Calculation method and system for shale content of paste-containing reservoir, terminal and medium

Abstract

The invention relates to the field of petroleum and natural gas exploration, and discloses a method and system for calculating the shale content of a paste-containing reservoir, a terminal and a medium, and the method comprises the steps: classifying lithology in the paste-containing reservoir, and obtaining a classification experiment data result; performing depth homing and logging response characteristic analysis on the classified test data result on a logging curve, and determining a logging sensitive curve reflecting lithology; establishing a multi-parameter intersection lithology identification chart according to the logging sensitive curve, identifying the lithology of the paste-containing reservoir, correspondingly calculating to obtain the shale content of the lithology of the paste-containing reservoir according to the identified lithology of the paste-containing reservoir, and taking the maximum value of the obtained shale content as a final shale content value. The problem of non-uniform distribution of the gypsum content of the gypsum-containing reservoir is solved, the calculation precision of the shale content and the reliable theoretical basis are improved, and the calculation error of the shale content is reduced.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration, and in particular to a method, system, terminal and medium for calculating the mud content of a grease-containing reservoir. Background Art

[0002] In oil and gas exploration for gypsum-bearing reservoirs, due to the presence of gypsum, the uneven distribution of gypsum, and the interbedded nature of various lithologies, it is difficult to accurately determine the reservoir's shale content using a single conventional gamma-ray curve, which in turn affects the accuracy of calculations of reservoir parameters such as porosity, permeability, and oil and gas saturation. Quantitative calculation of shale content is particularly important in logging evaluation of gypsum-bearing reservoirs.

[0003] As oil and gas exploration continues to expand into complex formations, complex formations such as gypsum-bearing sandstone reservoirs have gradually become an exploration focus. Compared with conventional sandstone and mudstone oil and gas reservoirs, gypsum-bearing reservoirs are characterized by strong heterogeneity, complex rock mineral content, and complex lithology. When a reservoir contains gypsum, the natural gamma ray logging curve value decreases. Since gypsum is not evenly distributed, continuing to use conventional gamma ray logging methods to calculate mud content will inevitably cause serious errors, making it difficult to accurately determine the mud content of the formation. This affects the accurate classification of reservoirs, the calculation of reservoir parameters, and the determination of reservoir fluid properties, bringing certain difficulties to logging interpretation and evaluation. Therefore, it is necessary to first identify and classify the lithology of gypsum-bearing reservoirs and establish a method for calculating the mud content of gypsum-bearing reservoirs based on lithology identification. Summary of the Invention

[0004] In order to overcome the defects of the above-mentioned prior art, the purpose of the present invention is to provide a method, system, terminal and medium for calculating the mud content of a gypsum-containing reservoir, so as to solve the technical problem in the prior art that the uneven distribution of gypsum content in the reservoir leads to large errors in calculating the mud content.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for calculating the mud content of a gypsum-containing reservoir, comprising:

[0007] Classify the lithology of gypsum-bearing reservoirs and obtain classification experimental data results;

[0008] Performing depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology;

[0009] A multi-parameter intersection lithology identification chart is established based on the logging sensitivity curve to identify the lithology of the gypsum-containing reservoir. The mud content of the lithology of the gypsum-containing reservoir is calculated based on the identified lithology of the gypsum-containing reservoir, and the maximum mud content obtained is used as the final mud content value.

[0010] Preferably, the lithology in the gypsum-bearing reservoir is classified by a rock thin section test method to obtain test data results, wherein the classification test data results include sandstone, gypsum-bearing sandstone, gypsum sandstone, mudstone, mudstone and gypsum mudstone.

[0011] Furthermore, the classification test data results were depth-relocated on the logging curves and the logging response characteristics were analyzed to determine the logging sensitivity curves including deep resistivity curve, uranium-free gamma curve, acoustic time difference curve, compensated neutron curve, compensated density curve and natural potential curve.

[0012] Furthermore, the specific process of establishing a multi-parameter intersection lithology identification chart based on the well logging sensitivity curve is as follows:

[0013] The ratio of uranium-free gamma to resistivity is used as the horizontal axis, and the ratio of acoustic time difference to compensated density is used as the vertical axis to establish an intersection lithology identification chart to identify mudstone, sandstone, gypsum sandstone and gypsum sandstone;

[0014] The ratio of density to the offset of natural potential from the baseline is used as the abscissa, and the compensated neutron is used as the ordinate to establish an intersection lithology identification chart to identify gypsum mudstone and argillaceous sandstone.

[0015] Furthermore, in the calculation of the mud content of the lithology of the gypsum-bearing reservoir, the uranium-free gamma curve was used to calculate the mud content of mudstone, sandstone and argillaceous sandstone respectively; for the gypsum-bearing sandstone, gypsum sandstone and gypsum mudstone, the difference NDC of the compensated neutron curve and the compensated density curve on the logging curve diagram was used to establish an exponential relationship with the mud content obtained from the core analysis experiment, and the mud content of the gypsum-bearing sandstone, gypsum sandstone and gypsum mudstone was calculated respectively using the modeling formula.

[0016] Furthermore, the formula for calculating the mud content without the uranium gamma curve is:

[0017]

[0018]

[0019] Where: V SH—KTH represents the shale content calculated by uranium-free gamma, %; KTHmin represents the uranium-free gamma value of mudstone, sandstone and pure sandstone without shale in the treated well section; KTHmax represents the uranium-free gamma value of pure mudstone in mudstone, sandstone and argillaceous sandstone in the treated well section; K represents the empirical coefficient related to formation characteristics;

[0020] The difference NDC formula between the compensated neutron curve and the compensated density curve on the logging curve diagram is:

[0021]

[0022] Where NDC represents the interval value of the neutron density curve on the well logging chart; DEN represents the density logging value, g / cm 3 ; 0.1 corresponds to the density curve scale of 1.95-2.95g / cm on the well logging chart 3 The size of each grid after 10 grids are divided; CNL represents the neutron logging value, %; 6% corresponds to the neutron curve scale on the logging chart -15%-45% The size of each grid after 10 grids are divided.

[0023] Furthermore, the final calculation formula for the mud content value is:

[0024] V SH =MAX(V SH-KTH ,V SH-NDC )

[0025] Among them, V SH-KTH represents the mud content calculated using uranium-free gamma; V SH-NDC Represents the mud content calculated using the NDC modeling method.

[0026] In a second aspect, the present invention further provides a system for calculating the mud content of a gypsum-containing reservoir, which is characterized by comprising:

[0027] Classification module, used to classify the lithology of gypsum-containing reservoirs and obtain classification experimental data results;

[0028] A curve determination module is used to perform depth regression and logging response characteristic analysis on the logging curve based on the classification test data results to determine the logging sensitivity curve reflecting the lithology;

[0029] A calculation module is used to establish a multi-parameter intersection lithology identification chart based on the logging sensitivity curve, identify the lithology of the gypsum-containing reservoir, calculate the mud content of the lithology of the gypsum-containing reservoir based on the identified lithology, and use the maximum mud content value obtained as the final mud content value.

[0030] In a third aspect, the present invention also provides a mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for calculating the mud content of the grease-containing reservoir when executing the computer program.

[0031] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for calculating the mud content of a gypsum-containing reservoir as described above.

[0032] Compared with the prior art, the present invention has the following beneficial technical effects:

[0033] The present invention provides a method, system, terminal and medium for calculating the mud content of a gypsum-containing reservoir. First, the lithology in the gypsum-containing reservoir is classified to obtain classification experimental data results; the classification test data results are subjected to depth regression and logging response characteristic analysis on a logging curve to determine a logging sensitivity curve reflecting the lithology; finally, a multi-parameter intersection lithology identification plate is established based on the logging sensitivity curve to identify the lithology of the gypsum-containing reservoir; and the mud content of the lithology of the gypsum-containing reservoir is obtained by corresponding calculation based on the identified lithology of the gypsum-containing reservoir. This solves the problem of uneven distribution of gypsum content in the gypsum-containing reservoir, improves the calculation accuracy and reliable theoretical basis of the mud content, and reduces the error in obtaining the mud content.

[0034] Furthermore, this method classifies the lithology of gypsum-bearing reservoirs and uses different calculation methods to calculate the shale content for different lithologies. The maximum value among the calculated results is ultimately selected as the final shale content value. This method has certain applicability and scalability for calculating the shale content of gypsum-bearing reservoirs, reducing calculation errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of the method of the present invention;

[0036] Figure 2 The lithology identification map of the six lithologies of the gypsum-bearing reservoir of the present invention;

[0037] Figure 3 This is a relationship diagram between the NDC of the present invention and the shale content index of core analysis;

[0038] Figure 4 A comprehensive well logging diagram of mud content according to an embodiment of the present invention;

[0039] Figure 5 This is a statistical diagram of the error between the mud content calculated by the present invention and the mud content analyzed by core experiments;

[0040] Figure 6 This is a system structure diagram of the present invention.

[0041] In the figure: 1. Classification module; 2. Curve determination module; 3. Calculation module. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] The present invention is described in further detail below with reference to the accompanying drawings:

[0044] The purpose of the present invention is to provide a method, system, terminal and medium for calculating the mud content of a gypsum-containing reservoir, so as to solve the technical problem in the prior art that the uneven distribution of gypsum content in the reservoir leads to large errors in calculating the mud content.

[0045] Example 1

[0046] See also Figure 1 In one embodiment of the present invention, a method for calculating the mud content of a gypsum-containing reservoir is provided, comprising the following steps:

[0047] Step 101: Based on rock thin section experimental data, the lithology of the gypsum-bearing reservoir is classified into six categories: sandstone, gypsum-bearing sandstone, gypsum sandstone, mudstone, mudstone, and gypsum mudstone;

[0048] Step 102: Depth-regressing the experimental data of rock thin sections of different lithologies in step 1) on conventional well logging curves, performing well logging response characteristic analysis, and determining a well logging sensitivity curve reflecting the lithology;

[0049] Step 103: Using the well logging sensitivity curve obtained in step 2), a multi-parameter intersection lithology identification chart is established to identify the six lithologies of sandstone, gypsum-containing sandstone, gypsum sandstone, muddy sandstone, mudstone, and gypsum mudstone in the gypsum-bearing reservoir;

[0050] Step 104: Based on the lithology identification, different shale content calculation methods are used for different lithologies. For mudstone, sandstone, and argillaceous sandstone reservoirs, the uranium-free gamma curve is used to calculate the shale content;

[0051] Step 105: For gypsum sandstone, gypsum sandstone, and gypsum mudstone reservoirs, an exponential relationship is established between the difference (NDC) between the compensated neutron curve and the compensated density curve on the well logging curve and the mud content obtained from the core analysis experiment, and the mud content is calculated using a modeling formula.

[0052] Step 106: Finally, the maximum value of the mud content calculated by the two methods is used as the final mud content value.

[0053] Below, the specific embodiments of this embodiment are further described in detail to support the technical problem to be solved by the present invention.

[0054] 1. Based on rock thin section experimental data, the lithology of gypsum reservoirs is divided into six categories: sandstone, gypsum-bearing sandstone, gypsum sandstone, mudstone, mudstone, and gypsum mudstone.

[0055] In this example, a gypsum-bearing reservoir in a block of a western oilfield was selected as the target layer for research. Core data and logging data of the target layer were collected, and 153 representative core samples were selected based on these data. In this example, the so-called representative cores refer to reservoir samples with different characteristics based on conventional logging, mud logging, coring description, special logging, etc.; their lithologic types were determined according to the standard process of "Rock Thin Section Identification (SY / T5368-2016)".

[0056] 2. Well logging response is a comprehensive reflection of lithology, porosity, pore structure, and fluid properties, and is particularly sensitive to the lithology of gypsum-bearing reservoirs. Analysis revealed that as the gypsum content of the reservoir increases, the uranium-free gamma value decreases, the acoustic transit time decreases, the compensated density increases, and the deep resistivity increases. As the reservoir shale content increases, the compensated neutron value increases, and the anomaly of the natural potential relative to the shale baseline decreases. Ultimately, six curves were selected as lithologic sensitivity curves: deep resistivity, uranium-free gamma, acoustic transit time, compensated neutron, compensated density, and natural potential.

[0057] 3. Using the determined logging sensitivity curve, a multi-parameter cross-plot was created for lithologic identification. Using "uranium-free gamma / resistivity" as the horizontal axis and "sonic transit time / compensated density" as the vertical axis, a cross-plot was created to identify mudstone, sandstone, gypsum-bearing sandstone, and gypsum sandstone. However, gypsum mudstone and argillaceous sandstone could not be distinguished. Using "density / spontaneous potential offset from baseline" as the horizontal axis and "compensated neutron" as the vertical axis, a cross-plot was created to identify gypsum mudstone and argillaceous sandstone. Figure 2 This is a lithology identification chart for six lithologies of gypsum-containing reservoirs according to an embodiment of the present invention.

[0058] 4. For the gypsum-containing reservoir in this embodiment, the mudstone, sandstone, and argillaceous sandstone in the formation do not contain gypsum or contain very little gypsum. The conventional uranium-free gamma curve is used to calculate the mud content.

[0059] The formula for calculating the mud content without uranium gamma curve is:

[0060]

[0061]

[0062] Where V SH—KTH represents the shale content calculated using uranium-free gamma ray, in %. KTHmin represents the uranium-free gamma ray value of pure sandstone containing no shale in the treated well section, including mudstone, sandstone, and argillaceous sandstone. KTHmax represents the uranium-free gamma ray value of pure mudstone in the treated well section, including mudstone, sandstone, and argillaceous sandstone. K represents an empirical coefficient related to formation properties; in this example, it is set to 2.3. The shale content of mudstone, sandstone, and argillaceous sandstone in the gypsum-bearing reservoir is calculated.

[0063] 5. For gypsum sandstone, gypsum sandstone, and gypsum mudstone in the formation, an exponential relationship is established between the difference (NDC) between the compensated neutron curve and the compensated density curve on the logging curve diagram and the mud content obtained from the core analysis experiment, and the mud content is calculated using the modeling formula.

[0064] The NDC formula for the difference between the compensated neutron curve and the compensated density curve on the logging curve is:

[0065]

[0066] Where NDC represents the interval value of the neutron density curve on the well logging chart; DEN represents the density logging value, g / cm 3 ; 0.1 corresponds to the density curve scale of 1.95-2.95g / cm on the well logging chart 3 The size of each grid after 10 grids are divided; CNL represents the neutron logging value, %; 6% corresponds to the neutron curve scale on the logging chart -15%-45% The size of each grid after 10 grids are divided.

[0067] The mud content obtained from the rock physics experiment is plotted against the NDC value, an exponential relationship is established, and the mud content is calculated using the modeling formula. Figure 3 The figure shows the relationship between the mud content and NDC index in core analysis. The calculation formulas for the mud content of gypsum sandstone, gypsum sandstone, and gypsum mudstone can be obtained:

[0068] V SH-NDC =5.1504×e 0.753×NDC (4)

[0069] 6. The method of calculating mud content using uranium-free gamma ray is only applicable to mudstone, muddy sandstone, and sandstone. Because gypsum-bearing sandstone, gypsum sandstone, and gypsum mudstone contain gypsum, which will reduce the value of the uranium-free gamma ray curve, the mud content calculated by this method is too low. The method of calculating mud content using NDC modeling is only applicable to gypsum-bearing sandstone, gypsum sandstone, and gypsum mudstone. Because mudstone, argillaceous sandstone, and sandstone do not contain gypsum and have low density values, the mud content calculated by this method is too low. Therefore, the maximum value of the mud content calculated by the two methods is used as the final mud content value. In this embodiment, the maximum value of the results calculated by formula (1) and formula (4) is selected as the final mud content value.

[0070] V SH =MAX(V SH-KTH ,V SH-NDC ) (5)

[0071] according to Figure 4 and Figure 5Shown are a comprehensive plot of single-well logging curves for a gypsum-bearing reservoir in a block of a western oilfield in this example, and an error analysis plot between the shale content calculated using the inventive method and the shale content obtained from rock physics experiments. As can be seen, the shale content calculated using this method in this example is essentially consistent with the results of core analysis, validating the reliability of this method and its ability to accurately calculate the shale content of gypsum-bearing reservoirs.

[0072] In summary, this embodiment provides a method for calculating the mud content of a gypsum-containing reservoir. First, based on rock thin section experimental data, the lithology in the gypsum-containing reservoir is divided into six categories: sandstone, gypsum-containing sandstone, gypsum sandstone, muddy sandstone, mudstone, and gypsum mudstone; the rock thin section experimental data of different lithologies are depth-relocated on the conventional logging curve, and the logging response characteristics are analyzed to determine the logging sensitivity curve reflecting the lithology, and finally the six curves of deep resistivity, uranium-free gamma, acoustic wave time difference, compensated neutron, compensated density, and natural potential are selected as the lithology sensitivity curves; using the obtained logging sensitivity curves, a multi-parameter intersection lithology identification plate is established to identify the six lithologies of sandstone, gypsum-containing sandstone, gypsum sandstone, muddy sandstone, mudstone, and gypsum mudstone in the gypsum-containing reservoir; on the basis of lithology identification, different mud content calculation methods are adopted for different lithologies. For mudstone, sandstone, and argillaceous sandstone reservoirs, uranium-free gamma curves are used to calculate shale content. For gypsum sandstone, gypsum sandstone, and gypsum mudstone reservoirs, an exponential relationship is established between the difference between the compensated neutron and compensated density curves on the well logging graph (NDC) and the shale content obtained from core analysis experiments. The shale content is then calculated using a modeling formula. The maximum value of the shale content calculated by the two methods is used as the final shale content value.

[0073] This embodiment solves the problem of low natural gamma logging values ​​for gypsum-bearing sandstone, gypsum sandstone, and gypsum mudstone in gypsum-bearing reservoirs due to uneven gypsum content distribution and interbedded layers of multiple lithologies. This problem also leads to significant discrepancies between the shale content calculated using a single natural gamma curve for the entire reservoir and experimental results. This method analyzes logging sensitivity curves for different lithologies, establishes a multi-parameter intersection lithology identification chart, and uses different calculation methods for different lithologies. The maximum value of the two calculation methods is taken as the final shale content value. This method has higher shale content calculation accuracy and a reliable theoretical basis, is convenient and concise in calculation, and has strong versatility and wide applicability.

[0074] Example 2

[0075] according to Figure 6 As shown, this embodiment provides a system for calculating the mud content of a gypsum-containing reservoir, comprising a classification module 1, a curve determination module 2, and a calculation module 3;

[0076] Classification module 1 is used to classify the lithology of the gypsum-containing reservoir and obtain classification experimental data results;

[0077] Curve determination module 2, for performing depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology;

[0078] Calculation module 3 is used to establish a multi-parameter intersection lithology identification chart based on the logging sensitivity curve, identify the lithology of the gypsum-containing reservoir, calculate the mud content of the lithology of the gypsum-containing reservoir based on the identified lithology, and use the maximum mud content value obtained as the final mud content value.

[0079] Example 3

[0080] The present invention also provides a mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a program for calculating the shale content of a gypsum-containing reservoir.

[0081] When the processor executes the computer program, the steps of the above-mentioned method for calculating the mud content of the gypsum-containing reservoir are implemented, for example:

[0082] Classify the lithology of gypsum-bearing reservoirs and obtain classification experimental data results;

[0083] Performing depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology;

[0084] A multi-parameter intersection lithology identification chart is established based on the logging sensitivity curve to identify the lithology of the gypsum-containing reservoir. The mud content of the lithology of the gypsum-containing reservoir is calculated based on the identified lithology of the gypsum-containing reservoir, and the maximum mud content obtained is used as the final mud content value.

[0085] Alternatively, when the processor executes the computer program, the functions of each module in the above system are realized, for example:

[0086] Classification module 1 is used to classify the lithology of the gypsum-containing reservoir and obtain classification experimental data results;

[0087] Curve determination module 2, for performing depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology;

[0088] Calculation module 3 is used to establish a multi-parameter intersection lithology identification chart based on the logging sensitivity curve, identify the lithology of the gypsum-containing reservoir, calculate the mud content of the lithology of the gypsum-containing reservoir based on the identified lithology, and use the maximum mud content value obtained as the final mud content value.

[0089] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the mobile terminal.

[0090] For example, the computer program can be divided into a classification module 1, a curve determination module 2, and a calculation module 3; the specific functions of each module are as follows:

[0091] Classification module, used to classify the lithology of gypsum-containing reservoirs and obtain classification experimental data results;

[0092] A curve determination module is used to perform depth regression and logging response characteristic analysis on the logging curve based on the classification test data results to determine the logging sensitivity curve reflecting the lithology;

[0093] A calculation module is used to establish a multi-parameter intersection lithology identification chart based on the logging sensitivity curve, identify the lithology of the gypsum-containing reservoir, calculate the mud content of the lithology of the gypsum-containing reservoir based on the identified lithology, and use the maximum mud content value obtained as the final mud content value.

[0094] The mobile terminal may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The mobile terminal may include, but is not limited to, a processor and a memory.

[0095] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the mobile terminal, and uses various interfaces and lines to connect various parts of the entire mobile terminal.

[0096] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the mobile terminal by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0097] The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0098] Example 4

[0099] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the method for calculating the mud content of a gypsum-containing reservoir are implemented.

[0100] If the module / unit integrated in the mobile terminal is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0101] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method by using a computer program to instruct relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method for calculating the shale content of a gypsum-containing reservoir. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form.

[0102] The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0103] It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the mud content of a gypsum-containing reservoir, characterized in that: include: Classify the lithology of gypsum-bearing reservoirs and obtain classification experimental data results; Performing depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology; A multi-parameter intersection lithology identification chart is established based on the logging sensitivity curve to identify the lithology of the gypsum-containing reservoir. The mud content of the lithology of the gypsum-containing reservoir is calculated based on the identified lithology of the gypsum-containing reservoir, and the maximum mud content obtained is used as the final mud content value.

2. The method for calculating the mud content of a gypsum-containing reservoir according to claim 1, characterized in that: The lithology of the gypsum-containing reservoir is classified by a rock thin section test method to obtain test data results, wherein the classification test data results include sandstone, gypsum-containing sandstone, gypsum sandstone, mudstone, mudstone and gypsum mudstone.

3. The method for calculating the mud content of a gypsum-containing reservoir according to claim 2, characterized in that: The classification test data results are subjected to depth regression and logging response characteristic analysis on the logging curve to determine the logging sensitivity curves including deep resistivity curve, uranium-free gamma curve, acoustic time difference curve, compensated neutron curve, compensated density curve and natural potential curve.

4. The method for calculating the mud content of a gypsum-containing reservoir according to claim 3, characterized in that: The specific process of establishing a multi-parameter intersection lithology identification chart based on the well logging sensitivity curve is as follows: The ratio of uranium-free gamma to resistivity is used as the horizontal axis, and the ratio of acoustic time difference to compensated density is used as the vertical axis to establish an intersection lithology identification chart to identify mudstone, sandstone, gypsum-bearing sandstone, and gypsum sandstone. The ratio of density to the offset of natural potential from the baseline is used as the abscissa, and the compensated neutron is used as the ordinate to establish an intersection lithology identification chart to identify gypsum mudstone and argillaceous sandstone.

5. The method for calculating the mud content of a gypsum-containing reservoir according to claim 4, characterized in that: In the calculation of the mud content of the lithology of the gypsum-bearing reservoir, the uranium-free gamma curve is used to calculate the mud content of the mudstone, sandstone and argillaceous sandstone respectively; for the gypsum-bearing sandstone, gypsum sandstone and gypsum mudstone, an exponential relationship is established between the difference NDC of the compensated neutron curve and the compensated density curve on the logging curve diagram and the mud content obtained from the core analysis experiment, and the mud content of the gypsum-bearing sandstone, gypsum sandstone and gypsum mudstone is calculated respectively using a modeling formula.

6. The method for calculating the mud content of a gypsum-containing reservoir according to claim 5, characterized in that: The formula for calculating the mud content using the uranium-free gamma curve is: Where: V SH—KTH represents the shale content calculated by uranium-free gamma, %; KTHmin represents the uranium-free gamma value of mudstone, sandstone and pure sandstone without shale in the treated well section; KTHmax represents the uranium-free gamma value of pure mudstone in mudstone, sandstone and argillaceous sandstone in the treated well section; K represents the empirical coefficient related to formation characteristics; The difference NDC formula between the compensated neutron curve and the compensated density curve on the logging curve diagram is: Where NDC represents the interval value of the neutron density curve on the well logging chart; DEN represents the density logging value, g / cm 3 ; 0.1 corresponds to the density curve scale of 1.95-2.95g / cm on the well logging chart 3 The size of each grid after 10 grids are divided; CNL represents the neutron logging value, %; 6% corresponds to the neutron curve scale on the logging chart -15%-45% The size of each grid after 10 grids are divided.

7. The method for calculating the mud content of a gypsum-containing reservoir according to claim 6, characterized in that: The final calculation formula for the mud content value is: V SH =MAX(V SH-KTH ,V SH-NDC ) Among them, V SH-KTH V represents the mud content calculated using uranium-free gamma ray; SH-NDC Represents the mud content calculated using the NDC modeling method.

8. A system for calculating the mud content of a gypsum-containing reservoir, characterized in that: include: Classification module (1), used to classify the lithology of the gypsum-containing reservoir and obtain classification experimental data results; A curve determination module (2) is used to perform depth regression and logging response characteristic analysis on the classification test data results on the logging curve to determine the logging sensitivity curve reflecting the lithology; A calculation module (3) is used to establish a multi-parameter intersection lithology identification chart based on the well logging sensitivity curve, identify the lithology of the gypsum-containing reservoir, calculate the mud content of the lithology of the gypsum-containing reservoir based on the identified lithology, and use the maximum mud content value obtained as the final mud content value.

9. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for calculating the mud content of a gypsum-containing reservoir according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for calculating the mud content of a gypsum-containing reservoir as claimed in any one of claims 1 to 7 are implemented.

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