Sandstone reservoir porosity quantitative prediction method under double-height and double-depth background

Abstract

The invention provides a sandstone reservoir porosity quantitative prediction method under a double-height and double-depth background, and relates to the technical field of oil-gas exploration and development, and the method comprises the steps: obtaining a rock sample of a target stratum of a research region; determining lithofacies types of sedimentary rocks in the research area; obtaining particle size distribution histograms of different lithofacies types through particle size analysis; measuring the pore size and the pore distribution type in the rock sample; determining the diagenesis type of the research area; establishing a statistical relationship between the porosity and the ground temperature gradient through the pore distribution type and the pore size; constructing a pressure field of the research area to obtain a pressure coefficient of the research area; and performing fitting superposition through the porosity, the lithofacies type, the particle size distribution histogram, the statistical relationship, the pressure coefficient and the diagenesis type, and predicting the porosity distribution range of the sandstone reservoir. By comprehensively considering multiple factors such as formation temperature, pressure, burial depth, diagenesis and the like, high-precision prediction of the reservoir porosity is realized.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration and development, and in particular to a method for quantitatively predicting the porosity of sandstone reservoirs under a double-height-double-depth background. Background Art

[0002] Porosity, as one of the key physical parameters for reservoir prediction and oilfield description, can provide a reliable reference for oil and gas prediction, reservoir quality assessment, and reserve calculation. Deepwater and deep burial conditions significantly influence reservoir diagenesis, pore structure, and fluid properties. Porosity prediction in sandstone reservoirs under the "double-high-double-depth" setting has become a key scientific issue. Obtaining porosity through coring and rock physics analysis is the most direct, accurate, and effective method, but it is unsuitable for large-scale application due to the high cost of sampling and testing. Therefore, pre-drilling reservoir porosity prediction is a hot topic and a difficult topic in oil and gas exploration. Summary of the Invention

[0003] The purpose of the present invention is to provide a sandstone reservoir porosity quantitative prediction method under the dual-height-dual-depth background in order to solve the problem that the existing reservoir porosity measurement method has high sampling and testing costs and is not suitable for large-scale application.

[0004] The above-mentioned purpose of this application is achieved through the following technical solutions: S1: Select the stratum with sandstone stratigraphic characteristics in the study area as the target stratum and obtain rock samples from the target stratum; S2: Determine the mineral content of rock samples and determine the lithofacies type of sedimentary rocks in the study area; S3: Obtain the particle size distribution histogram of different lithofacies types through particle size analysis; S4: Measure pore size and pore distribution in rock samples; use thin section observation and cathodoluminescence to determine the type of diagenesis in the study area; S5: Establish a statistical relationship between porosity and geothermal gradient through pore distribution type and pore size; S6: Construct the pressure field of the study area and obtain the pressure coefficient of the study area; S7: Predict the porosity distribution range of sandstone reservoirs by fitting and superimposing porosity, lithofacies type, grain size distribution histogram, statistical relationship, pressure coefficient, and diagenesis type.

[0005] Optionally, step S2 includes: X-ray diffraction analysis, scanning electron microscopy and EDS energy spectrum analysis techniques are used to determine the mineral composition of rock samples and obtain the mineral content; Based on the mineral content and sedimentary structural characteristics of the target strata, different sedimentary facies are identified and divided to obtain the lithofacies type.

[0006] Optionally, step S3 includes: Use a laser particle size analyzer or a sedimentation particle size analyzer to perform particle size analysis on rock samples and obtain particle size analysis results; Particle size analysis results include: particle size distribution curve and statistical parameters; Statistical parameters include: mean particle size, sorting, skewness, and kurtosis; Based on the results of particle size analysis, the particle size intervals of different lithofacies types were divided, the particle content in each interval was counted, and a particle size distribution histogram was drawn.

[0007] Optionally, step S4 includes: Using thin-section observation under a microscope, CT scanning or cathodoluminescence technology, the pore size and pore distribution type in rock samples are measured and recorded. Pore size includes pore diameter and pore shape.

[0008] Optionally, step S5 includes: Based on the thermal evolution model of the geological history, paleo-geothermal data and modern geothermal measurements, combined with the physical properties of rocks such as thermal conductivity and thermal diffusivity, the geothermal gradient of the study area is obtained; Based on the influence of geothermal gradient on mineral transformation, fluid activity and pore structure changes in rocks, combined with pore distribution type and pore size, a statistical relationship between porosity and geothermal gradient is established.

[0009] Optionally, step S6 includes: Using well logging data, seismic data, and geomechanical models, combined with fluid pressure measurements, the pressure field of the study area is obtained; the pressure field includes: hydrostatic pressure, formation pressure, and abnormal pressure distribution; The pressure coefficient is calculated based on the relationship between pressure and depth in the pressure field.

[0010] Optionally, step S7 includes: Formula for the surface ratio of primary pores:

[0011] Secondary porosity surface ratio formula:

[0012] Original porosity formula:

[0013] Where, represents the native pore porosity; represents secondary porosity; represents the original porosity; represents the geothermal gradient; Indicates the base value of geothermal gradient; represents the overpressure effect factor, which is obtained through the pressure coefficient; 、 Indicates the proportion of glauconite dissolution and feldspar dissolution; Indicates the dissolution benchmark value; represents the sorting coefficient; represents the regional calibration coefficient; represents the accelerating effect of temperature on compaction; Indicates overpressure storage capacity; It represents the pore destruction coefficient of cement; represents the glauconite dissolution enhancement coefficient; It represents the feldspar dissolution enhancement coefficient; Indicates high-temperature dissolution gain.

[0014] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs a method for quantitatively predicting the porosity of sandstone reservoirs under a dual-height-dual-depth background.

[0015] A computer-readable storage medium stores instructions. When the instructions are executed, a method for quantitatively predicting the porosity of a sandstone reservoir under a dual-height-dual-depth background is performed.

[0016] The beneficial effects of the technical solution provided by this application are: The present invention adopts a multi-factor analysis and regression fitting method to establish a quantitative prediction chart of the current porosity of sandstone reservoirs under the "double high-double depth" background according to different control factors, converts the microscopic control factors into controllable macroscopic factors and establishes an application model, which has important theoretical and practical significance for the prediction of porosity of sandstone before drilling under the "double high-double depth" background. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 This is a graph showing the change of carbonate content with depth in Area A of the Qiongdongnan Basin; Figure 2 This is a graph showing the change of cementation porosity with depth in Area A of the Qiongdongnan Basin; Figure 3 This is a scatter plot of differential dissolution of glauconite and feldspar; Figure 4 This is the original structural analysis map of Well A-1 in the Qiongdongnan Basin; Figure 5 This is the original structural analysis map of Well B-1 in the Qiongdongnan Basin; Figure 6 This is a diagram showing the variation of formation temperature with depth in Area A and Area B of the Qiongdongnan Basin; Figure 7 This is a diagram showing the variation of formation pressure coefficient with depth in Area A of the Qiongdongnan Basin; Figure 8 This is the primary pore prediction map under the background of high geothermal gradient; Figure 9 This is a prediction map of secondary porosity under the background of high geothermal gradient.

[0018] Figure 10 is a step diagram in an embodiment of the present application; Figure 11 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to have a clearer understanding of the technical features, purposes and effects of this application, the specific implementation methods of this application are now described in detail with reference to the accompanying drawings.

[0020] The embodiments of the present application provide a method for quantitatively predicting the porosity of sandstone reservoirs under a dual-height-dual-depth background.

[0021] Please refer to Figure 10 , Figure 10 This is a step diagram of a method for quantitatively predicting the porosity of sandstone reservoirs under a double-height-double-depth background in an embodiment of the present application, including: S1: Select the stratum with sandstone stratigraphic characteristics in the study area as the target stratum and obtain rock samples from the target stratum; As an example, for deepwater environments, deepwater sandstone reservoirs are selected as the research focus, and the application of oil and gas reservoirs in deepwater phase zones such as submarine fans and deepwater fan deltas is considered.

[0022] S2: Determine the mineral content of rock samples and determine the lithofacies type of sedimentary rocks in the study area; S3: Obtain the particle size distribution histogram of different lithofacies types through particle size analysis; S4: Measure pore size and pore distribution in rock samples; use thin section observation and cathodoluminescence to determine the type of diagenesis in the study area; S5: Establish a statistical relationship between porosity and geothermal gradient through pore distribution type and pore size; S6: Construct the pressure field of the study area and obtain the pressure coefficient of the study area; S7: Predict the distribution characteristics of deep pores by fitting and superimposing porosity, lithofacies type, grain size distribution histogram, statistical relationship, pressure coefficient and diagenesis type.

[0023] Step S2 includes: As an example, at high water depths and burial depths, the mineralogy and lithofacies of reservoir rocks may be influenced by unique hydrodynamic conditions and diagenetic environments. Therefore, when quantitatively characterizing mineral content, special attention should be paid to the types of minerals that may occur under these special conditions and their impact on reservoir quality. For example, common authigenic minerals in deepwater areas (such as glauconite) and common diagenetic minerals at high burial depths (such as illite) are common.

[0024] X-ray diffraction analysis, scanning electron microscopy and EDS energy spectrum analysis techniques are used to determine the mineral composition of rock samples and obtain the mineral content; Based on the mineral content and sedimentary structural characteristics of the target strata, different sedimentary facies are identified and divided to obtain the lithofacies type.

[0025] As an example, combining geological background data, analyzing sedimentary environment changes, and determining facies belt combination types, such as submarine fans, fan deltas and other deep-water sedimentary sand bodies, can help understand the sedimentary history and diagenesis process of the reservoir.

[0026] Step S3 includes: As an example, particle size distribution at high water depths and burial depths can be influenced by multiple factors, including sediment transport distance, hydrodynamic intensity, and diagenetic compaction. Therefore, particle size analysis must consider the impact of these factors on particle size distribution and use them to define different particle size intervals to more accurately reflect the reservoir's sedimentary environment and diagenetic history.

[0027] Use a laser particle size analyzer or a sedimentation particle size analyzer to perform particle size analysis on rock samples and obtain particle size analysis results; Particle size analysis results include: particle size distribution curve and statistical parameters; Statistical parameters include: mean particle size, sorting, skewness, and kurtosis; Based on the results of particle size analysis, the particle size intervals of different lithofacies types were divided, the particle content in each interval was counted, and a particle size distribution histogram was drawn.

[0028] Step S4 includes: Using thin-section observation under a microscope, CT scanning or cathodoluminescence technology, the pore size and pore distribution type in rock samples are measured and recorded. Pore size includes pore diameter and pore shape.

[0029] As an example, thin-section observation, cathodoluminescence, isotope dating and other means are used to identify and record the types of diagenetic processes in rocks, such as compaction, cementation, dissolution, etc., to clarify the transformation and impact of these processes on the pore structure. For example, high hydrostatic pressure in deep water areas will lead to enhanced compaction of the reservoir. As the water depth increases, the weight of the overlying sediments of the target layer gradually increases, causing the reservoir rock to be subjected to greater vertical pressure, thereby reducing the pore space; under deep water conditions, fluid activity is frequent and cements, such as calcite, are easily formed. These cements fill the pores and reduce the porosity.

[0030] Step S5 includes: Based on the thermal evolution model of the geological history, paleo-geothermal data and modern geothermal measurements, combined with the physical properties of rocks such as thermal conductivity and thermal diffusivity, the geothermal gradient of the study area is obtained; Based on the influence of geothermal gradient on mineral transformation, fluid activity and pore structure changes in rocks, combined with pore distribution type and pore size, a statistical relationship between porosity and geothermal gradient is established.

[0031] Step S6 includes: Using well logging data, seismic data, and geomechanical models, combined with fluid pressure measurements, the pressure field of the study area is obtained; the pressure field includes: hydrostatic pressure, formation pressure, and abnormal pressure distribution; The pressure coefficient is calculated based on the relationship between pressure and depth in the pressure field.

[0032] As an example, the pressure coefficient is used to divide the study area into a normal pressure zone and an abnormally high pressure zone.

[0033] Step S7 includes: As an example, based on acquired reservoir parameters (porosity, lithofacies type, facies combination type, grain size distribution histogram, statistical relationships, normal pressure zones, and abnormally high-pressure zones), the present invention established a quantitative prediction formula for sandstone reservoir porosity in a "double-high-double-depth" setting through multivariate nonlinear regression analysis. This formula comprehensively considers key controlling factors such as geothermal gradient, formation pressure coefficient, sorting coefficient, cement content, dissolution-induced porosity enhancement, and original porosity.

[0034] Formula for the surface ratio of primary pores:

[0035] Secondary porosity surface ratio formula:

[0036] Original porosity formula:

[0037] Where, represents the native pore porosity; represents secondary porosity; represents the original porosity; represents the geothermal gradient; Indicates the base value of geothermal gradient; represents the overpressure effect factor, which is obtained through the pressure coefficient; 、 Indicates the proportion of glauconite dissolution and feldspar dissolution; Indicates the dissolution benchmark value; represents the sorting coefficient; represents the regional calibration coefficient; represents the accelerating effect of temperature on compaction; Indicates overpressure storage capacity; It represents the pore destruction coefficient of cement; represents the glauconite dissolution enhancement coefficient; It represents the feldspar dissolution enhancement coefficient; Indicates high-temperature dissolution gain.

[0038] As an example, α=0.15-0.25 represents the accelerating effect of temperature on compaction; β=0.8-1.2 reflects the overpressure preservation capacity; γ=1.0-2.0 represents the pore destruction coefficient of the cement; δ=0.3-0.5 represents the high-temperature dissolution gain; μ=1.5 represents the glauconite dissolution enhancement coefficient; ω=1.0 represents the feldspar dissolution enhancement coefficient.

[0039] As an embodiment, the technical solution of the present application is used to quantitatively predict the porosity distribution range of sandstone reservoirs, providing more reliable parameters for oil and gas resource reservoir characteristic evaluation and effective exploration, development and evaluation, and specifically includes the following steps: Step S1: The study area is determined to be the Qiongdongnan Basin, and the sandstone reservoirs of the third member of the Lingshui Formation and the Meishan Formation in deepwater areas A and B are selected as target strata. The average water depth in area A is about 940m, and the average burial depth is about 4020m; the average water depth in area B is about 2000m, and the average burial depth is about 5200m; area A is mainly composed of primary pores, and area B is mainly composed of secondary pores.

[0040] Step S2: The mineral composition of the rock samples was accurately determined using thin section analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and ImageJ software. Area A primarily contains carbonate cements, and higher carbonate content correlates with a higher deep-cement porosity reduction, with the porosity reduction effect reaching up to 34%. Carbonate cementation has a destructive effect on porosity evolution. Area B primarily contains carbonates, feldspar, and the deepwater mineral glauconite. Dissolution of feldspar and glauconite is predominant, and the dissolution-induced porosity of these two minerals differs. The primary products of feldspar dissolution are quartz and clay minerals, resulting in relatively less effective pores. However, glauconite dissolution produces more effective pores, with a dissolution-induced porosity ratio approximately 1.5 times that of feldspar. Area A represents lowstand submarine fan deposits, while Area B represents fan delta front deposits.

[0041] Step S3: Use a laser particle size analyzer or a sedimentation particle size analyzer to analyze the particle size of the rock sample, and divide the particle size intervals based on the particle size analysis results, count the particle content in each interval, and draw a particle size distribution histogram or cumulative frequency curve. The particle size distribution of the submarine fan in the low-stand period of Area A is affected by the velocity of the submarine bottom current. It has good sorting, is mainly fine sandstone, and has good sorting. The main concentration is 1.3-1.4, and the original porosity of area A is set at 35%. The water flow distance at the fan delta front in area B is short, the particle size variation range is large, and the sorting is poor. It is mainly composed of siltstone, and the sorting is poor. (parameter The particle size probability curve (obtained by dividing the particle size value with a cumulative probability of 75% by the particle size value with a cumulative probability of 25%) is mainly concentrated in the range of 1.5-1.75, and the original porosity of area B is set at 30%. .

[0042] Step S4: Measure and record the distribution of pore types in the rock sample using thin-section observation under a microscope, CT scanning, or cathodoluminescence technology. Area A is primarily primary pores with good pore connectivity and straight edges; Area B is primarily secondary pores with poor pore connectivity and bay-like edges. Thin-section observation, cathodoluminescence, and isotope dating are used to identify and record the diagenetic type in the rock. The overlying stratum pressure of the target layer is the combined effect of seawater pressure and rock pressure. Compared to Area A, Area B has a higher average water depth and average burial depth after dewatering, resulting in greater vertical pressure and, therefore, stronger compaction. Furthermore, the porosity in Area A is also affected by cementation, while the porosity in Area B is influenced by both cementation and dissolution.

[0043] Step S5: Based on thermal evolution models from geological history, paleo-geothermal data, and modern geothermal measurements, combined with rock physical properties such as thermal conductivity and thermal diffusivity, the geothermal gradient of the study area was quantitatively restored. The geothermal gradient in Area A is 3.8°C / 100m, and in Area B is 4.1°C / 100m. Areas A and B are clearly defined as high geothermal gradient zones, separated by a geothermal gradient of 3.7°C / 100m. For primary pores, as the geothermal gradient increases, rock mineral particles expand due to heat, increasing contact pressure and accelerating rock compaction, gradually compressing and reducing primary pores. Furthermore, high geothermal temperatures promote the dissolution and reprecipitation of minerals in the rock, further altering the pore structure. For secondary pores, high geothermal gradients promote their formation. High geothermal gradients can also accelerate the thermal evolution of organic matter in source rocks, generating large amounts of acidic fluids such as organic acids and carbon dioxide, which promote the formation of secondary pores. At the same time, high temperatures accelerate fluid circulation, allowing dissolved substances to be removed more quickly, which in turn facilitates the formation of secondary pores and provides storage space. High temperatures increase the dissolution rate of rock minerals, making dissolution more intense and promoting the development of secondary pores. However, high temperatures also increase the likelihood of minerals in the rock cementing, filling pore spaces and reducing the preservation rate of secondary pores.

[0044] Step S6: Utilizing well logging data, seismic data, and geomechanical models, combined with fluid pressure measurements, the pressure field in the study area is quantitatively restored, including hydrostatic pressure, formation pressure, and abnormal pressure distribution. Based on the relationship between pressure and depth, the formation pressure coefficient is calculated, and the study area is divided into normal pressure zones and abnormally high pressure zones. Area A primarily experiences moderate overpressure (1.4 < pressure coefficient < 1.8). When the pressure coefficient is greater than 1.4, the overpressure's anti-compaction effect is more pronounced.

[0045] Step S7: Utilize the distribution characteristics of the determined samples in the study area, such as porosity, lithofacies type, grain size distribution, genesis, stratigraphic depth of the formation, terrace temperature, and formation pressure, to perform quantitative fitting and superposition, establish a quantitative prediction formula for the porosity (φ) of sandstone reservoirs under the "double-high-double-depth" background, fit the porosity trend line, perform quantitative characterization of the porosity, and predict the distribution characteristics of deep pores.

[0046] In the prediction of primary pores, the influence of pressure field, original structure and cement content on primary pores in Area A is comprehensively considered. The average pressure coefficient of Area A is 1.7, which has a good preservation effect on primary pores. If the pressure coefficient is higher, the preservation effect may be better. If the pressure coefficient is lower, the preservation effect on primary pores may not be obvious. Area A is mainly composed of fine sandstone, and the separation Between 1 and 1.5, the original structure is good, with an original porosity of 35%. If the grain size is larger, such as medium sandstone, the corresponding original porosity will be higher; if the grain size is smaller, such as siltstone, the corresponding original porosity will be lower. Cementation has a destructive effect on primary pores, and its content is negatively correlated with primary pores. Combining the above factors, the primary pore prediction formula and quantitative chart under the background of high geothermal gradient are formed ( Figure 8 ).

[0047] Formula for the surface ratio of primary pores:

[0048] Input parameters =35%, =3.8, =1.7, =15%, =0.2, =1.0, =1.5, calculate =12.15%, the measured primary porosity is 12.83%, with an error of 5.3%.

[0049] In the prediction of secondary pores, the influence of original structure, cement content and differential dissolution of cement on primary pores is comprehensively considered in Area B. Area B is mainly composed of siltstone. The density ranges from 1.5 to 1.75, indicating poor original fabric and an original porosity of 30%. The main types of dissolved minerals in Area B are feldspar and glauconite. The secondary porosity developed in the case of glauconite dissolution as the primary factor and feldspar dissolution as the secondary factor is better than that in the case of feldspar dissolution as the primary factor and glauconite dissolution as the secondary factor. Cement also has a destructive effect on secondary porosity, and its content is negatively correlated with secondary porosity. Combining the above factors, a prediction formula and quantitative chart for secondary porosity under high geothermal gradients were developed ( Figure 9 ).

[0050] Secondary porosity surface ratio formula:

[0051] Input parameters , , , ; , Indicates the dissolution benchmark value; , , Indicates the base value of geothermal gradient; , , , , ,calculate The measured secondary porosity is 15.6%, with an error of 5.8%.

[0052] This method comprehensively considers multiple geological factors in the context of "dual highs and dual depths." By introducing multiple coefficients and parameters (such as the pressure coefficient, sorting coefficient, and cement content in the context of high geothermal gradients), it enhances the applicability of the prediction model under different geological conditions and improves the accuracy and reliability of porosity prediction for sandstone reservoirs. This provides scientific porosity prediction methods and technical support for oil and gas exploration and development, helping to optimize development plans and increase exploration success rates.

[0053] This application also discloses an electronic device. Figure 11 , Figure 11 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0054] The communication bus 502 is used to implement the connection and communication between these components.

[0055] The user interface 503 may include a display screen, and the optional user interface 503 may also include a standard wired interface or a wireless interface.

[0056] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0057] The present application also discloses a computer-readable storage medium storing a plurality of instructions suitable for loading by a processor to execute the above-mentioned method for quantitatively predicting the porosity of sandstone reservoirs under a dual-height-dual-depth background.

[0058] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. In other words, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure.

[0059] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for quantitatively predicting the porosity of sandstone reservoirs under the background of double height and double depth, characterized in that: The method comprises the following steps: S1: Select the stratum with sandstone stratigraphic characteristics in the study area as the target stratum and obtain rock samples from the target stratum; S2: Determine the mineral content of rock samples and determine the lithofacies type of sedimentary rocks in the study area; S3: Obtain the particle size distribution histogram of different lithofacies types through particle size analysis; S4: Measure pore size and pore distribution in rock samples; use thin section observation and cathodoluminescence to determine the type of diagenesis in the study area; S5: Establish a statistical relationship between porosity and geothermal gradient through pore distribution type and pore size; S6: Construct the pressure field of the study area and obtain the pressure coefficient of the study area; S7: Predict the porosity distribution range of sandstone reservoirs by fitting and superimposing porosity, lithofacies type, grain size distribution histogram, statistical relationship, pressure coefficient, and diagenesis type.

2. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S2 includes: X-ray diffraction analysis, scanning electron microscopy and EDS energy spectrum analysis techniques are used to determine the mineral composition of rock samples and obtain the mineral content; Based on the mineral content and sedimentary structural characteristics of the target strata, different sedimentary facies are identified and divided to obtain the lithofacies type.

3. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S3 includes: Use a laser particle size analyzer or a sedimentation particle size analyzer to perform particle size analysis on rock samples and obtain particle size analysis results; Particle size analysis results include: particle size distribution curve and statistical parameters; Statistical parameters include: mean particle size, sorting, skewness, and kurtosis; Based on the results of particle size analysis, the particle size intervals of different lithofacies types were divided, the particle content in each interval was counted, and a particle size distribution histogram was drawn.

4. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S4 includes: Measure and record the pore size and pore distribution type in rock samples using thin-section observation under a microscope, CT scanning, or cathodoluminescence technology. Pore size includes pore diameter and pore shape. Types of diagenesis include compaction, cementation, and dissolution.

5. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S5 includes: Based on the thermal evolution model of the geological history, paleo-geothermal data and modern geothermal measurements, combined with the physical properties of rocks such as thermal conductivity and thermal diffusivity, the geothermal gradient of the study area is obtained; Based on the influence of geothermal gradient on mineral transformation, fluid activity and pore structure changes in rocks, combined with pore distribution type and pore size, a statistical relationship between porosity and geothermal gradient is established.

6. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S6 includes: Using well logging data, seismic data, and geomechanical models, combined with fluid pressure measurements, the pressure field of the study area is obtained; the pressure field includes: hydrostatic pressure, formation pressure, and abnormal pressure distribution; The pressure coefficient is calculated based on the relationship between pressure and depth in the pressure field.

7. The method for quantitatively predicting the porosity of sandstone reservoirs under the dual-height and dual-depth background according to claim 1, characterized in that: Step S7 includes: Formula for the surface ratio of primary pores: Secondary porosity surface ratio formula: Original porosity formula: Where, represents the native pore porosity; represents secondary porosity; represents the original porosity; represents the geothermal gradient; Indicates the base value of geothermal gradient; represents the overpressure effect factor, which is obtained through the pressure coefficient; 、 Indicates the proportion of glauconite dissolution and feldspar dissolution; Indicates the dissolution benchmark value; represents the sorting coefficient; represents the regional calibration coefficient; represents the accelerating effect of temperature on compaction; Indicates overpressure storage capacity; It represents the pore destruction coefficient of cement; represents the glauconite dissolution enhancement coefficient; It represents the feldspar dissolution enhancement coefficient; Indicates high-temperature dissolution gain.

8. An electronic device, characterized in that: The electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 7 is executed.

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