Oil and gas productivity grading method and device in reservoir area, medium and program product

Abstract

The invention discloses an oil and gas productivity grading method for a reservoir area, and relates to the field of oil and gas exploration, and the method comprises the steps: obtaining target sampling data of a to-be-graded reservoir area; determining a porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model, and determining a porosity sensitive factor curve according to the porosity correlation factor; determining a porosity aspect ratio corresponding to the target sampling data through a pre-constructed digital elevation model, and determining a permeability sensitive factor curve according to the porosity aspect ratio; according to the porosity sensitive factor curve and the permeability sensitive factor curve, productivity grading is conducted on the reservoir area to be graded, and a productivity grading result of the reservoir area to be graded is obtained. According to the embodiment of the invention, the oil-gas productivity grading accuracy of the reservoir area can be improved, and the oil-gas exploitation efficiency of the reservoir area is further improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method, equipment, medium and program product for classifying oil and gas production capacity in reservoir areas. Background Technology

[0002] Oil and gas resources are widely distributed globally, but their distribution and enrichment patterns are often complex and variable in specific regions or reservoirs. This presents significant challenges to oil and gas exploration and development. To utilize resources more effectively, detailed reservoir evaluation and classification are necessary to identify areas with high production potential, thereby prioritizing exploration and development.

[0003] In existing technologies, reservoir productivity is generally evaluated by constructing a single sensitive factor based on rock physics models. This method has certain application effects in reservoirs with relatively simple pore structures and weak fracture anisotropy.

[0004] However, reservoir productivity is easily affected by various factors, and evaluation methods based on a single sensitive factor to classify reservoir productivity are difficult to effectively distinguish between reservoirs and non-reservoirs in complex reservoirs. Low accuracy in classifying reservoir productivity further leads to reduced oil and gas development efficiency. Summary of the Invention

[0005] This invention provides a method, equipment, medium, and program product for classifying oil and gas production capacity in reservoir areas, in order to solve the problem that the accuracy of reservoir production capacity classification is low when based on a single sensitive factor in the prior art, which further leads to a decrease in oil and gas development efficiency.

[0006] According to one aspect of the present invention, a method for classifying oil and gas productivity in a reservoir region is provided, comprising:

[0007] Acquire target sampling data of the reservoir region to be graded;

[0008] Based on a pre-constructed porous elastomer model, a porosity correlation factor corresponding to the target sampling data is determined, and a porosity sensitivity factor curve is determined based on the porosity correlation factor.

[0009] The porosity aspect ratio corresponding to the target sampling data is determined by a pre-constructed digital elevation model, and the permeability sensitivity factor curve is determined based on the porosity aspect ratio.

[0010] Based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, the reservoir region to be classified is classified in terms of productivity, and the productivity classification result of the reservoir region to be classified is obtained.

[0011] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the oil and gas production capacity classification method for reservoir areas according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the oil and gas production capacity classification method for reservoir regions as described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the oil and gas production capacity classification method for reservoir regions as described in any embodiment of the present invention.

[0017] The technical solution of this invention involves acquiring target sampling data of a reservoir region to be graded, determining a porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model, then determining a porosity sensitivity factor curve with high correlation to the reservoir region to be graded based on the porosity correlation factor, determining the porosity aspect ratio corresponding to the target sampling data through a pre-constructed digital elevation model, and determining a permeability sensitivity factor curve with high correlation to the reservoir region to be graded based on the porosity aspect ratio. Finally, the reservoir region to be graded is classified for production capacity based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, resulting in a production capacity classification result for the reservoir region to be graded. This improves the accuracy of oil and gas production capacity classification in reservoir regions and further enhances the oil and gas extraction efficiency when exploiting reservoir regions to be graded.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a method for classifying oil and gas production capacity in a reservoir region according to Embodiment 1 of the present invention;

[0021] Figure 2 This is a schematic diagram of a well logging curve provided according to an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of another method for classifying oil and gas production capacity in a reservoir region according to Embodiment 2 of the present invention;

[0023] Figure 4 This is a schematic diagram of the overlay of the porosity sensitivity factor curve and the porosity curve after nonlinear transformation, as well as the pore aspect ratio curve and the permeability curve after nonlinear transformation, provided by an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the structure of an oil and gas production capacity grading device in a reservoir area according to Embodiment 3 of the present invention;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the oil and gas production capacity classification method in the reservoir region according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0028] Example 1

[0029] Figure 1 This invention provides a flowchart of a method for classifying oil and gas production capacity in a reservoir region, as described in Embodiment 1. This embodiment is applicable to situations where oil and gas production capacity is classified in a reservoir region. The method can be executed by an oil and gas production capacity classification device for the reservoir region. This device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method may include:

[0030] S110. Obtain target sampling data of the reservoir area to be graded.

[0031] The reservoir area to be graded can be an area with exploitable potential during oil and gas exploration, but whose reservoir productivity needs to be graded. The target sampling data can be a series of geological and physical parameter data obtained through geological exploration, well logging, or seismic methods, or it can be sampling data of the reservoir area to be graded after processing and storage following information obtained through geological exploration, well logging, or seismic methods. The target sampling data can include at least one of well logging data, seismic exploration data, or core data.

[0032] For example, technicians can install multiple geophones on the ground in the reservoir area to be graded, then artificially generate seismic waves, and receive the feedback seismic wave data through the geophones. Further, by analyzing and processing the seismic wave data, target sampling data corresponding to multiple sub-regions of the reservoir area to be graded are obtained, and these target sampling data are then stored in a database. When grading the productivity of the reservoir area to be graded, the target sampling data corresponding to the reservoir area to be graded can be directly obtained from the database. By analyzing and processing the seismic wave data to obtain the target sampling data corresponding to multiple sub-regions of the reservoir area to be graded, the target sampling data required for productivity grading of the reservoir area to be graded can be obtained quickly and accurately at a low cost.

[0033] S120. Based on the pre-constructed porous elastomer model, determine the porosity correlation factor corresponding to the target sampling data, and determine the porosity sensitivity factor curve according to the porosity correlation factor.

[0034] Among them, the porous elastomer model can be constructed by well logging curves, interpretation result curves and core test elastic parameters obtained under various exploration scenarios, and is used to determine the porosity correlation factor corresponding to the target sampling data.

[0035] Well logging curves can be curves showing changes in the physical properties of formation rocks measured and plotted using well logging technology. These can include spontaneous potential logging curves, resistivity logging curves (such as deep-penetration resistivity, medium-penetration resistivity, micro-focused resistivity, etc.), porosity logging curves (such as compensated neutron, compensated density, sonic transit time, etc.), shale indicator logging curves (such as wellbore diameter, natural gamma, etc.), as well as induction logging curves, dual-lateral logging curves, and other types of data. Figure 2 This is a schematic diagram of a well logging curve provided according to an embodiment of the present invention, such as... Figure 2 As shown, well logging curves may include P-wave velocity curves, S-wave velocity curves, density curves, porosity curves, permeability curves, water saturation curves, calcite content curves, silica content curves, dolomite content curves, clay content curves, gypsum content curves, potassium feldspar content curves, sodium feldspar content curves, and pyrite content curves.

[0036] Interpretive curves are curves obtained by interpreting data acquired through methods such as well logging during geological exploration or oil and gas development. These curves characterize the geological features, physical properties, and fluid properties of formations. Through specific algorithms and models, key parameters such as formation porosity, permeability, and saturation can be derived. Interpretive curves can include resistivity curves, sonic transit time curves, and natural gamma curves. Core test elastic parameters are parameters of rock elastic properties obtained through core sampling and experimental testing. These parameters characterize the deformation and recovery capacity of rocks under stress.

[0037] Specifically, this involves collecting well logging data from oil and gas reservoirs of different reservoir types. After processing and analyzing this data, interpretation curves that directly reflect the characteristics of the oil and gas reservoir area are obtained. A porous elastic body model is then constructed by combining the well logging data, interpretation curves, and core test elastic parameters of the oil and gas reservoir area.

[0038] Among them, the porosity correlation factor can be calculated by combining the rock skeleton shear modulus corresponding to the target sampling data obtained by the porous elastomer model with the shear wave velocity and density of the saturated fluid rock, and is used to characterize the reservoir storage capacity of the reservoir in the reservoir area to be graded.

[0039] Among them, the porosity sensitivity factor curve can be obtained by transforming the porosity correlation factor of the target sampling data according to the function corresponding to the reservoir type of the reservoir area to be classified after determining the reservoir type of the reservoir area to be classified based on the core data, and then using it to characterize the reservoir storage capacity of the reservoir in the reservoir area to be classified.

[0040] For example, target sampling data can be input into a pre-constructed porous elastomer model, and the porosity correlation factor corresponding to the target sampling data can be determined based on the porous elastomer model. According to the reservoir characteristics of the reservoir region to be graded, the reservoir type of the target sampling data is determined. The target sampling data is then processed according to the reservoir type to obtain a porosity sensitivity factor curve that characterizes the reservoir's storage capacity and fluid flowability in the reservoir region to be graded. The porosity sensitivity factor curve can accurately reflect the reservoir's storage capacity in the reservoir region to be graded, thus improving the accuracy of production capacity grading in the reservoir region to be graded.

[0041] S130. Determine the porosity aspect ratio corresponding to the target sampling data through a pre-constructed digital elevation model, and determine the permeability sensitivity factor curve based on the porosity aspect ratio.

[0042] The digital elevation model can be constructed from well logging curves, interpretation result curves, and core test elastic parameters obtained under various exploration scenarios. It is used to calculate the porosity aspect ratio corresponding to the target sampling data of the reservoir area to be graded.

[0043] The porosity aspect ratio, determined using a digital elevation model, characterizes the ratio of pore width to pore diameter in the reservoir region to be classified. The porosity aspect ratio can range from 0 to 1, and different reservoir types may correspond to different porosity aspect ratios. For example, the porosity aspect ratio of sandstone is typically between 0.1 and 1.0; that of dolomite is between 0.01 and 0.1; while that of limestone can be as low as 0.01 to 0.001.

[0044] Among them, the permeability sensitive factor curve is obtained by transforming the pore aspect ratio of the target sampling data according to the function corresponding to the reservoir type of the reservoir area to be classified after determining the reservoir type of the reservoir area to be classified. This curve is used to characterize the ability of fluid to flow through the pore space, the connectivity of pores in the reservoir, and the flow rate of fluid in it.

[0045] For example, target sampling data can be input into a pre-constructed digital elevation model (DEM). The pore aspect ratio can be adjusted using the DEM. Then, the calculated P-wave velocity based on the adjusted pore aspect ratio is compared with the actual logging P-wave velocity. When the error between the two reaches a preset value, the pore aspect ratio is determined as the pore aspect ratio corresponding to the reservoir area to be classified. Then, based on the reservoir characteristics of the reservoir area to be classified, the pore aspect ratio is processed to obtain a permeability sensitivity factor curve that characterizes the fluid flow capacity through the pore space, the connectivity of pores in the reservoir, and the fluid flow rate within it. The permeability sensitivity factor curve accurately reflects the fluid flow capacity through the pore space in the reservoir area to be classified, thus improving the accuracy of productivity classification of the reservoir area.

[0046] S140. Based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, the reservoir region to be classified is classified in terms of production capacity to obtain the production capacity classification result of the reservoir region to be classified.

[0047] The capacity classification result can be determined based on the porosity sensitive factor curve and the permeability sensitive factor curve, which represents the capacity level corresponding to different reservoir regions in the reservoir region to be classified.

[0048] Specifically, the porosity sensitivity factor of different regions in the reservoir to be graded can be determined based on the porosity sensitivity factor curve, the permeability sensitivity factor of different regions in the reservoir to be graded can be determined based on the permeability sensitivity factor curve, the production capacity level of each region in the reservoir to be graded can be determined based on the porosity sensitivity factor and permeability sensitivity factor, and finally the production capacity level of each region in the reservoir to be graded can be summarized to obtain the production capacity grading result of the reservoir to be graded.

[0049] The technical solution of this invention involves acquiring target sampling data of the reservoir area to be graded, determining the porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model, then determining a porosity sensitivity factor curve with high correlation to the reservoir area to be graded based on the porosity correlation factor, determining the porosity aspect ratio corresponding to the target sampling data using a pre-constructed digital elevation model, and determining a permeability sensitivity factor curve with high correlation to the reservoir area to be graded based on the porosity aspect ratio. Finally, the reservoir area to be graded is classified for production capacity based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, resulting in a production capacity classification result for the reservoir area to be graded. This improves the accuracy of oil and gas production capacity classification in the reservoir area, further enhancing the oil and gas extraction efficiency when exploiting the reservoir area to be graded.

[0050] Example 2

[0051] Figure 3 This is a flowchart of another method for classifying oil and gas production capacity in a reservoir region, provided in Embodiment 2 of the present invention. Based on the embodiments described above, this embodiment involves: acquiring target sampling data of the reservoir region to be classified; determining a porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model; determining a porosity sensitivity factor curve based on the porosity correlation factor; determining the porosity aspect ratio corresponding to the target sampling data using a pre-constructed digital elevation model; determining a permeability sensitivity factor curve based on the porosity aspect ratio; and classifying the production capacity of the reservoir region to be classified based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, thus further refining the production capacity classification results of the reservoir region to be classified. Figure 3 As shown, the method may include:

[0052] S210. Obtain target sampling data of the reservoir area to be graded.

[0053] The reservoir region to be graded can include multiple sub-regions. When grading the production capacity of the reservoir region, it can be divided into multiple sub-regions. Finally, based on the production capacity grading results of the multiple sub-regions, the production capacity of the reservoir region is graded, providing valuable reference for oil and gas extraction and further improving development efficiency. Target sampling data can correspond to multiple sub-regions within the reservoir region to be graded; that is, one sub-region can correspond to at least one target sampling data point.

[0054] S220. The target sampling data is processed using the porous elastomer model to obtain the shear modulus of the rock skeleton corresponding to the target sampling data.

[0055] Among them, the porous elastomer model can be constructed by combining the logging curves, interpretation result curves and core test elastic parameters obtained under various exploration scenarios with Biot theory (Biot consolidation theory, which was first derived by Biot in 1941 based on a rigorous consolidation mechanism and can accurately reflect the coupling relationship between pore pressure dissipation and soil skeleton deformation) to calculate the rock skeleton shear modulus corresponding to the target sampling data of the reservoir area to be graded.

[0056] The shear modulus of the rock skeleton can be defined as the ability of the solid skeleton portion of a rock formation to resist shear deformation under multiaxial shear stress. The shear modulus of the rock skeleton can be used to characterize the shear strength and plasticity of the reservoir region corresponding to the target sampling data under shear loading.

[0057] S230. Based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock, determine the porosity correlation factor corresponding to the target sampling data.

[0058] Among them, the transverse wave velocity of saturated fluid rock

[0059] The density of saturated fluid rock is used to characterize the mass per unit volume of rock when the pores in the reservoir are completely filled with fluid.

[0060] Optionally, the porosity correlation factor corresponding to the target sampling data can be determined using the following formula, based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock:

[0061]

[0062] Where, β μ β is the porosity correlation factor. m v is the shear modulus of the skeleton.s Let ρ be the shear wave velocity of the fluid-rock mixture. s The density of the fluid rock.

[0063] Optionally, the above formula can be derived by combining the P-wave velocity, S-wave velocity, and density of the rock in the reservoir region with the calculation formula in Biot theory. The specific derivation process is as follows:

[0064] The Biot theory includes the following formulas (1) and (2):

[0065]

[0066] Among them, v p Let v be the longitudinal wave velocity. s k is the transverse wave velocity. s μ is the bulk modulus of saturated fluid rock. s ρ is the shear modulus of saturated fluid rock. s For the density of saturated fluid rock, v s The velocity is the transverse wave velocity.

[0067] The relationship between the Biot coefficient (proposed by French geophysicist Maurice Anthony Biot in the 1950s, used to characterize the relationship between the kinematic properties of acoustic media and solid media in porous media) and the skeleton modulus can be expressed by the following formula (3):

[0068] k d =k m (1-β)(3)

[0069] Where, k d k is the bulk modulus of the rock skeleton. m β is the bulk modulus of the matrix, and β is the Biot coefficient.

[0070] The expression for the bulk modulus of the rock skeleton is similar in form to that for the shear modulus. Therefore, the relationship between the bulk modulus of the rock skeleton with Biot coefficients and the bulk modulus of the matrix can be generalized to the relationship between the shear modulus of the rock skeleton and the shear modulus of the matrix:

[0071] μ d =μ m (1-β μ (4)

[0072] Where, μ d μ is the shear modulus of the rock skeleton. m β is the shear modulus of the matrix. μ This is the porosity correlation factor.

[0073] Generally, bulk modulus is greatly affected by fluid, while shear modulus is largely unaffected. Therefore, when a reservoir contains oil and gas, the saturated rock shear modulus is approximately equal to the rock skeleton shear modulus. Thus, the relationship between the saturated rock shear modulus and the rock skeleton shear modulus can be expressed by the following formula:

[0074] μ s =μ d (5)

[0075] The expression for the porosity correlation factor can be determined from the above formulas (2), (4), and (5).

[0076] S240. Obtain core data of the reservoir area to be graded, and determine the porosity sensitivity factor curve based on the core data and the porosity correlation factor.

[0077] Optionally, determining the porosity sensitivity factor curve based on the core data and the porosity correlation factor includes: determining the reservoir type of the reservoir area to be classified based on the core data; determining a first target transformation function of the porosity correlation factor based on the reservoir type; and performing a nonlinear transformation on the porosity correlation factor using the first target transformation function to determine the porosity sensitivity factor curve.

[0078] Core data, obtained through drilling and core analysis, characterizes the physical properties of the reservoir area to be classified. This data can include density, porosity, and permeability, among other aspects. Reservoir type, determined from the core data, characterizes the reservoir rock type. Reservoir types can include clastic reservoirs, carbonate reservoirs, and other lithological reservoirs: clastic reservoirs can include conglomerate, sandstone, and siltstone; carbonate reservoirs can include limestone, dolomite, biogenic limestone, and oolitic limestone; other lithological reservoirs can be formed from rocks other than clastic and carbonate rocks, such as igneous rocks, metamorphic rocks, and mudstone.

[0079] The first objective transformation function can be a function that performs a nonlinear transformation on the porosity correlation factor, determined according to the reservoir type of the reservoir area to be classified. Different reservoir types correspond to different first objective transformation functions. For example, Table 1 shows the nonlinear transformation analysis table of the porosity correlation factor (F_Bu_TCMR) and the porosity curve (Porosity) when the reservoir type of the reservoir area to be classified is carbonate reservoir. As shown in Table 1, the porosity correlation factor after square transformation can be determined as the porosity sensitive factor, and the corresponding first objective transformation function is the nonlinear transformation function that squares the porosity correlation factor.

[0080] Table 1. Nonlinear Transformation Analysis of Porosity Correlation Factor and Porosity Curve

[0081]

[0082]

[0083] Figure 4 This is a superimposed schematic diagram of the porosity sensitivity factor curve and the porosity curve after nonlinear transformation, as well as the pore aspect ratio curve and permeability curve after nonlinear transformation, provided by an embodiment of the present invention. For example, target sampling data can be input into a pre-constructed porous elastomer model, and the rock skeleton shear modulus corresponding to the target sampling data can be calculated through the porous elastomer model. The porosity correlation factor corresponding to the target sampling data is calculated based on the rock skeleton shear modulus, the shear wave velocity of saturated fluid rock, and the density of saturated fluid rock. Based on the core data of the reservoir area to be graded, the reservoir type of the target sampling data is determined to be a carbonate reservoir. Then, the first target transformation function corresponding to the target sampling data is determined to be a nonlinear transformation function that squares the porosity correlation factor. By performing a nonlinear transformation on the porosity correlation factor corresponding to the target sampling data using the nonlinear transformation function that squares the porosity correlation factor, a porosity sensitivity factor curve that can characterize the reservoir's storage capacity and fluid flowability in the reservoir area to be graded is obtained. Porosity sensitivity curves can accurately reflect the reservoir capacity of reservoirs in the reservoir region to be classified, and can improve the accuracy of capacity classification of reservoir regions to be classified.

[0084] S250. Determine the porosity aspect ratio corresponding to the target sampling data through a pre-constructed digital elevation model, and determine the permeability sensitivity factor curve based on the porosity aspect ratio.

[0085] Optionally, determining the permeability sensitivity factor curve based on the porosity aspect ratio includes: acquiring core data of the reservoir area to be classified; determining the reservoir type of the reservoir area to be classified based on the core data; determining a second target transformation function of the porosity aspect ratio based on the reservoir type; and performing a nonlinear transformation on the porosity aspect ratio using the second target transformation function to determine the permeability sensitivity factor curve.

[0086] The second objective transformation function can be a function that performs a nonlinear transformation on the porosity aspect ratio, determined according to the reservoir type of the reservoir area to be classified. Different reservoir types correspond to different second objective transformation functions. For example, Table 2 shows the nonlinear transformation analysis of the porosity aspect ratio (F_asp_CMFF) and permeability curve (Permeability) when the reservoir type of the reservoir area to be classified is carbonate reservoir. As shown in Table 2, the porosity correlation factor after taking the reciprocal transformation can be determined as the porosity sensitive factor, and the corresponding second objective transformation function is the nonlinear transformation function that takes the reciprocal of the porosity correlation factor.

[0087] Table 2. Nonlinear Transformation Analysis of Porosity Aspect Ratio and Permeability Curves

[0088] Nonlinear transformation of permeability curve Nonlinear transformation of pore aspect ratio Correlation coefficient Log(Permeability) 1 / (F_asp_CMFF) -0.68859 Permeability 1 / (F_asp_CMFF) -0.3095 (Permeability)**2 1 / (F_asp_CMFF) -0.172863 1 / (Permeability) Log(F_asp_CMFF) -0.16314 1 / (Permeability) Sqrt(F_asp_CMFF) -0.15896 1 / (Permeability) F_asp_CMFF -0.154761 1 / (Permeability) (F_asp_CMFF)**2 -0.14634 1 / (Permeability) 1 / (F_asp_CMFF) 0.171398 (Permeability)**2 Log(F_asp_CMFF) 0.178801

[0089] Figure 4 This is a schematic diagram overlaid with the porosity sensitivity factor curve and the porosity curve after nonlinear transformation, as well as the pore aspect ratio curve and permeability curve after nonlinear transformation, according to an embodiment of the present invention. For example, target sampling data can be input into a pre-constructed digital elevation model. The pore aspect ratio can be adjusted through the digital elevation model. Then, the P-wave velocity calculated based on the adjusted pore aspect ratio is compared with the actual logging P-wave velocity. When the error between the two reaches a preset value, the pore aspect ratio is determined as the pore aspect ratio corresponding to the reservoir region to be graded. Then, based on the core data of the reservoir area to be graded, the reservoir type of the target sampling data is determined to be carbonate reservoir. According to the reservoir type, the corresponding nonlinear transformation function for the target sampling data is determined to be a nonlinear transformation function that takes the reciprocal of the porosity correlation factor. This nonlinear transformation function, taking the reciprocal of the porosity correlation factor, is used to nonlinearly transform the pore aspect ratio of the target sampling data, resulting in a permeability sensitivity factor curve that characterizes the fluid flow capacity through the pore space of the reservoir in the area to be graded, the connectivity of the pores in the reservoir, and the fluid flow rate within them. The permeability sensitivity factor curve accurately reflects the fluid flow capacity through the pore space of the reservoir area to be graded, thus improving the accuracy of productivity grading in the reservoir area to be graded.

[0090] S260. Based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, the reservoir region to be classified is classified in terms of productivity, and the productivity classification result of the reservoir region to be classified is obtained.

[0091] Optionally, the step of classifying the reservoir region to be classified according to the porosity sensitivity factor curve and the permeability sensitivity factor curve to obtain the capacity classification result of the reservoir region to be classified includes: determining the porosity sensitivity factor of each reservoir sub-region to be classified according to the porosity sensitivity factor curve, determining the permeability sensitivity factor of each reservoir sub-region to be classified according to the permeability sensitivity factor curve; determining the capacity level of each reservoir sub-region to be classified according to the porosity sensitivity factor and the permeability sensitivity factor; and obtaining the capacity classification result of the reservoir region to be classified according to the capacity level of each reservoir sub-region to be classified.

[0092] The reservoir region to be graded includes multiple sub-regions, which can be divided according to the production capacity grading requirements of the reservoir region. The production capacity level of each sub-region can be the corresponding production capacity level of the individual sub-regions within the reservoir region. Production capacity levels can include high-producing areas, low-producing areas, and non-producing areas. Different production capacity levels of the sub-regions correspond to different reservoir qualities, oil and gas resource distributions, development plans, and economic benefits. After obtaining the production capacity grading results, the extraction personnel can determine the extraction sequence based on the different levels of producing areas, further improving the efficiency of oil and gas extraction from the reservoir region.

[0093] For example, Figure 5 The intersection plot of the porosity-sensitive factor and permeability-sensitive factor curves in the embodiment is shown below. Figure 5 As shown, the porosity of the reservoir region to be graded can be determined based on the porosity sensitivity factor curve, and the permeability of the reservoir region to be graded can be determined based on the permeability sensitivity factor curve. Furthermore, the intersecting analysis of the porosity sensitivity factor curve and the permeability sensitivity factor curve is performed to evaluate the reservoir productivity of the reservoir region to be graded.

[0094] The technical solution of this invention involves acquiring target sampling data of a reservoir region to be classified, determining a porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model, then determining a first objective function for nonlinearly transforming the porosity correlation factor based on the reservoir type of the reservoir region to be classified, and using the first objective function to determine a porosity sensitive factor curve with high correlation to the reservoir region to be classified. A porosity aspect ratio corresponding to the target sampling data is determined using a pre-constructed digital elevation model, and then a second objective function for nonlinearly transforming the porosity aspect ratio based on the reservoir type of the reservoir region to be classified, and using the second objective function to determine a permeability sensitive factor curve with high correlation to the reservoir region to be classified. Finally, the reservoir region to be classified is classified for production capacity based on the porosity sensitive factor curve and the permeability sensitive factor curve, resulting in a production capacity classification result for the reservoir region to be classified. This method can improve the accuracy of oil and gas production capacity classification in reservoir regions, and further enhance the oil and gas extraction efficiency of reservoir regions during exploitation.

[0095] Example 3

[0096] Figure 5 This is a schematic diagram of a reservoir region oil and gas production capacity grading device provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes: a data acquisition module 310, a porosity sensitive factor curve determination module 320, a permeability sensitive factor curve determination module 330, and a reservoir region productivity classification module 340.

[0097] The data acquisition module 310 is used to acquire target sampling data of the reservoir area to be graded; the porosity sensitivity factor curve determination module 320 is used to determine the porosity correlation factor corresponding to the target sampling data based on a pre-constructed porous elastomer model, and determine the porosity sensitivity factor curve according to the porosity correlation factor; the permeability sensitivity factor curve determination module 330 is used to determine the porosity aspect ratio corresponding to the target sampling data through a pre-constructed digital elevation model, and determine the permeability sensitivity factor curve according to the porosity aspect ratio; the reservoir area productivity grading module 340 is used to perform productivity grading of the reservoir area to be graded according to the porosity sensitivity factor curve and the permeability sensitivity factor curve, and obtain the productivity grading result of the reservoir area to be graded.

[0098] The data acquisition module acquires target sampling data of the reservoir area to be graded. Based on a pre-constructed porous elastomer model in the porosity sensitivity factor curve determination module, a porosity correlation factor corresponding to the target sampling data is determined. Based on this porosity correlation factor, a porosity sensitivity factor curve with a high correlation to the reservoir area to be graded is determined. Similarly, a porosity aspect ratio corresponding to the target sampling data is determined using a pre-constructed digital elevation model in the permeability sensitivity factor curve determination module. Based on this aspect ratio, a permeability sensitivity factor curve with a high correlation to the reservoir area to be graded is determined. Finally, the reservoir area productivity grading module performs productivity grading on the reservoir area to be graded based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, obtaining the productivity grading results. This improves the accuracy of oil and gas productivity grading in reservoir areas, further enhancing oil and gas extraction efficiency during the exploitation of these areas.

[0099] Furthermore, the porosity sensitivity factor curve determination module 320 includes: a porosity correlation factor unit 321 and a porosity sensitivity factor curve determination unit 322. The porosity correlation factor unit 321 is used to process the target sampling data through the porous elastomer model to obtain the shear modulus of the rock skeleton corresponding to the target sampling data; and to determine the porosity correlation factor corresponding to the target sampling data based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock.

[0100] Furthermore, the porosity correlation factor unit 321 can determine the porosity correlation factor corresponding to the target sampling data based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock using the following formula:

[0101]

[0102] Where, β μ β is the porosity correlation factor. μ v is the shear modulus of the skeleton. s Let ρ be the shear wave velocity of the fluid-rock mixture. s The density of the fluid rock.

[0103] Furthermore, the porosity sensitivity factor curve determination unit 322 is used to acquire core data of the reservoir area to be graded, and determine the porosity sensitivity factor curve based on the core data and the porosity correlation factor.

[0104] Furthermore, the porosity sensitivity factor curve determination unit 322 is specifically used to determine the reservoir type of the reservoir area to be classified based on the core data, determine the first target transformation function of the porosity correlation factor based on the reservoir type, and perform a nonlinear transformation on the porosity correlation factor through the first target transformation function to determine the porosity sensitivity factor curve.

[0105] Furthermore, the permeability sensitivity factor curve determination module 330 is specifically used for: acquiring core data of the reservoir area to be classified; determining the reservoir type of the reservoir area to be classified based on the core data; determining a second target transformation function for the porosity aspect ratio based on the reservoir type; performing a nonlinear transformation on the porosity aspect ratio through the second target transformation function to determine the permeability sensitivity factor curve.

[0106] Furthermore, the reservoir region productivity classification module 340 is specifically used for: determining the porosity sensitivity factor of each reservoir sub-region to be classified based on the porosity sensitivity factor curve, and determining the permeability sensitivity factor of each reservoir sub-region to be classified based on the permeability sensitivity factor curve; determining the productivity level of each reservoir sub-region to be classified based on the porosity sensitivity factor and the permeability sensitivity factor; and obtaining the productivity classification result of the reservoir sub-region to be classified based on the productivity level of each reservoir sub-region to be classified.

[0107] The oil and gas production capacity classification device for reservoir areas provided in the embodiments of the present invention can execute the oil and gas production capacity classification method for reservoir areas provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0108] Example 4

[0109] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0110] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0112] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as oil and gas production capacity classification methods for reservoir regions.

[0113] In some embodiments, the reservoir region oil and gas productivity classification method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the reservoir region oil and gas productivity classification method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the reservoir region oil and gas productivity classification method by any other suitable means (e.g., by means of firmware).

[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0120] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for classifying oil and gas productivity in a reservoir region, characterized in that, include: Acquire target sampling data of the reservoir region to be graded; Based on a pre-constructed porous elastomer model, a porosity correlation factor corresponding to the target sampling data is determined, and a porosity sensitivity factor curve is determined based on the porosity correlation factor. The porosity aspect ratio corresponding to the target sampling data is determined by a pre-constructed digital elevation model, and the permeability sensitivity factor curve is determined based on the porosity aspect ratio. Based on the porosity sensitivity factor curve and the permeability sensitivity factor curve, the reservoir region to be classified is classified in terms of productivity, and the productivity classification result of the reservoir region to be classified is obtained.

2. The method according to claim 1, characterized in that, The determination of the porosity correlation factor corresponding to the target sampling data based on the pre-constructed porous elastomer model includes: The target sampling data is processed using the porous elastomer model to obtain the shear modulus of the rock skeleton corresponding to the target sampling data; Based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock, a porosity correlation factor corresponding to the target sampling data is determined.

3. The method according to claim 2, characterized in that, The porosity correlation factor corresponding to the target sampling data is determined using the following formula, based on the shear modulus of the rock skeleton, the transverse wave velocity of the saturated fluid rock, and the density of the saturated fluid rock: Where, β μ β is the porosity correlation factor. m v is the shear modulus of the skeleton. s Let ρ be the shear wave velocity of the fluid-rock mixture. s The density of the fluid rock.

4. The method according to claim 1, characterized in that, The step of determining the porosity sensitivity factor curve based on the porosity correlation factor includes: Obtain core data from the reservoir area to be graded, and determine the porosity sensitivity factor curve based on the core data and the porosity correlation factor.

5. The method according to claim 4, characterized in that, The step of determining the porosity sensitivity factor curve based on the core data and the porosity correlation factor includes: The reservoir type of the reservoir area to be classified is determined based on the core data, and the first objective transformation function of the porosity correlation factor is determined based on the reservoir type. The porosity-sensitive factor curve is determined by performing a nonlinear transformation on the porosity correlation factor using the first target transformation function.

6. The method according to claim 1, characterized in that, The determination of the permeability sensitivity factor curve based on the porosity aspect ratio includes: Obtain core data of the reservoir area to be classified, and determine the reservoir type of the reservoir area to be classified based on the core data; The second objective transformation function for the porosity aspect ratio is determined based on the reservoir type. The porosity aspect ratio is then nonlinearly transformed using the second objective transformation function to determine the permeability sensitivity factor curve.

7. The method according to claim 1, characterized in that, in, The reservoir region to be graded includes multiple sub-regions. The process of classifying the reservoir region to be graded based on the porosity sensitivity factor curve and the permeability sensitivity factor curve to obtain the capacity classification result of the reservoir region to be graded includes: The porosity sensitivity factor of each reservoir sub-region to be graded is determined according to the porosity sensitivity factor curve, and the permeability sensitivity factor of each reservoir sub-region to be graded is determined according to the permeability sensitivity factor curve. The production capacity level of each reservoir sub-region to be graded is determined based on the porosity sensitivity factor and permeability sensitivity factor of each sub-region to be graded; Based on the production capacity level of each reservoir sub-region to be graded, the production capacity grading result of the reservoir sub-region to be graded is obtained.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the oil and gas production capacity classification method for reservoir regions as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the oil and gas production capacity classification method for the reservoir region as described in any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, wherein, When the computer program / instructions are executed by the processor, they implement the method for classifying the oil and gas production capacity of the reservoir region as described in any one of claims 1-7.

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