compensate for the geometric error caused by approximating the volume ratio with the area ratio, and the basic correction formula based on the area ratio is improved into a nonlinear volume correction model. For the nonlinear volume correction model, the calculation formula is: ； In the formula, The true porosity of the formation, in % %. Porosity was measured using nuclear magnetic resonance logging, unit: % The percentage of gravel area within the detection zone, in decimal form; , These are the parameters for the volume correction model; A magnetic mineral correction term is introduced into the nonlinear volume correction model to form the final correction model, which includes: Based on well logging and core test data, the types and contents of magnetic minerals in the formation were identified, the deviation between nuclear magnetic resonance logging porosity and core analysis porosity was calculated, and the correlation between the deviation and the magnetic mineral content, as well as the correlation with the area proportion of different particle size components, were analyzed. If elemental logging was performed on the target well, the magnetic mineral correction term is directly calculated based on the correlation between porosity deviation and core magnetic mineral content determined in the previous step. If elemental logging was not performed, the area ratio of the particle size component with the strongest correlation to porosity deviation is selected as the characterization factor of magnetic mineral influence, and a magnetic mineral correction term is constructed to form the final correction model. The calculation formula for the final correction model is as follows: ； In the formula, The true porosity of the formation, in % %. Porosity was measured using nuclear magnetic resonance logging, unit: % The percentage of gravel area within the detection zone, in decimal form; , These are the parameters for the volume correction model; For magnetic mineral correction terms; This is the correction factor for magnetic minerals.

2. The method for calculating reservoir porosity based on rock structure and composition according to claim 1, characterized in that, Based on microresistivity imaging logging data, the area percentage of gravel and the area percentage of different grain sizes were obtained, including: Using microresistivity imaging logging data, a semantic segmentation dataset of rock gravel was constructed. Based on the semantic segmentation dataset, a convolutional neural network model in deep learning is used to intelligently identify and segment the wellbore electrical imaging images, extract the gravel area ratio of the entire well section, and further subdivide and extract the gravel area ratio of different grain sizes; wherein, at least the area ratio of fine gravel, small gravel, medium gravel, and coarse gravel is included.

3. The method for calculating reservoir porosity based on rock structure and composition according to claim 1, characterized in that, Based on the block data, the model parameters of the final calibration model are determined. The final calibration model is then used to perform point-by-point calibration of the original nuclear magnetic resonance logging porosity, outputting the calibrated porosity, including: Select well sections with core data from the target block, use core NMR porosity as the true value and logging NMR porosity at the corresponding depth as the input value, and determine the optimal model parameters of the final calibration model based on the deviation between the true value and the input value; wherein, the optimal model parameters include volume calibration model parameters. , Magnetic mineral correction factor ; The calibrated final correction model is applied to the entire block and well section to correct the original nuclear magnetic resonance logging porosity point by point, outputting corrected porosity that conforms to the actual formation.

4. A reservoir nuclear magnetic resonance porosity calculation device based on rock structure and composition, characterized in that, The apparatus is used to perform the method according to any one of claims 1 to 3; the apparatus comprises: The area proportion analysis module is used to obtain the area proportion of gravel and the area proportion of different particle size components based on microresistivity imaging logging data. The basic correction formula construction module is used to construct a basic correction formula for nuclear magnetic porosity based on a volume physical model. The basic correction formula is simplified according to the area ratio of the gravel to obtain a basic correction formula based on the area ratio. The volume correction module is used to introduce a volume correction term to improve the basic correction formula based on area ratio and establish a nonlinear volume correction model. A magnetic mineral correction module is used to introduce a magnetic mineral correction term into the nonlinear volume correction model to form a final correction model; wherein, the magnetic mineral correction term is determined based on the magnetic mineral content in the formation or the area ratio of the different grain size components; The point-by-point correction module is used to determine the model parameters of the final correction model based on the block data, and to use the final correction model to perform point-by-point correction on the original nuclear magnetic resonance logging porosity, and output the corrected porosity.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 3.

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