Method, device and storage medium for establishing physical properties and fluid rock physics quantities

Abstract

The application discloses a method and device for establishing a physical property and fluid rock physical quantity version and a storage medium. The method comprises the following steps: acquiring well logging data of a reservoir; determining a fluid sensitive parameter based on elastic parameters under different water saturation in a preset porosity range; determining a physical property sensitive parameter based on elastic parameters under different porosity in a preset water saturation range; constructing a rock physical model which can consider the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside the pores based on an equivalent embedded body stress average model and a standard linear body model; establishing a physical property prediction rock physical quantity version based on the rock physical model in combination with the physical property sensitive parameter, the porosity and the water saturation; and establishing a fluid prediction rock physical quantity version based on the rock physical model in combination with the fluid sensitive parameter, the porosity and the water saturation. The application can provide theoretical support for fine prediction of physical properties and fluids of an ultra-deep carbonate reservoir.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of rock physical analysis, and in particular to a method, apparatus, equipment and storage medium for establishing physical properties and fluid rock physical quantities. Background Technology

[0002] Global carbonate formations contain abundant oil and gas resources, making them a crucial area for oil and gas exploration and a realistic field for energy succession. In recent years, with the continuous advancement of exploration technology, carbonate oil and gas exploration has gradually moved towards deeper and ultra-deep formations. Following the definition used in drilling engineering, formations with a burial depth >4500m are considered deep, while those with a burial depth >6000m are considered ultra-deep. Ultra-deep carbonate reservoirs are characterized by their deep burial depth, old strata, and new physical environments such as high temperature and pressure, significant diagenesis, complex fluid phases, and widespread abnormal pressures. The oil and gas accumulation patterns are complex, and reservoir properties and fluids exhibit dramatic variations. Furthermore, the lack of understanding of the physical properties and variation patterns of ultra-deep carbonate reservoirs leads to significant uncertainties in reservoir seismic prediction and interpretation. This poses a major challenge to the high-precision characterization and description of ultra-deep oil and gas reservoirs, constituting a bottleneck restricting the progress of ultra-deep oil and gas exploration and development.

[0003] The pore structure of ultra-deep carbonate reservoirs exhibits strong heterogeneity, meaning that the pore parameters within the rock are non-uniform. Pores of different shapes and compressibility are embedded and distributed within the same rock framework, forming a complex pore structure. Differences in lithology and lithofacies may affect fluid migration over geological timescales, further leading to patchy saturation and non-uniform distribution of immiscible fluids. Therefore, research on the physical properties and fluid identification within partially saturated carbonate reservoirs is crucial for seismic exploration of oil and gas in these reservoirs.

[0004] Both pore structure and patchy fluid distribution heterogeneity are generally coexisting in reservoir rocks. For ultra-deep carbonate rocks, further research into the overlapping effects of these two heterogeneities is crucial. This is because, during the long hydrocarbon accumulation process, the low porosity and low permeability of these rocks more easily induce heterogeneity in the internal fluid distribution, resulting in less pronounced gas-water or oil-water differentiation. Current rock physics theories, models, and quantitative studies are lacking in addressing the simultaneous presence of pore structure and fluid heterogeneity in ultra-deep carbonate reservoirs, necessitating urgent research into relevant rock physics experiments, theories, and quantitative studies in this type of reservoir. Summary of the Invention

[0005] The purpose of this invention is to provide at least one method, apparatus, equipment, and storage medium for establishing physical properties and fluid rock physical quantities, which can at least solve the technical problems of complex seismic response, strong heterogeneity, complex gas-water relationship, and multiple solutions in gas content prediction for ultra-deep carbonate reservoirs.

[0006] To address the aforementioned technical problems, at least one embodiment of this application provides a method for establishing physical properties and fluid-rock physical quantities, comprising:

[0007] Acquire well logging data of the reservoir, wherein the well logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range;

[0008] The fluid-sensitive parameters are determined based on the elastic parameters under different water saturation levels within the preset porosity range.

[0009] Based on the elastic parameters under different porosities within the preset water saturation range, determine the physical property sensitive parameters;

[0010] A rock physics model that can consider the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores is constructed based on the equivalent embedded stress-averaged model and the standard linear body model.

[0011] Based on the rock physics model, a rock physics model for predicting physical properties is established by combining the physical property sensitive parameters, porosity, and water saturation; and a rock physics model for predicting fluid properties is established by combining the rock physics model, the fluid sensitive parameters, porosity, and water saturation.

[0012] At least one embodiment of this application also provides an apparatus for establishing physical properties and fluid-rock physical quantities, comprising:

[0013] The acquisition module is used to acquire well logging data of the reservoir, wherein the well logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range.

[0014] The first determining module is used to determine the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range.

[0015] The second determining module is used to determine the physical property sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range;

[0016] The model building module is used to build rock physics models that can take into account the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores, based on the equivalent embedded stress-averaged model and the standard linear body model.

[0017] The rock physics model construction module is used to build a rock physics model for predicting physical properties based on the rock physics model, combined with the physical property sensitive parameters, porosity, and water saturation; and to build a rock physics model for predicting fluid flow based on the rock physics model, combined with the fluid sensitive parameters, porosity, and water saturation.

[0018] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for establishing physical properties and fluid-rock physical quantities.

[0019] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for establishing physical properties and fluid-rock physical quantities.

[0020] The method for establishing physical properties and fluid rock physics models provided in the embodiments of this application is based on geological analysis of ultra-deep carbonate reservoirs in the target area, understanding the main controlling factors of reservoir development, selecting key strata, conducting well logging data analysis, revealing the sensitive parameters for predicting the physical properties and fluids of the reservoir, and constructing a rock physics model based on an equivalent embedded stress-averaged model and a standard linear model to simultaneously consider the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside the pores, thereby constructing a physical property and fluid prediction model applicable to ultra-deep carbonate reservoirs, which can provide theoretical support for the fine prediction of physical properties and fluids in ultra-deep carbonate reservoirs.

[0021] In some optional embodiments, the step of determining the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range includes:

[0022] The fluid indicator factor of each elastic parameter is calculated based on the elastic parameters under different water saturation levels within the preset porosity range.

[0023] The elastic parameter corresponding to the highest value of the fluid indicator factor is determined to be the fluid sensitive parameter.

[0024] Based on the principle of a single variable, the fluid-sensitive parameters are determined by calculating the fluid indicator factors corresponding to each elastic parameter, thereby ensuring the accuracy of the selection of fluid-sensitive parameters.

[0025] In some optional embodiments, the expression for the fluid indicator factor is:

[0026] The fluid indicator factor is equal to the ratio of the difference between the elastic modulus corresponding to saturation and the elastic modulus of different water saturation states to the elastic modulus corresponding to saturation.

[0027] In this way, the fluid indicator factor is used to quantitatively measure the relative changes in elastic properties after different fluids are saturated. By calculating the fluid indicator factor corresponding to each elastic parameter, the accuracy of the selection of fluid sensitive parameters is ensured.

[0028] In some optional embodiments, the step of determining the property-sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range includes:

[0029] The intersection relationship between each elastic parameter and the porosity is linearly fitted to obtain the fitting coefficient of each elastic parameter;

[0030] The elastic parameter corresponding to the highest value of the fitting coefficient is determined to be a material property sensitive parameter.

[0031] Based on the principle of single variable, the fitting coefficients of each elastic parameter are calculated to determine the material property sensitive parameters, thereby ensuring the accuracy of the selection of material property sensitive parameters.

[0032] In some optional embodiments, the step of constructing a rock physics model that takes into account the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids within the pores based on an equivalent embedded stress-averaged model and a standard linear body model includes:

[0033] The equivalent embedded stress-average model is extended across the entire frequency band using the standard linear body model, and a reservoir rock physics model is constructed based on Wood's theoretical equations.

[0034] In some optional embodiments, the step of extending the equivalent embedded volume stress-averaged model across the entire frequency band using a standard linear volume model and constructing a reservoir rock physics model based on Wood's theoretical equations includes:

[0035] The low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model of rock were determined using an equivalent embedded stress averaging model.

[0036] Based on the low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model, the reservoir's volumetric and shear complex modulus are obtained through a standard linear volume model.

[0037] Based on the volumetric and shear moduli, a reservoir rock physical model is obtained using Wood's theoretical equations.

[0038] In this embodiment, the equivalent embedded stress averaging model (i.e., the EIAS model) can effectively consider the relationship between the internal fracture properties of the rock and the elastic modulus. In order to study the relationship between the elastic modulus of the rock and the P-wave velocity with frequency, and to better apply the constructed model to well logging data, the EIAS model is extended to the full frequency band through the Zener body (standard linear body) model to obtain the EIAS-Zener model. Furthermore, based on the Wood theoretical equation, the EIAS-Zener model is extended into the EIAS-Zener-Wood model, which simultaneously considers the non-uniformity of the pore structure and the non-uniform distribution of immiscible fluids inside the pores.

[0039] In some optional embodiments, the step of acquiring reservoir logging data, wherein the logging data includes elastic parameters at different water saturations within a preset porosity range, and the step of acquiring elastic parameters at different porosities within a preset water saturation range further includes:

[0040] The number of elastic parameters under different porosity ranges is counted, and the porosity range with the largest number of elastic parameters is determined as the preset porosity range.

[0041] To ensure sufficient data volume, the porosity range with the largest number of elastic parameters is selected as the preset porosity range. Attached Figure Description

[0042] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0043] Figure 1 This is a flowchart of a method for establishing physical properties and fluid-rock physical quantities according to an embodiment of this application;

[0044] Figure 2 This is a flowchart of a method for establishing physical properties and fluid-rock physical quantities according to another embodiment of this application;

[0045] Figure 3 This is a flowchart illustrating the overall concept of a method for establishing physical properties and fluid-rock physical quantities according to another embodiment of this application;

[0046] Figure 4 This is another embodiment of the present application providing a dataset with substantially consistent porosity, and fluid indicator factor analysis results;

[0047] Figure 5 This is another embodiment of the present application providing a dataset with essentially consistent saturation, and the results of linear fitting coefficient analysis of the intersection relationship between porosity and rock physical parameters;

[0048] Figure 6 This is a schematic diagram of the double-double pore structure of the carbonate rock in the Welllight Shadow Formation reservoir provided in another embodiment of this application;

[0049] Figure 7 This is a flowchart illustrating the construction of a petrophysical model for an ultra-deep carbonate reservoir, provided in another embodiment of this application.

[0050] Figure 8 This is an embodiment of the present application providing a rock physical quantity version for predicting fluids in ultra-deep carbonate reservoirs and the result of well logging data correction;

[0051] Figure 9 This is another embodiment of the present application providing a rock physical quantity version for predicting the physical properties of ultra-deep carbonate reservoirs and the result of well logging data correction;

[0052] Figure 10 This is a schematic diagram of a device for establishing physical properties and fluid-rock physical quantities according to another embodiment of this application;

[0053] Figure 11 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application.

[0054] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0058] This invention proposes a method for establishing physical properties and fluid-rock physical scales. The implementation details of the method for establishing physical properties and fluid-rock physical scales in this embodiment are described below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0059] Example 1:

[0060] The method for establishing physical properties and fluid-fluid rock physical quantities in this embodiment is applied to ultra-deep carbonate reservoirs. The specific process of this method is as follows: Figure 1 As shown, it includes:

[0061] Step 110: Obtain reservoir logging data, wherein the logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range.

[0062] Specifically, the process involves acquiring logging data of the reservoir to be tested, preprocessing the logging data to determine a preset porosity range, obtaining elastic parameters under different water saturation levels within the preset porosity range, and determining a preset water saturation range to obtain elastic parameters under different porosity levels within the preset water saturation range.

[0063] In some embodiments, the logging data of the well to be tested also includes pore structure feature data, and the pore structure feature data is analyzed to determine the pore structure features of the reservoir rock.

[0064] Step 120: Determine the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range.

[0065] Specifically, fluid-sensitive parameters are determined to accurately assess the fluid properties in the reservoir. These parameters are determined based on elastic parameters at different water saturation levels within a preset porosity range. This means that fluid-sensitive parameters are analyzed using data with relatively consistent porosity, ensuring sufficient data while effectively reducing the influence of porosity as a factor. This adheres to the principle of single variable selection and improves the accuracy of fluid-sensitive parameter optimization.

[0066] In some cases, the sensitivity of each elastic parameter to changes in fluid properties is assessed by calculating the rate of change or difference of each elastic parameter at different water saturation levels. The elastic parameter that is most sensitive to changes in fluid properties is then selected as the fluid-sensitive parameter.

[0067] Step 130: Determine the physical property sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range.

[0068] Specifically, sensitive parameters are determined to accurately assess the physical properties of the reservoir. These sensitive parameters are determined based on elastic parameters under different porosities within a preset water saturation range. This means that sensitive parameters are analyzed using data with relatively consistent water saturation levels. This approach ensures sufficient data while effectively reducing the influence of water saturation, adhering to the principle of single variable selection and improving the accuracy of sensitive parameter selection.

[0069] In some cases, the sensitivity of each elastic parameter to changes in reservoir properties is assessed by calculating the rate of change or difference of each elastic parameter under different porosity ranges, and the elastic parameter most sensitive to changes in properties is selected as the property-sensitive parameter.

[0070] Step 140: Construct a rock physics model that can take into account the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores, based on the equivalent embedded stress-averaged model and the standard linear model.

[0071] Specifically, ultra-deep carbonate reservoirs exhibit strong heterogeneity in their pore structure, meaning that the pore parameters within the rock are non-uniform. Pores of different shapes and compressibility are embedded and distributed within the same rock framework, forming a complex pore structure. Differences in lithology and lithofacies may affect fluid transport over geological timescales, further leading to patchy saturation and non-uniform distribution of immiscible fluids. For ultra-deep carbonate reservoirs containing both pore structure and fluid heterogeneity, a rock physics model is constructed based on an equivalent embedded volume stress-averaged model and a standard linear volume model to account for both the heterogeneity of pore structure and the non-uniform distribution of immiscible fluids within the pores.

[0072] The constructed rock physics model can simultaneously consider the non-uniformity of the pore structure in the rock and the non-uniform distribution of immiscible fluids inside the pores, effectively characterizing the relationship between elastic properties, physical properties and fluid characteristics in the reservoir.

[0073] Step 150: Based on the reservoir rock physics model, establish a rock physics model for predicting physical properties by combining the physical property sensitive parameters, porosity, and water saturation; and based on the reservoir rock physics model, establish a rock physics model for predicting fluid flow by combining the fluid sensitive parameters, porosity, and water saturation.

[0074] Specifically, ultra-deep carbonate reservoirs exhibit strong heterogeneity in their pore structure, meaning that the pore parameters within the rock are non-uniform. Pores of different shapes and compressibility are embedded and distributed within the same rock framework, forming a complex pore structure. Differences in lithology and lithofacies may affect fluid migration over geological timescales, further leading to patchy saturation and non-uniform distribution of immiscible fluids. Therefore, research on the identification of physical properties and fluids within partially saturated carbonate reservoirs is crucial for seismic exploration of oil and gas in these reservoirs. Reservoir rock physics models are mathematical models used to describe the relationship between rock physical properties and reservoir parameters. These models consider factors such as mineral composition, pore structure, and fluid properties, and establish mathematical equations based on these factors to predict the reservoir's elastic parameters, including fluid-sensitive parameters and physical property-sensitive parameters.

[0075] Specifically, based on the rock physics model, the relationship between physical property sensitive parameters and porosity and water saturation can be determined. Therefore, based on the reservoir rock physics model, combined with physical property sensitive parameters, varying porosity and water saturation, a suitable physical property prediction rock physics model for the reservoir can be established. Similarly, based on the rock physics model, the relationship between fluid sensitive parameters and porosity and water saturation can be determined. Therefore, based on the rock physics model, combined with fluid sensitive parameters, varying porosity and water saturation, a suitable fluid prediction rock physics model for the reservoir can be established.

[0076] Both pore structure and patchy fluid distribution heterogeneity are generally coexisting in reservoir rocks. For ultra-deep carbonate rocks, further research into the overlapping effects of these two heterogeneities is crucial, as the low porosity and low permeability of these rocks during the long formation process easily induces heterogeneity in the internal fluid distribution, resulting in indistinct gas-water or oil-water differentiation. In this embodiment, addressing the simultaneous presence of pore structure and fluid heterogeneity in ultra-deep carbonate reservoirs, geological analysis of the target area's ultra-deep carbonate reservoirs is conducted to understand the main controlling factors of reservoir development, select key strata, and analyze well logging data to reveal the reservoir's physical properties and fluid prediction sensitive parameters. A rock physics model is constructed based on an equivalent embedded stress-averaged model and a standard linear model to simultaneously consider the heterogeneity of pore structure and the heterogeneous distribution of immiscible fluids within the pores. This leads to the development of a physical property and fluid prediction model applicable to ultra-deep carbonate reservoirs, providing theoretical support for the precise prediction of physical properties and fluids in ultra-deep carbonate reservoirs.

[0077] In some cases, the geological characteristics of ultra-deep carbonate reservoirs are characterized based on the acquired well logging data.

[0078] Specifically, understanding the geology of ultra-deep carbonate reservoirs provides direction for fundamental research in rock physics. Investigating the formation mechanisms and development patterns of key strata in ultra-deep carbonate reservoirs plays a crucial guiding role in selecting key strata for targeted fundamental rock physics research. Based on geological analysis of ultra-deep carbonate reservoirs in the target area, understanding the main controlling factors of reservoir development and selecting key strata provides more precise targets for fundamental rock physics research on these reservoirs, and also supports the better application of fundamental rock physics research results to the integrated geological-geophysical-engineering system.

[0079] In existing technologies, carbonate reservoirs are classified into three types based on three main controlling factors: facies-controlled, fault-controlled, and surface-controlled, as well as reservoirs controlled by a combination of two factors: facies-fault, facies-surface, surface-fault combined, and facies-surface-fault combined. Among these, facies-controlled carbonate reservoirs are the most common hydrocarbon accumulation sites. Facies-controlled carbonate reservoirs are widely found in high-energy sedimentary environments such as reefs, shoals, and flats, which are the most common areas for hydrocarbon concentration. These reservoirs typically exhibit a layered structure, with main reservoir spaces including intergranular pores, intergranular dissolution pores, intercrystalline pores, intercrystalline dissolution pores, biocavitary pores, and molding pores. Reservoir formation benefits from early syn-depositional exposure and burial dissolution, processes that may lead to the formation of small caverns. Furthermore, during burial, tectonic strain and hydraulic fracturing under overpressure may also generate a small number of fractures. The heterogeneity of these reservoirs is relatively low, mainly influenced by periodic changes in the sedimentary environment, with vertical heterogeneity greater than horizontal heterogeneity. The Feixianguan Formation reservoir in the Puguang Gas Field, the Changxing Formation reservoir in the Yuanba Gas Field, and the Leikoupo Formation reservoir in the Western Sichuan Gas Field in the Sichuan Basin are typical examples of this type.

[0080] This invention takes the Dengying Formation of Well A in the Yuanba area as an example. This section features siliceous dolomite and algal dolomite, and is generally a confined platform-intraplatform hill-shoal sedimentary environment. Based on the geological characteristics of this reservoir, facies-constrained petrophysical studies are conducted.

[0081] In some cases, the pore structure characteristics of ultra-deep carbonate reservoirs are analyzed based on the acquired well logging data.

[0082] Taking the Dengying Formation of Well A in the Yuanba area as an example, based on the obtained logging data, it can be seen that Well A in the Yuanba area encountered a platform-margin hilly algal dolomite porous reservoir with a cumulative reservoir thickness of 259m. The lithology of this section is a single dolomite reservoir rock, mainly composed of dolomite, solution-porous dolomite, algal dolomite, etc.

[0083] Dolomite: Gray to light gray, mainly composed of dolomite, with small amounts of silica and bituminous material. Dolomite content is approximately 92%-95%, and silica and bituminous material content is approximately 5%-8%. It has a fine-grained to microcrystalline structure, with locally visible elliptical algal grains and spherules. Silica and bituminous material are unevenly distributed, appearing as star-shaped spots, clumps, and bands. Porous Dolomite: Light gray, mainly composed of dolomite, with small amounts of silica and argillaceous material. Dolomite content is approximately 90%-95%, and silica and argillaceous material content is approximately 5%-10%. It has a fine-grained structure, with unevenly distributed silica and argillaceous material appearing as star-shaped spots and clumps. Algal Dolomite: Gray to dark gray, with mineral composition mainly consisting of 80% dolomite, 15% argillaceous material, and 5% silica. It has an algal grain structure, with small algal grains concentrated in clumps, and locally visible elliptical algal grains and spherules. Aryl and silica are unevenly distributed, and cracks are not well developed.

[0084] Structural characteristics: Microscopic observation of thin sections reveals well-developed dissolution pores between algal grains, filled with black bitumen and quartz particles. Fractures are also well-developed, filled with black bitumen, quartz, and calcite. Thin sections of the reservoir rock show relatively well-developed pores and fractures, with carbonaceous bitumen either unfilled or completely filled.

[0085] Based on the analysis of the microscopic pore structure characteristics of the rocks, the background phase medium is mainly dolomite, containing a small amount of silica and bituminous material. The rocks contain algal framework dissolution pores, residual intragranular and intergranular dissolution pores, small algal caves, and microfractures. Therefore, the Dengying Formation reservoir carbonate rocks have a dual (structural heterogeneity) and dual (fluid distribution heterogeneity) pore structure.

[0086] Example 2:

[0087] Based on the above embodiments, the step of determining the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range includes:

[0088] The fluid indicator factor of each elastic parameter is calculated based on the elastic parameters under different water saturation levels within the preset porosity range.

[0089] The elastic parameter corresponding to the highest value of the fluid indicator factor is determined to be the fluid sensitive parameter.

[0090] Specifically, based on the elastic parameters under different water saturation levels within a preset porosity range, a column corresponding to the saturation level is determined. The entire dataset is sorted in ascending order by the data in this column. Starting from a water saturation level of 0%, the dataset (i.e., each elastic parameter) is divided into multiple subsets at 10% intervals. The mean value of the data in each subset is calculated, and the fluid indicator factor of each elastic parameter is calculated. The elastic parameter corresponding to the highest value of the fluid indicator factor is selected as the fluid sensitive parameter.

[0091] Among them, the fluid indicator factor is a parameter that quantitatively characterizes the relative changes in elastic properties after saturation with different fluids. For fluid-saturated rocks with different properties, the elastic parameters and their combinations change differently after saturation with different fluids. Elastic parameters and their combinations with large changes can be selected as knowledge factors for identifying different pore fluids.

[0092] In some embodiments, the expression for the fluid indicator factor is:

[0093] The fluid indicator factor is equal to the ratio of the difference between the elastic modulus corresponding to saturation and the elastic modulus of different water saturation states to the elastic modulus corresponding to saturation.

[0094] Specifically, the fluid indicator factor is calculated according to the following formula:

[0095] Fluid indicator factor = (elastic modulus corresponding to saturation - elastic modulus of different water saturation states) / elastic modulus corresponding to saturation.

[0096] In some embodiments, the step of determining the physical property sensitive parameter based on the elastic parameter under different porosities within the preset water saturation range includes:

[0097] The intersection relationship between each elastic parameter and the porosity is linearly fitted to obtain the fitting coefficient of each elastic parameter;

[0098] The elastic parameter corresponding to the highest value of the fitting coefficient is determined to be a material property sensitive parameter.

[0099] Specifically, based on the elastic parameters under different porosity ranges within a preset water saturation range, each elastic parameter is intersected with the porosity, and the intersecting relationship is linearly fitted. The elastic parameter corresponding to the highest fitting coefficient is then identified as the physical property sensitive parameter.

[0100] In some embodiments, the step of acquiring reservoir logging data, wherein the logging data includes elastic parameters at different water saturations within a preset porosity range, and the step of acquiring elastic parameters at different porosities within a preset water saturation range further includes:

[0101] The number of elastic parameters under different porosity ranges is counted, and the porosity range with the largest number of elastic parameters is determined as the preset porosity range.

[0102] Specifically, the logging data of the well to be tested includes various elastic parameters within different porosity ranges. The preset porosity range is determined by the number of elastic parameters within each porosity range. Furthermore, the porosity range with the largest number of elastic parameters is preferred as the preset porosity range.

[0103] Example 3:

[0104] Based on the above embodiments, the steps of constructing a rock physics model that considers the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores based on the equivalent embedded stress averaging model and the standard linear body model include:

[0105] The equivalent embedded stress-average model is extended across the entire frequency band using the standard linear body model, and a reservoir rock physics model is constructed based on Wood's theoretical equations.

[0106] In some embodiments, the step of extending the equivalent embedded volume stress-averaging model across the entire frequency band using a standard linear volume model and constructing a reservoir rock physics model based on Wood's theoretical equations includes:

[0107] The low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model of rock were determined using an equivalent embedded stress averaging model.

[0108] Based on the low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model, the reservoir's volumetric and shear complex modulus are obtained through a standard linear volume model.

[0109] Based on the volumetric and shear moduli, a reservoir rock physical model is obtained using Wood's theoretical equations.

[0110] Specifically, the EIAS (Equivalent Inlay Stress Average) model approximates the micropore structure of porous rocks as a combination of spherical hard pores and coin-shaped fractures, with a porosity of φ (φ = φ). s +φ c ), φ s It refers to the porosity of hard pores, φ c It refers to the porosity of soft pores. Furthermore, the aspect ratio of the fracture is 'a', and 'c' is the fracture volume ratio (c = φ). c / φ), this parameter is related to the properties of soft and hard pores. The high-frequency volumetric and shear moduli in rocks are (Endres and Knight, 1997):

[0111]

[0112] in

[0113] γ=(1-c)P1+cP2, χ=(1-c)Q1+cQ2, (3)

[0114]

[0115] P1 and Q1 correspond to spherical hard pores, while P2 and Q2 correspond to coin-shaped fissures.

[0116] When the fluid pressure reaches equilibrium throughout the pore space, the low-frequency effective modulus is (Endres and Knight, 1997).

[0117]

[0118] Here

[0119] γ0=(1-c)P 01 +cP 02 ,χ0=(1-c)Q 01 +cQ 02 (9)

[0120]

[0121] The above equation is applicable to the calculation of the effective modulus of a spherical hard hole at low frequencies.

[0122] The EIAS models mentioned above can effectively consider the relationship between the internal fracture properties of rocks and their elastic modulus. However, these models cannot currently study the relationship between the obtained elastic modulus and the frequency across the entire frequency band, and therefore cannot be well used to study the viscoelastic characteristics of complex reservoirs. To investigate the relationship between the elastic modulus and P-wave velocity of rocks as a function of frequency, and to better apply the constructed model to well logging data, this embodiment extends the EIAS model to the entire frequency band using the Zener volume (standard linear volume) model, resulting in the EIAS-Zener model. The specific process is described below:

[0123] The Zener model describes frequency-dependent elastic wave dispersion and attenuation, satisfying the Kramers-Kronig relation. Based on the EIAS model, the volumetric / shear modulus of rock at low and high frequencies is obtained. and Using the Zener volume (standard linear volume) model, this embodiment obtains the minimum values ​​corresponding to the quality factors of rock volume and shear modulus, as follows:

[0124]

[0125] The volumetric and shear modulus are:

[0126]

[0127] in, f0 is the frequency corresponding to the attenuation peak, and f is the frequency. When f→∞, and And when f→0 and

[0128] It is worth noting that the formulas for calculating volumetric and shear complex moduli here also apply to other moduli, such as Young's modulus Y or longitudinal wave modulus E, by replacing K in formula (13) with Y or E, etc. All the above elastic moduli are represented by M. The real modulus and quality factor of the elastic modulus of complex reservoirs are:

[0129]

[0130] Given the density of the rock, the complex P-wave and S-wave velocities related to the frequency can be calculated based on the obtained complex volume and shear modulus.

[0131]

[0132] Where ρ is the density of the rock. When v P or v S When expressed as v, the phase velocity corresponding to the body wave is further obtained as:

[0133]

[0134] Through the above steps, the elastic parameters corresponding to ultra-deep carbonate reservoirs under water-saturated and gas-saturated conditions can be obtained in this embodiment. In fractured reservoirs at the logging scale, the permeability and fluid transport capacity of the rock are often much higher than at the microscale. The pore pressure may be balanced when elastic waves pass through. Therefore, Wood's theoretical equations can be used to calculate the elastic modulus of the mixed fluid (Mavko et al., 2009).

[0135] 1 / M Mix =S Gas / M Gas +S Water / M Water (18)

[0136] Therefore, in this embodiment, the EIAS model is extended across the entire frequency band using the Zener model to obtain the EIAS-Zener model, which is applicable across the entire frequency band and can take into account the heterogeneity of pore structure. Furthermore, based on the Wood theoretical equations, the EIAS-Zener model is extended into the EIAS-Zener-Wood model, which simultaneously considers the heterogeneity of pore structure and the non-uniform distribution of immiscible fluids inside the pores, i.e., the reservoir rock physics model.

[0137] Example 4:

[0138] Another embodiment of this application relates to a method for establishing rock physical quantities for predicting the physical properties and fluids of ultra-deep carbonate reservoirs, applied to the Dengying Formation of Well A in the Yuanba area. The specific process is as follows: Figure 2 and Figure 3 As shown, it includes:

[0139] Step (1): Characterization of the geological features of ultra-deep carbonate reservoirs.

[0140] Specifically, understanding the geology of ultra-deep carbonate reservoirs provides direction for fundamental research in rock physics. Investigating the formation mechanisms and development patterns of key strata in ultra-deep carbonate reservoirs plays a crucial guiding role in selecting key strata for targeted fundamental rock physics research. Based on geological analysis of ultra-deep carbonate reservoirs in the target area, understanding the main controlling factors of reservoir development and selecting key strata provides more precise targets for fundamental rock physics research on these reservoirs, and also supports the better application of fundamental rock physics research results to the integrated geological-geophysical-engineering system.

[0141] He et al. (2023) proposed classifying carbonate reservoirs into three types based on three main controlling factors: facies-controlled, fault-controlled, and surface-controlled, as well as reservoirs controlled by two factors in combination: facies-fault, facies-surface, surface-fault, and facies-surface-fault. Among these, facies-controlled carbonate reservoirs are the most common hydrocarbon accumulation sites. Facies-controlled carbonate reservoirs are widely found in high-energy sedimentary environments such as reefs, shoals, and flats, which are the most common areas for hydrocarbon accumulation. These reservoirs typically exhibit a layered structure, with main reservoir spaces including intergranular pores, intergranular dissolution pores, intercrystalline pores, intercrystalline dissolution pores, biocavitary pores, and molding pores. Reservoir formation benefits from early syn-depositional exposure and burial dissolution, processes that may lead to the formation of small caverns. Furthermore, during burial, tectonic strain and hydraulic fracturing under overpressure may also generate a small number of fractures. The heterogeneity of these reservoirs is relatively low, mainly influenced by periodic changes in the sedimentary environment, with vertical heterogeneity greater than horizontal heterogeneity. The Feixianguan Formation reservoir in the Puguang Gas Field, the Changxing Formation reservoir in the Yuanba Gas Field, and the Leikoupo Formation reservoir in the Western Sichuan Gas Field in the Sichuan Basin are typical examples of this type.

[0142] This embodiment takes the Dengying Formation of Well A in the Yuanba area as an example. This section features siliceous dolomite and algal dolomite, and is generally a restricted platform-intraplatform hill-shoal sedimentary environment. Based on the geological characteristics of this reservoir, facies-constrained petrophysical studies are conducted.

[0143] Step (2): Optimize the physical properties and fluid-rock physics sensitive parameters of ultra-deep carbonate reservoirs.

[0144] In this embodiment, well logging data from Well A in the Yuanba area was acquired. First, the logging data was preprocessed. Then, a process was employed to analyze fluid-sensitive parameters based on data volume across different porosity ranges, prioritizing the range with the most data, and ensuring consistent porosity. This approach ensured sufficient data volume while effectively reducing the influence of porosity, maintaining the principle of a single variable and improving the accuracy of fluid-sensitive parameter selection. Based on the acquired dataset, the column corresponding to saturation was identified. The entire dataset was sorted in ascending order by this column, starting from 0% water saturation and dividing into 10 subsets at 10% intervals. The mean of the data in each subset was calculated, and the fluid indicator factor was calculated using the following formula: Fluid indicator factor = (Elastic modulus corresponding to saturation - Elastic modulus at different water saturation states) / Elastic modulus corresponding to saturation. Therefore, the elastic parameter corresponding to the highest value of the fluid indicator factor was selected as the fluid-sensitive parameter. The results are as follows: Figure 4 As shown, the highest value of λρ is 0.065, which is the most fluid-sensitive elastic parameter among the 10 types of elastic parameters analyzed.

[0145] Similarly, a dataset with relatively consistent saturation can be obtained. Based on this dataset, the elastic parameters and porosity are intersected, and the intersecting relationship is linearly fitted to obtain the elastic parameter with the highest fitting coefficient. The results are as follows: Figure 5 As shown, the highest value of Ip is 0.56, which is the most sensitive parameter to physical properties among the 12 types of rock physical parameters analyzed.

[0146] Step (3): Analysis of the pore structure characteristics of ultra-deep carbonate reservoirs.

[0147] Well A in the Yuanba area encountered a platform-margin hilly dolomite porous reservoir with a cumulative thickness of 259m. The lithology of this section is a single dolomite reservoir, mainly consisting of dolomite, solution-porous dolomite, and algal dolomite.

[0148] Dolomite: Gray to light gray, mainly composed of dolomite, with small amounts of silica and bituminous material. Dolomite content is approximately 92%-95%, and silica and bituminous material content is approximately 5%-8%. It has a fine-grained to microcrystalline structure, with locally visible elliptical algal grains and spherules. Silica and bituminous material are unevenly distributed, appearing as star-shaped spots, clumps, and bands. Porous Dolomite: Light gray, mainly composed of dolomite, with small amounts of silica and argillaceous material. Dolomite content is approximately 90%-95%, and silica and argillaceous material content is approximately 5%-10%. It has a fine-grained structure, with unevenly distributed silica and argillaceous material appearing as star-shaped spots and clumps. Algal Dolomite: Gray to dark gray, with mineral composition mainly consisting of 80% dolomite, 15% argillaceous material, and 5% silica. It has an algal grain structure, with small algal grains concentrated in clumps, and locally visible elliptical algal grains and spherules. Aryl and silica are unevenly distributed, and cracks are not well developed.

[0149] Structural characteristics: Microscopic observation of thin sections reveals well-developed dissolution pores between algal grains, filled with black bitumen and quartz particles. Fractures are also well-developed, filled with black bitumen, quartz, and calcite. Thin sections of the reservoir rock show relatively well-developed pores and fractures, with carbonaceous bitumen either unfilled or completely filled.

[0150] Based on the analysis of the microscopic pore structure characteristics of the rocks, the background phase medium is mainly dolomite, containing small amounts of silica and bituminous materials. The rocks contain algal framework dissolution pores, residual intragranular and intergranular dissolution pores, small algal caves, and microfractures. Therefore, the Dengying Formation reservoir carbonate rocks have a dual (structurally heterogeneous) and dual (fluid distribution heterogeneous) pore structure, as shown in the schematic diagram. Figure 6 As shown.

[0151] Step (4): Constructing a petrological model of ultra-deep carbonate reservoirs that simultaneously considers the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside the pores.

[0152] The EIAS (Equivalent Inlay Stress Average) model approximates the micropore structure of porous rocks as a combination of spherical hard pores and coin-shaped fractures, with a porosity of φ (φ = φ). s +φ c ), φ s It refers to the porosity of hard pores, φ c It refers to the porosity of soft pores. Furthermore, the aspect ratio of the fracture is 'a', and 'c' is the fracture volume ratio (c = φ). c / φ), this parameter is related to the properties of soft and hard pores. The high-frequency volumetric and shear moduli in rocks are (Endres and Knight, 1997):

[0153]

[0154] in

[0155] γ=(1-c)P1+cP2, χ=(1-c)Q1+cQ2, (3)

[0156]

[0157] P1 and Q1 correspond to spherical hard pores, while P2 and Q2 correspond to coin-shaped fissures.

[0158] When the fluid pressure reaches equilibrium throughout the pore space, the low-frequency effective modulus is (Endres and Knight, 1997).

[0159]

[0160] Here

[0161] γ0=(1-c)P 01 +cP02 ,χ0=(1-c)Q 01 +cQ 02 (9)

[0162]

[0163] The above equation is applicable to the calculation of the effective modulus of a spherical hard hole at low frequencies.

[0164] The EIAS models mentioned above can effectively consider the relationship between the internal fracture properties of rocks and their elastic modulus. However, these models cannot currently study the relationship between the obtained elastic modulus and the frequency across the entire frequency band, and therefore cannot be well used to study the viscoelastic characteristics of complex reservoirs. To investigate the relationship between the elastic modulus and P-wave velocity of rocks as a function of frequency, and to better apply the constructed model to well logging data, this invention extends the EIAS model to the entire frequency band using the Zener volume (standard linear volume) model, resulting in the EIAS-Zener model. The specific process is described below:

[0165] The Zener model describes frequency-dependent elastic wave dispersion and attenuation, satisfying the Kramers-Kronig relation. Based on the EIAS model, the volumetric / shear modulus of rock at low and high frequencies is obtained. and Using the Zener volume (standard linear volume) model, this paper obtains the minimum values ​​of the quality factors corresponding to the volume and shear modulus of the rock, as follows:

[0166]

[0167] The volume and shear modulus are

[0168]

[0169] in, f0 is the frequency corresponding to the attenuation peak, and f is the frequency. When f→∞, and And when f→0 and

[0170] It is worth noting that the formulas for calculating the volumetric and shear complex moduli here also apply to other moduli, such as Young's modulus Y or longitudinal wave modulus E, by replacing K in formula (13) with Y or E, etc. All the above elastic moduli are represented by M, then the real modulus and quality factor of the elastic modulus of complex reservoirs are...

[0171]

[0172] Given the density of the rock, the complex P-wave and S-wave velocities related to the frequency can be calculated based on the obtained complex volume and shear modulus.

[89] ,

[0173]

[0174] Where ρ is the density of the rock. When v P or v S When expressed as v, the phase velocity corresponding to the body wave is further calculated as follows:

[0175]

[0176] Through the above steps, the present invention can obtain the elastic parameters corresponding to ultra-deep carbonate reservoirs under water-saturated and gas-saturated conditions. In fractured reservoirs at the logging scale, the permeability and fluid transport capacity of the rock are often much higher than at the microscale. The pore pressure may be balanced when elastic waves pass through. Therefore, Wood's theoretical equations can be used to calculate the elastic modulus of the mixed fluid (Mavko et al., 2009).

[0177] 1 / M Mix =S Gas / M Gas +S Water / M Water (18)

[0178] Therefore, this study extends the EIAS model across the entire frequency band using the Zener model, resulting in an EIAS-Zener model applicable to the entire frequency band, which can consider the heterogeneity of pore structure. Furthermore, based on Wood's theoretical equations, the EIAS-Zener model is extended into an EIAS-Zener-Wood model that simultaneously considers the heterogeneity of pore structure and the non-uniform distribution of immiscible fluids within the pores. The flowchart of the model construction is as follows... Figure 7 As shown.

[0179] Step (5): Establish rock physical quantities for predicting the physical properties and fluids of ultra-deep carbonate reservoirs.

[0180] Based on the selected sensitive parameters for fluid prediction in the target reservoir of Well A, and by varying porosity and saturation, a fluid prediction petrophysical quantity model applicable to this reservoir was established. The results are as follows: Figure 8 As shown, the log data of the gauge and the well A lamp shadow group are consistent. As the porosity increases, ρ, Ip^2-c·Is^2 and λρ all decrease; as the water saturation increases, ρ, Ip^2-c·Is^2 and λρ all increase. This gauge provides a basis for reservoir fluid prediction.

[0181] Based on the selected sensitive parameters for predicting the physical properties of the discontinuous reservoir in Well A, and by varying porosity and saturation, a rock physical quantity scale for predicting the physical properties of this reservoir was established. The results are as follows: Figure 9 As shown, the log data of the gauge and the well A lamp shadow group are consistent. As porosity increases, ρ, Ip and Vp all decrease; as water saturation increases, ρ, Ip and Vp all increase. This gauge provides a basis for reservoir prediction.

[0182] In this embodiment, based on the geological understanding of ultra-deep carbonate reservoirs, the main controlling factors of reservoir development are identified, key strata are selected, and well logging data analysis is conducted to reveal the reservoir's physical properties and fluid prediction sensitive parameters. This study extends the EIAS model across the entire frequency band using the Zener model, resulting in an EIAS-Zener model applicable to the entire frequency band, which can consider the heterogeneity of pore structure. Furthermore, based on Wood's theoretical equations, the EIAS-Zener model is extended to an EIAS-Zener-Wood model that simultaneously considers the heterogeneity of pore structure and the non-uniform distribution of immiscible fluids within the pores. This model is well-suited for ultra-deep carbonate reservoirs containing both pore structure and fluid heterogeneity. Based on the constructed rock physics model, a physical property and fluid prediction template applicable to ultra-deep carbonate reservoirs is established, and the applicability of the template is verified using well logging data from the reservoir. This embodiment is characterized by its strong focus and innovation; the established template can provide theoretical support for the precise prediction of physical properties and fluids in ultra-deep carbonate reservoirs.

[0183] Example 5:

[0184] Another embodiment of this application relates to a device for establishing physical properties, fluids, and rocks. The implementation details of this embodiment's physical properties, fluids, and rocks are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of this embodiment's physical properties, fluids, and rocks can be seen as follows: Figure 10 As shown, it includes an acquisition module 801, a first determination module 802, a second determination module 803, a model construction module 804, and a rock physical quantity plate construction module 805.

[0185] The acquisition module 801 is used to acquire well logging data of the reservoir, wherein the well logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range.

[0186] The first determining module 802 is used to determine the fluid sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range.

[0187] The second determining module 803 is used to determine the physical property sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range.

[0188] Model building module 804 is used to build a rock physics model that can take into account the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores, based on the equivalent embedded stress-averaged model and the standard linear body model.

[0189] The rock physics model construction module 805 is used to build a rock physics model for predicting physical properties based on the rock physics model, combined with the physical property sensitive parameters, porosity and water saturation; and to build a rock physics model for predicting fluid based on the rock physics model, combined with the fluid sensitive parameters, porosity and water saturation.

[0190] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0191] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0192] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0193] Example 6:

[0194] Another embodiment of this application relates to an electronic device, such as... Figure 11 As shown, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the above method steps.

[0195] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0196] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0197] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0198] Example 7:

[0199] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the above-described method steps.

[0200] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0201] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0202] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0203] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0204] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for establishing physical properties and fluid-rock physical quantities, characterized in that, include: Acquire well logging data of the reservoir, wherein the well logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range; The fluid-sensitive parameters are determined based on the elastic parameters under different water saturation levels within the preset porosity range. Based on the elastic parameters under different porosities within the preset water saturation range, determine the physical property sensitive parameters; A rock physics model that can consider the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores is constructed based on the equivalent embedded stress-averaged model and the standard linear body model. Based on the rock physics model, a rock physics model for predicting physical properties is established by combining the physical property sensitive parameters, porosity, and water saturation; and a rock physics model for predicting fluid properties is established by combining the rock physics model, the fluid sensitive parameters, porosity, and water saturation.

2. The method for establishing physical properties and fluid-rock physical quantities according to claim 1, characterized in that, The step of determining the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range includes: The fluid indicator factor of each elastic parameter is calculated based on the elastic parameters under different water saturation levels within the preset porosity range. The elastic parameter corresponding to the highest value of the fluid indicator factor is determined to be the fluid sensitive parameter.

3. The method for establishing physical properties and fluid-rock physical quantities according to claim 2, characterized in that, The expression for the fluid indicator factor is: The fluid indicator factor is equal to the ratio of the difference between the elastic modulus corresponding to saturation and the elastic modulus of different water saturation states to the elastic modulus corresponding to saturation.

4. The method for establishing physical properties and fluid-rock physical quantities according to claim 1, characterized in that, The step of determining the physical property sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range includes: The intersection relationship between each elastic parameter and the porosity is linearly fitted to obtain the fitting coefficient of each elastic parameter; The elastic parameter corresponding to the highest value of the fitting coefficient is determined to be a material property sensitive parameter.

5. The method for establishing physical properties and fluid-rock physical quantities according to claim 1, characterized in that, The steps for constructing a rock physics model that considers the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids within pores based on the equivalent embedded stress-averaged model and the standard linear body model include: The equivalent embedded stress-average model is extended across the entire frequency band using the standard linear body model, and a reservoir rock physics model is constructed based on Wood's theoretical equations.

6. The method for establishing physical properties and fluid-rock physical quantities according to claim 5, characterized in that, The steps of extending the equivalent embedded stress-averaged model across the entire frequency band using a standard linear volume model and constructing a reservoir rock physics model based on Wood's theoretical equations include: The low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model of rock were determined using an equivalent embedded stress averaging model. Based on the low-frequency bulk modulus, low-frequency shear modulus, high-frequency bulk modulus, and high-frequency shear model, the reservoir's volumetric and shear complex modulus are obtained through a standard linear volume model. Based on the volumetric and shear moduli, a reservoir rock physical model is obtained using Wood's theoretical equations.

7. The method for establishing physical properties and fluid-rock physical quantities according to claim 1, characterized in that, The step of acquiring reservoir logging data, wherein the logging data includes elastic parameters under different water saturation levels within a preset porosity range, and the step of acquiring elastic parameters under different porosity levels within a preset water saturation range further includes: The number of elastic parameters under different porosity ranges is counted, and the porosity range with the largest number of elastic parameters is determined as the preset porosity range.

8. A device for establishing physical properties and fluid-rock physical quantities, characterized in that, include: The acquisition module is used to acquire well logging data of the reservoir, wherein the well logging data includes elastic parameters under different water saturation levels within a preset porosity range, and elastic parameters under different porosity levels within a preset water saturation range. The first determining module is used to determine the fluid-sensitive parameters based on the elastic parameters under different water saturation levels within the preset porosity range. The second determining module is used to determine the physical property sensitive parameters based on the elastic parameters under different porosities within the preset water saturation range; The model building module is used to build rock physics models that can take into account the non-uniformity of pore structure and the non-uniform distribution of immiscible fluids inside pores, based on the equivalent embedded stress-averaged model and the standard linear body model. The rock physics model construction module is used to build a rock physics model for predicting physical properties based on the rock physics model, combined with the physical property sensitive parameters, porosity, and water saturation; and to build a rock physics model for predicting fluid flow based on the rock physics model, combined with the fluid sensitive parameters, porosity, and water saturation.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the physical properties and fluid rock physical scale establishment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for establishing physical properties and fluid-rock physical quantities as described in any one of claims 1 to 7.

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