A petrophysical surface modeling method, system, storage medium and electronic device

Abstract

The present application relates to the technical field of rock physics and reservoir geophysics, and particularly relates to a rock physics surface modeling method, system, storage medium and electronic device, the method comprising: assigning the elastic modulus of the carbonate rock along the logging axial direction to the rock matrix, adding the preset pores and / or preset fractures of the carbonate rock along the logging axial direction to the rock matrix to obtain a rock dry skeleton; adding the parameter data of the fluid to the rock dry skeleton, calculating the wave response characteristics of the carbonate rock containing the fluid, establishing a physical point model of the carbonate rock containing the fluid, unifying the physical point models corresponding to the multiple logging respectively to obtain a physical surface model of the carbonate rock containing the fluid, combining the advantages of the point and line modeling methods, realizing high-precision and unified rock physics surface modeling, and being suitable for surface modeling of complex carbonate rock reservoirs and the like.

Description

Technical Field

[0001] This invention relates to the fields of rock physics and reservoir geophysics, and in particular to a rock physics surface modeling method, system, storage medium, and electronic device. Background Technology

[0002] Rock physics models serve as a bridge between rock composition, fluid dynamics, and macroscopic elastic properties. Accurate rock physics models are crucial for understanding and retrieving reservoir elastic parameters. Current rock physics modeling includes: point modeling based on rock cores and line modeling based on well logging data. Specifically:

[0003] 1) Core-based point modeling methods mainly include (Mavko, 2009): Differential Equivalent Medium Model (DEM), Self-Compatible Approximation Model (SCA), Backus Mean Model, Kuster-Toksoz Model, Hudson Model, etc. Xu et al. (2009) used RVH mean, KT model and Gassmann theory to model carbonate reservoirs and characterize the influence of fluids, fractures, etc. on rock elastic parameters and their anisotropy. Carcione et al. (2011) further described the influence of kerogen content on seismic velocity using Backus mean and Krief / Gassmann model. Zhao et al. (2016) combined Xu & White, SCA, DEM and other models to propose elastic equivalent parameters to characterize shale with different maturity levels. These rock physics modeling methods are all based on core "points". The models built are effective for that point and its small range, and are difficult to apply to strongly heterogeneous carbonate reservoirs with rapid vertical and horizontal variations in lithological properties. Pang et al. (2021) used the Biot-Rayleigh multiporosity equation to construct a petrological model of tight sandstone, and then analyzed the influence of fracture content on P-wave dispersion and attenuation. The multiporosity method can better describe the complex pore structure of the rock core itself and improve modeling accuracy, but it still has the problem of limited applicability.

[0004] 2) Well-logging-based rock physics line modeling utilizes well-logging data from the reservoir section of the well to obtain a rock physics line model that varies with depth, effectively addressing the problem of strong vertical heterogeneity. Avseth et al. (2005) established a rock physics model based on rock texture characteristics and well-logging curves, analyzing the limitations of well-logging modeling for applications far from well control through applications in various geological environments. Guo et al. (2013) used SCA and Backus averaging methods to construct a rock physics point template for shale cores, combined with well-logging data to obtain a line model, and further studied the influence of mineral composition, porosity, brittleness, and rock elastic parameters on seismic response. Well-logging-based rock physics line modeling improves the vertical representation range of the model, but there is still a problem of decreased accuracy in lateral extrapolation from the well outwards, and the models built for different wells are not uniform, making it difficult to effectively represent the overall characteristics of complex reservoirs. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing a method, system, storage medium and electronic device for modeling rock physical surfaces.

[0006] The technical solution of the rock physical surface modeling method of the present invention is as follows:

[0007] S1. Obtain the elastic modulus of the carbonate rock along the logging axis and obtain the corresponding rock matrix of the carbonate rock along the logging axis. Assign the elastic modulus of the carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, add the preset pores and / or preset fractures of the carbonate rock along the logging axis to the rock matrix to obtain the rock skeleton.

[0008] S2. Obtain the parameter data of the fluid in the carbonate rock along the logging axis and add it to the rock skeleton;

[0009] S3. Using the dual-hole model, plane wave analysis was performed on the rock skeleton after adding fluid parameters. The wave response characteristics of the carbonate rock containing fluid were calculated, and a physical point model of the carbonate rock containing fluid was established.

[0010] S4. Repeat S1 to S3 for multiple well logs to obtain the physical point model corresponding to each well log.

[0011] S5. Using Markov chains, the physical point models of the carbonate rocks containing fluids corresponding to each well log are unified to obtain the physical surface model of the carbonate rocks containing fluids.

[0012] The beneficial effects of the rock physical surface modeling method of the present invention are as follows:

[0013] The elastic modulus of carbonate rocks along the logging axis is assigned to the rock matrix, and pre-defined porosity and / or pre-defined fractures of the carbonate rocks along the logging axis are added to the rock matrix to obtain the rock skeleton. Fluid parameter data is then added to the rock skeleton, and the wave response characteristics of the fluid-containing carbonate rocks are calculated. A physical point model of the fluid-containing carbonate rocks is established. The physical point models corresponding to multiple wells are unified to obtain a physical surface model of the fluid-containing carbonate rocks. Combining the advantages of point and line modeling methods, high-precision, unified rock physical surface modeling is achieved. This effectively solves the limitations of conventional rock physical modeling, such as the inability to select a unified model, limited accuracy range, and difficulty in applying it to complex carbonate reservoirs, laying the foundation for geophysical prediction of complex deep carbonate reservoirs. Furthermore, based on the high-precision rock physical model, reservoir lithology and physical property inversion from 3D seismic data with better accuracy than conventional inversion is achieved.

[0014] Based on the above scheme, the rock physical surface modeling method of the present invention can be further improved as follows.

[0015] Furthermore, obtaining the elastic modulus of the carbonate rock along the logging axis includes:

[0016] The content of each preset mineral component of carbonate rock along the logging axis is obtained from the logging curve.

[0017] The elastic modulus of the carbonate rock along the logging axis is obtained based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component.

[0018] Furthermore, the fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

[0019] Furthermore, acquiring parameter data of the fluid in the carbonate rock along the logging axis includes:

[0020] The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, flow velocity, and density of the fluid in the carbonate rock along the logging axis based on the logging curves.

[0021] The technical solution of the rock physical surface modeling system of the present invention is as follows:

[0022] It includes a first acquisition and addition module, a second acquisition and addition module, a plane wave analysis module, a calling module, and a data unification module;

[0023] The first acquisition and addition module is used to: acquire the elastic modulus of carbonate rock along the logging axis, acquire the rock matrix corresponding to the carbonate rock along the logging axis, and assign the elastic modulus of carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, the preset pores and / or preset fractures of carbonate rock along the logging axis are added to the rock matrix to obtain the rock skeleton.

[0024] The second acquisition and addition module is used to: acquire parameter data of fluid in carbonate rock along the logging axis and add it to the rock skeleton;

[0025] The plane wave analysis module is used to: perform plane wave analysis on the rock skeleton after adding fluid parameters using a dual-hole model, calculate the wave response characteristics of carbonate rocks containing fluid, and establish a physical point model of carbonate rocks containing fluid.

[0026] The calling module is used to repeatedly call the first acquisition and addition module, the second acquisition and addition module, and the plane wave analysis module for multiple well logs to obtain the physical point model corresponding to each well log.

[0027] The data unification module is used to: unify the physical point models of the carbonate rocks containing fluids corresponding to each well log through a Markov chain, and obtain the physical surface model of the carbonate rocks containing fluids.

[0028] The beneficial effects of the rock physical surface modeling system of the present invention are as follows:

[0029] The elastic modulus of carbonate rocks along the logging axis is assigned to the rock matrix, and pre-defined pores and / or pre-defined fractures of carbonate rocks along the logging axis are added to the rock matrix to obtain the rock skeleton. Fluid parameter data is added to the rock skeleton, and the wave response characteristics of carbonate rocks containing fluids are calculated to establish a physical point model of carbonate rocks containing fluids. The physical point models corresponding to multiple wells are unified to obtain a physical surface model of carbonate rocks containing fluids. Combining the advantages of point and line modeling methods, high-precision and unified rock physical surface modeling is achieved. This can effectively solve the limitations of conventional rock physical modeling, such as the inability to select a unified model, limited accuracy range, and difficulty in being applied to complex carbonate reservoirs, laying the foundation for geophysical prediction of complex deep carbonate reservoirs.

[0030] Based on the above scheme, the rock physical surface modeling system of the present invention can be further improved as follows.

[0031] Furthermore, the process by which the first acquisition and addition module acquires the elastic modulus of carbonate rock along the logging axis includes:

[0032] The content of each preset mineral component of carbonate rock along the logging axis is obtained from the logging curve.

[0033] The elastic modulus of the carbonate rock along the logging axis is obtained based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component.

[0034] Furthermore, the fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

[0035] Furthermore, the process by which the second acquisition and addition module acquires parameter data of the fluid in the carbonate rock along the logging axis includes:

[0036] The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, flow velocity, and density of the fluid in the carbonate rock along the logging axis based on the logging curves.

[0037] The present invention provides a storage medium storing instructions, which, when read by a computer, cause the computer to execute any of the above-described rock physical surface modeling methods.

[0038] An electronic device according to the present invention includes a processor and the above-described storage medium, wherein the processor executes instructions in the storage medium. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a rock physical surface modeling method according to an embodiment of the present invention;

[0040] Figure 2 Flowchart for modeling "points" in rock physics;

[0041] Figure 3 A "point" petrographic template for deep fluid-bearing carbonate rocks;

[0042] Figure 4 A graph showing the content of each preset mineral component;

[0043] Figure 5 This is a probability distribution diagram of porosity;

[0044] Figure 6 This is a probability distribution diagram of the pressure.

[0045] Figure 7 This is a probability distribution diagram of the flow velocity;

[0046] Figure 8 One of the scale diagrams showing the longitudinal wave velocity as a function of porosity;

[0047] Figure 9The second graph shows the longitudinal wave velocity as a function of porosity.

[0048] Figure 10 A graph showing formation pressure as a function of porosity;

[0049] Figure 11 Modeling a surface diagram for velocity-porosity-pressure;

[0050] Figure 12 A schematic diagram of the "surface" modeling board and the verification results on the well;

[0051] Figure 13 This is a schematic diagram of the structure of a rock physical surface modeling system according to an embodiment of the present invention; Detailed Implementation

[0052] like Figure 1 As shown, a rock physical surface modeling method according to an embodiment of the present invention includes the following steps:

[0053] S1. Obtain the elastic modulus of the carbonate rock along the logging axis and obtain the corresponding rock matrix of the carbonate rock along the logging axis. Assign the elastic modulus of the carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, add the preset pores and / or preset fractures of the carbonate rock along the logging axis to the rock matrix to obtain the rock skeleton.

[0054] S2. Obtain the parameter data of the fluid in the carbonate rock along the logging axis and add it to the rock skeleton;

[0055] S3. Using the dual-hole model, plane wave analysis was performed on the rock skeleton after adding fluid parameters. The wave response characteristics of the carbonate rock containing fluid were calculated, and a physical point model of the carbonate rock containing fluid was established.

[0056] S4. Repeat S1 to S3 for multiple well logs to obtain the physical point model corresponding to each well log.

[0057] S5. Using Markov chains, the physical point models of the carbonate rocks containing fluids corresponding to each well log are unified to obtain the physical surface model of the carbonate rocks containing fluids.

[0058] The elastic modulus of carbonate rocks along the logging axis is assigned to the rock matrix, and pre-defined pores and / or pre-defined fractures of carbonate rocks along the logging axis are added to the rock matrix to obtain the rock skeleton. Fluid parameter data is added to the rock skeleton, and the wave response characteristics of carbonate rocks containing fluids are calculated to establish a physical point model of carbonate rocks containing fluids. The physical point models corresponding to multiple wells are unified to obtain a physical surface model of carbonate rocks containing fluids. Combining the advantages of point and line modeling methods, high-precision and unified rock physical surface modeling is achieved. This can effectively solve the limitations of conventional rock physical modeling, such as the inability to select a unified model, limited accuracy range, and difficulty in being applied to complex carbonate reservoirs, laying the foundation for geophysical prediction of complex deep carbonate reservoirs.

[0059] Furthermore, based on a high-precision rock physics model, it achieves reservoir lithology and physical property inversion of 3D seismic data with better accuracy than conventional inversion.

[0060] Preferably, in the above technical solution, in step S1, obtaining the elastic modulus of the carbonate rock along the logging axis includes:

[0061] S10. Obtain the content of each preset mineral component of carbonate rock along the logging axis based on the logging curve;

[0062] S11. Based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component, obtain the elastic modulus of the carbonate rock along the logging axis.

[0063] Preferably, in the above technical solution, the fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

[0064] Preferably, in the above technical solution, step S2, acquiring parameter data of the fluid in the carbonate rock along the logging axis, includes:

[0065] S20. Using the Batzle-Wang equation, the temperature, pressure, bulk modulus, flow velocity, and density of the fluid in the carbonate rock along the logging axis are obtained based on the logging curves.

[0066] The following is a detailed description of a rock physical surface modeling method of this application through a complete embodiment:

[0067] S100. Obtain the elastic modulus of the carbonate rock along the logging axis, specifically:

[0068] First, based on the logging curves, the content of each preset mineral component of carbonate rock along the logging axis is obtained. Each preset mineral component of carbonate rock includes calcite, quartz and clay. That is, based on the logging curves, the content of calcite, quartz and clay in carbonate rock along the logging axis is obtained respectively.

[0069] Then, based on the Voigt-Ruess-Hill formula, the contents of calcite, quartz, and clay in the carbonate rock along the logging axis, and the elastic modulus of each preset mineral component, the elastic modulus of the carbonate rock along the logging axis is obtained. The Voigt-Ruess-Hill formula is also known as the VRH formula. The specific calculation process is well known to those skilled in the art and will not be elaborated here.

[0070] S101, and obtain the rock matrix corresponding to the carbonate rock along the logging axis, specifically:

[0071] Based on three formulas: The rock matrix modulus is calculated by using a formula to construct a rock skeleton model, i.e., a three-dimensional model of carbonate rocks; where η i It represents the percentage content of each component, where L is the quantity of mineral components, and M is the percentage content of each component. i It is the elastic modulus of each component.

[0072] S102. Using the self-compatibility theory, pre-defined pores and / or pre-defined fractures of the carbonate rock along the logging axis are added to the rock matrix to obtain a rock skeleton, specifically:

[0073] According to the first formula, preset pores and preset fractures of carbonate rock along the logging axis are added to the rock matrix to obtain the modulus of the dry rock skeleton. The first formula is: Where, x i K is the percentage content of the i-th preset mineral component. i This represents the shear modulus of the i-th preset mineral component. P represents the bulk modulus of dry rock after mixing with pores or fractures. *i G represents the equivalent bulk modulus of a material, including pores or cracks. i Represents the shear modulus of the i-th preset mineral component; R represents the shear modulus of dry rock after mixing with pores or fractures. *i It represents the equivalent shear modulus that includes material (such as pores or cracks);

[0074] In this process, an ellipsoid with any aspect ratio is typically used as the preset pore, and a "coin-shaped" crack is typically used as the preset crack for the calculation.

[0075] S103. Obtain parameter data of the fluid in the carbonate rock along the logging axis, including temperature, pressure, bulk modulus, flow velocity, and density. Specifically:

[0076] The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, velocity, and density of the fluid in the carbonate rock along the logging axis based on the logging curves. The Batzle-Wang equation is as follows:

[0077]

[0078]

[0079]

[0080]

[0081] Where, ρ f V represents the density of the fluid in the carbonate rock along the logging axis. f k represents the flow velocity of the fluid in the carbonate rock along the logging axis. f η represents the bulk modulus of the fluid in carbonate rocks along the logging axis. f η represents the viscosity coefficient of the fluid in the carbonate rock along the logging axis, T represents the temperature of the fluid in the carbonate rock along the logging axis, P represents the pressure of the fluid in the carbonate rock along the logging axis, ρ0 represents the reference density of the fluid measured at standard atmospheric pressure, and η0 represents the viscosity coefficient of the fluid measured at standard atmospheric pressure.

[0082] The parameter data of the fluid in the carbonate rock along the logging axis are then added to the rock skeleton.

[0083] S104. Using a dual-pore model, plane wave analysis was performed on the rock skeleton after adding fluid parameters. The wave response characteristics of the carbonate rock containing fluid were calculated, and a cubic equation in wavenumber was obtained as follows:

[0084]

[0085] Among them, c 11 =A+2N+i(Q2φ1-Q1φ2)x1,d 11 =-ρ 11 ω 2 +iω(b1+b2), c 12 =Q1+i(Q2φ1-Q1φ2)x2,d 12 =-ρ 12 ω 2 -iωb1,c 13=Q2+i(Q2φ1-Q1φ2)x3,d 13 =-ρ 13 ω 2 -iωb2,c 21 =Q1-iR1φ2x1,d 21 =-ρ 12 ω 2 -iωb1,c 22 =R1-iR1φ2x2,d 22 =-ρ 22 ω 2 +iωb1,c 23 =-iR1φ2x3,d 23 =0, c 31 =Q2+iR2φ1x1,d 31 =-ρ 13 ω 2 -iωb2,c 32 =iR2φ1x2,d 32 =0, c 33 =R2+iR2φ1x3,d 33 =-ρ 33 ω 2 +iωb2, x1=i(Q1φ2-Q2φ1) / Z, x2=iφ2R1 / Z, x3=-iφ1R2 / Z,

[0086] get This establishes a physical point model of carbonate rocks containing fluids; the dual-pore model is illustrated in the second, third, fourth, and fifth formulas, with the second formula being:

[0087]

[0088] The third formula is:

[0089] The fourth formula is:

[0090] The fifth formula is:

[0091]

[0092] Where N represents the dry rock shear modulus, u represents the rock shear modulus, A represents the rock elastic parameter, ε represents the solid displacement field; Q1 represents the rock elastic parameter, ζ (2) φ1 represents the absolute porosity of the skeleton, ζ represents the local fluid deformation increment generated during seismic wave excitation, and ρ represents the displacement divergence field of flow phase 2 (fluid in microfractures). 11 Represents the combined density parameter. Let ρ represent the second derivative of u. 12 This represents the coupling density between the solid and the fluid phase 1. U (1) The second derivative, ρ 13 This represents the coupling density between the solid and the fluid phase 2. U (2) The second derivative, This represents the first derivative of u. U (1) The first derivative of , b2 represents the dissipation parameter of fluid 2, U (2) The first derivative, ζ (1) φ represents the displacement divergence field of flow phase 1 (fluid in microfractures), φ2 represents the absolute porosity of the fracture, and ρ represents the displacement divergence field of flow phase 1 (fluid in microfractures). 22 Let ρ represent the density of the fluid phase 1, Q2 represent the elastic parameter of the coupling between the microcrack and the skeleton, R2 represent the elastic parameter of the coupling between the microcrack and the fluid, and ρ represent the density of the fluid phase 1. 33 φ represents the density of the flow phase 2. 20 φ represents the local porosity of the body skeleton. 10 This indicates the local porosity of microcracks. R represents the first derivative of ζ. 12 Let η represent the radius of the microcrack, η represent the fluid viscosity, κ1 represent the fluid permeability, and b1 and b2 both represent dissipation parameters.

[0093] Based on the second, third, fourth, and fifth formulas, the wave response characteristics of carbonate rocks containing fluids are calculated, and a physical point model of carbonate rocks containing fluids is established.

[0094] S105. Repeat S100 to S104 for multiple well logs to obtain the physical point model corresponding to each well log. Specifically, one well log corresponds to one physical point model.

[0095] S5. Unification using Markov chains: By using Markov chains, the physical point models of the carbonate rocks containing fluids corresponding to each well logging are unified to obtain the physical surface models of the carbonate rocks containing fluids. The specific calculation process of Markov chains is well known to those skilled in the art and will not be elaborated here.

[0096] In another embodiment, firstly, a dry rock skeleton is obtained, then a rock physical point model is established. Next, the parameters of the fluid in the carbonate rock along the logging axis are analyzed using actual well logging curves. These parameters are then iteratively input into the dry rock skeleton to obtain a physical point model of the carbonate rock containing fluid. Finally, a Markov chain is used to unify the physical point models of the carbonate rock containing fluid corresponding to each well, resulting in a physical surface model of the carbonate rock containing fluid. Specifically:

[0097] S300, based on the core mineral composition of deep, high-temperature, and high-pressure carbonate rocks, mainly includes calcite, quartz, clay, and pores. Referring to the rock physics modeling process shown in Figure 2, the matrix elastic modulus of the multi-mineral composition rock is calculated using the VRH formula. Simultaneously, using the self-compatibility theory (SCA), pre-defined pores and fractures are sequentially added to the rock matrix to obtain the modulus of the dry rock skeleton. The Batzle-Wang equation is used to obtain the bulk modulus and density of the fluid under different temperature and pressure conditions. Then, plane wave analysis is performed using the Biot-Rayleigh dual-pore model to estimate the wave response characteristics of the fluid-bearing rock, thereby establishing a rock physics point model of deep fluid-bearing carbonate rocks. Specifically:

[0098] ①Based on the mineral composition of deep carbonate rock cores, the elastic modulus of the rock matrix is ​​determined using the Voigt-Ruess-Hill formula.

[0099] ② Based on ① above, using the self-compatibility theory (SCA), pores and fractures are sequentially added to the rock matrix, and the modulus of the rock skeleton is obtained using the first formula, which is: Where, x i It is the percentage content of the i-th preset mineral component;

[0100] In this process, an ellipsoid with any aspect ratio is typically used as the preset pore, and a "coin-shaped" crack is typically used as the preset crack for the calculation.

[0101] ③ Based on ① and ②, the Batzle-Wang equation is used to obtain the bulk modulus, flow velocity, and density of the fluid under different temperature and pressure conditions. The Batzle-Wang equation is as follows:

[0102]

[0103]

[0104]

[0105]

[0106] ④ Based on the descriptions of steps ①, ②, and ③ above, the elastic parameters of the fluid-containing rock are obtained. Plane wave analysis is performed using a two-hole model. The two-hole model is shown in the second, third, fourth, and fifth formulas. The second formula is:

[0107]

[0108] The third formula is:

[0109] The fourth formula is:

[0110] The fifth formula is:

[0111]

[0112] ⑤ Based on step ④, estimate the wave response characteristics of fluid-bearing rocks, thereby establishing... Figure 3 The image shows a rock physics point model of deep fluid-bearing carbonate rocks.

[0113] S301, Well Logging Rock Physical Line Modeling:

[0114] Mineral composition analysis is performed based on density, sonic transit time, and neutron porosity logging curves to obtain the content of each component vertically above ground. Based on this, the S300 circulation point modeling process yields the results of rock physics line modeling. By integrating the line modeling models of each well in the exploration area, the probability distributions of parameters such as porosity, pressure, and velocity are calculated. Specifically:

[0115] ① The analyzed rock mainly consists of calcite, quartz, clay, and pores. Mineral composition analysis was performed based on density, sonic transit time, and neutron porosity logging curves to obtain... Figure 4 The content of each component in the vertical direction of the well is shown in Formula 6, which is:

[0116] ρ r =φ×ρ f +V clay ×ρ clay +V quar ×ρ quar +V cal ×ρ cal

[0117] φ CNL =φ×φ f +V clay ×φ clay +V quar ×φ quar +V cal ×φ cal

[0118] Δt=φ×Δt f +V clay ×Δt clay +V quar ×Δt quar +V cal ×Δt cal

[0119] 1=φ+V clay +V quar +V cal

[0120] In the formula, φ represents porosity, and V clay V represents the clay content. quar V indicates the quartz content. cal ρ represents the calcite content. r φ represents the density logging value. CNL ρ represents the compensated neutron logging value, Δt represents the sonic transit time logging value; clay ρ represents the density value of clay minerals. quar ρ represents the density value of quartz minerals. cal φ represents the density value of calcite; f φ represents the neutron value in the fluid. clay φ represents the neutron value of clay minerals. quar φ represents the neutron value of quartz minerals. cal Δt represents the neutron value in calcite. f Δt represents the time difference of fluid acoustic waves. clay The time difference of sound waves in clay minerals, Δt quar The transit time of sound waves in quartz minerals, Δt cal This indicates the time difference of sound waves from calcite.

[0121] ② The S300 circulating rock physics point modeling process yields the results of line modeling. By integrating the line modeling models of each well in the exploration area, porosity, pressure, expected velocity μ, and variance σ are calculated. 2 And calculate based on the probability density function of the normal distribution, as follows Figures 5 to 7 The probability distributions of parameters such as porosity, pressure, and velocity are shown in the seventh formula, which is:

[0122]

[0123] S302. Rock Physics Surface Modeling: The relationships between parameters such as porosity, pressure, and velocity in the models of various wells in the exploration area can be considered stochastic. Through Monte Carlo (Markov chain) simulation, the relationship between porosity, pressure, and velocity is established, thereby creating a high-precision and unified rock physics template. Specifically:

[0124] ① Based on the probability distributions of parameters such as porosity, pressure, and velocity, assuming pressure as the objective function f(x), where x is a multidimensional variable such as porosity and velocity, the specific form of f(x) is quite complex. Therefore, Monte Carlo integration is used to establish the expectation of multiple parameters (porosity, pressure, and velocity) under a single probability distribution, as shown in Formula 8. Formula 8 is: E f (Y) = ∫f(x)q(x)dx, where q(x) is the probability distribution of x in the interval. Then, n samples are drawn from q(x). When n is sufficiently large, the sample mean can be used to approximate the distribution.

[0125] ② Use Markov chains to extract samples corresponding to each rock physical parameter:

[0126] P(X t+1 =j|X t ,X t-1 ,…)=P(X t+1 =j|X t );

[0127] Where P represents probability and Xt represents sample. The transition probability is the change from time t+1 to time t. for:

[0128]

[0129] Where π t This represents the marginal distribution of a Markov chain at time t, which converges to a stationary distribution as t→∞. like At this point, the detailed equilibrium equation is obtained: π(i)P ij =π(j)P ji The stationarity of the target distribution can be verified through detailed equilibrium equations.

[0130] ③ Using the detailed balance equation in ② above as the verification standard, construct a suitable Markov chain using the Metropolis-Hastings algorithm, given an X t Given the current state, the next step X is generated. t+1 The state. First, construct a suitable proposal distribution g(·|X). t X0 is generated from a certain g distribution; then the following process is repeated for t = 0, 1, 2, 3…:

[0131] At time t, the Markov chain is X. t =x t Sampling y~q(x|x t Then, from a uniform distribution u ~ U[0,1], if Then accept the transfer x t →y, i.e., X t+1 =y, otherwise the transfer is not accepted, i.e., X t+1 =x t Finally, add t and repeat the above process.

[0132] ④ Based on ①, ②, and ③, construct an appendix. Figures 8 to 10 The P-wave velocity-formation pressure graph shown is a variation of porosity.

[0133] ⑤ Based on ④, multiple simulations and fitting were performed to obtain the following results: Figure 11 The velocity-porosity-pressure surface modeling template shown yields the physical surface model, which is obtained through... Figure 12 The well verification results shown demonstrate that the surface modeling results proposed in this invention for deep carbonate oil and gas reservoirs are highly accurate and widely applicable, effectively solving the thorny problem of model unification.

[0134] This application presents a rock physical surface modeling method that combines the advantages of point and line modeling to achieve a high-precision, unified rock physical surface modeling method, which has the following characteristics:

[0135] 1) For deep, high-temperature, and high-pressure carbonate reservoirs, the elastic modulus of the matrix is ​​calculated based on the mineral composition of the rock using the VRH formula. Simultaneously, using the self-compatible theory (SCA), pores and fractures are sequentially added to the rock matrix to obtain the rock skeleton. The Batzle-Wang equation is employed to obtain the bulk modulus, velocity, and density of the fluid under different temperature and pressure conditions. Then, a dual-pore model is used to analyze the wave response characteristics of fluid-bearing rocks, thereby establishing a rock physical point model of deep fluid-bearing carbonate rocks.

[0136] 2) Based on well logging curves such as density, sonic transit time, and neutron porosity, mineral composition analysis is performed to obtain the content of each component in the vertical direction above the well. The circulation point modeling process (1) yields the line modeling model.

[0137] 3) By combining the line modeling models of each well in the exploration area, the probability distribution of parameters such as porosity, pressure, and velocity is obtained; through Monte Carlo simulation, a high-precision and unified rock physics surface modeling model is obtained.

[0138] This invention aims to provide a rock physics modeling method with significant advantages applicable to deep, high-temperature, and high-pressure carbonate reservoirs with strong heterogeneity, effectively addressing the limitations of conventional rock physics modeling, such as the inability to select a unified model, limited accuracy range, and difficulty in applying to complex carbonate reservoirs. This invention focuses on deep carbonate oil and gas reservoirs, firstly by performing rock physics point modeling. The matrix elastic modulus of multi-mineral composed rocks is calculated using the VRH formula; based on this, the self-compatibility theory (SCA) is used to obtain the rock skeleton; the Batzle-Wang equation is selected to obtain the bulk modulus, velocity, and density of fluids under different temperature and pressure conditions. Plane wave analysis is performed using a dual-pore model to estimate the wave response characteristics of fluid-bearing rocks, thereby establishing a rock physics point model of deep fluid-bearing carbonate rocks. Then, mineral composition analysis is performed based on density, sonic transit time, and neutron porosity logging curves to obtain the content of each component above ground, and the point modeling process is iterated to obtain the line modeling results. By combining the line modeling results from various wells, the probability distributions of parameters such as porosity, pressure, and velocity are obtained, and Monte Carlo simulation is used to achieve rock physics surface modeling. This invention combines the advantages of point and line modeling methods to achieve high-precision, unified rock physical surface modeling, laying the foundation for geophysical prediction of complex deep carbonate reservoirs.

[0139] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given in this application. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of this invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0140] like Figure 13 As shown, a rock physical surface modeling system 200 according to an embodiment of the present invention includes a first acquisition and addition module 210, a second acquisition and addition module 220, a plane wave analysis module 230, a calling module 240, and a data unification module 250;

[0141] The first acquisition and addition module 210 is used to: acquire the elastic modulus of carbonate rock along the logging axis, acquire the rock matrix corresponding to the carbonate rock along the logging axis, and assign the elastic modulus of carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, the preset pores and / or preset fractures of carbonate rock along the logging axis are added to the rock matrix to obtain the rock skeleton.

[0142] The second acquisition and addition module 220 is used to: acquire parameter data of fluid in carbonate rock along the logging axis and add it to the rock skeleton;

[0143] The plane wave analysis module 230 is used to: perform plane wave analysis on the rock skeleton after adding fluid parameters using a dual-hole model, calculate the wave response characteristics of carbonate rocks containing fluid, and establish a physical point model of carbonate rocks containing fluid.

[0144] The calling module 240 is used to repeatedly call the first acquisition and addition module 210, the second acquisition and addition module 220 and the plane wave analysis module 230 for multiple well logs to obtain the physical point model corresponding to each well log.

[0145] The data unification module 250 is used to: unify the physical point models of the carbonate rocks containing fluids corresponding to each well log through a Markov chain, so as to obtain the physical surface model of the carbonate rocks containing fluids.

[0146] The elastic modulus of carbonate rocks along the logging axis is assigned to the rock matrix, and pre-defined pores and / or pre-defined fractures of carbonate rocks along the logging axis are added to the rock matrix to obtain the rock skeleton. Fluid parameter data is added to the rock skeleton, and the wave response characteristics of carbonate rocks containing fluids are calculated to establish a physical point model of carbonate rocks containing fluids. The physical point models corresponding to multiple wells are unified to obtain a physical surface model of carbonate rocks containing fluids. Combining the advantages of point and line modeling methods, high-precision and unified rock physical surface modeling is achieved. This can effectively solve the limitations of conventional rock physical modeling, such as the inability to select a unified model, limited accuracy range, and difficulty in being applied to complex carbonate reservoirs, laying the foundation for geophysical prediction of complex deep carbonate reservoirs.

[0147] Preferably, in the above technical solution, the process by which the first acquisition and addition module 210 acquires the elastic modulus of carbonate rock along the logging axis includes:

[0148] The content of each preset mineral component of carbonate rock along the logging axis is obtained from the logging curve.

[0149] The elastic modulus of the carbonate rock along the logging axis is obtained based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component.

[0150] Preferably, in the above technical solution, the fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

[0151] Preferably, in the above technical solution, the process by which the second acquisition and addition module 220 acquires parameter data of the fluid in the carbonate rock along the logging axis includes:

[0152] The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, flow velocity, and density of the fluid in the carbonate rock along the logging axis based on the logging curves.

[0153] The parameters and steps for implementing the corresponding functions of each unit module in the rock physical surface modeling system 200 of the present invention described above can be referred to the parameters and steps in the embodiments of the rock physical surface modeling method described above, and will not be repeated here.

[0154] An embodiment of the present invention provides a storage medium storing instructions, which, when read by a computer, cause the computer to execute any of the above-described rock physical surface modeling methods.

[0155] An electronic device according to an embodiment of the present invention includes a processor and the aforementioned storage medium, wherein the processor executes instructions in the storage medium.

[0156] The electronic device can be a computer, mobile phone, etc., and the corresponding program is computer software or mobile APP, etc. The parameters and steps of the electronic device of the present invention can be referred to the parameters and steps in the embodiment of the rock physical surface modeling method above, and will not be repeated here.

[0157] Those skilled in the art will know that this invention can be implemented as a system, method, or computer program product.

[0158] Therefore, this disclosure can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product in one or more computer-readable media, the computer-readable medium containing computer-readable program code.

[0159] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0160] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for modeling physical surfaces of rocks, characterized in that, include: S1. Obtain the elastic modulus of the carbonate rock along the logging axis and obtain the corresponding rock matrix of the carbonate rock along the logging axis. Assign the elastic modulus of the carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, add the preset pores and / or preset fractures of the carbonate rock along the logging axis to the rock matrix to obtain the rock skeleton. S2. Obtain the parameter data of the fluid in the carbonate rock along the logging axis and add it to the rock skeleton; S3. Using the dual-hole model, plane wave analysis was performed on the rock skeleton after adding fluid parameters. The wave response characteristics of the carbonate rock containing fluid were calculated, and a physical point model of the carbonate rock containing fluid was established. The process of establishing a physical point model for carbonate rocks containing fluids is as follows: Plane wave analysis was performed on the rock skeleton after adding fluid parameters using a two-pore model. The wave response characteristics of the carbonate rock containing fluid were calculated, and a cubic equation in wavenumber was obtained: among them,c 11 =A+2N+i(Q2φ1-Q1φ2)x1,d 11 =-ρ 11 oh 2 +iω(b1+b2),c 12 =Q1+i(Q2φ1-Q1φ2)x2,d 12 =-ρ 12 oh 2 -iωb1,c 13 =Q2+i(Q2φ1-Q1φ2)x3,d 13 =-ρ 13 oh 2 -iωb2,c 21 =Q1-iR1φ2x1,d 21 =-ρ 12 oh 2 -iωb1,c 22 =R1-iR1φ2x2,d 22 =-ρ 22 oh 2 +iωb1,c 23 =-iR1φ2x3,d 23 =0,c 31 =Q2+iR2φ1x1,d 31 =-ρ 13 oh 2 -iωb2,c 32 =iR2φ1x2,d 32 =0,c 33 =R2+iR2φ1x3,d 33 =-ρ 33 oh 2 +iωb2,x1=i(Q1φ2-Q2φ1) / Z,x2=iφ2R1 / Z,x3=-iφ1R2 / Z, get The double-hole model is shown in the second, third, fourth, and fifth formulas. The second formula is: The third formula is: The fourth formula is: The fifth formula is: Where N represents the dry rock shear modulus, u represents the rock shear modulus, A represents the rock elastic parameter, ε represents the solid displacement field; Q1 represents the rock elastic parameter, ζ (2) The flow phase 2 displacement divergence field of the fluid in the microfracture is represented by φ1, which represents the absolute porosity of the skeleton, and ζ represents the local fluid deformation increment generated during seismic wave excitation. 11 Represents the combined density parameter. Let ρ represent the second derivative of u. 12 The coupling density of the flow phase 1 between the solid and the fluid in the microcracks. U (1) The second derivative, ρ 13 This represents the coupling density between the solid and the fluid phase 2. U (2) The second derivative, This represents the first derivative of u. U (1) The first derivative of , b2 represents the dissipation parameter of the fluid in the microcrack. U (2) The first derivative, ζ (1) φ2 represents the displacement divergence field of the fluid phase 1 in the microfracture, φ2 represents the absolute porosity of the fracture, and ρ represents the displacement divergence field of the fluid phase 1 in the microfracture. 22 Q1 represents the density of the fluid phase 1 within the microcrack, Q2 represents the elastic parameter of the coupling between the microcrack and the skeleton, R2 represents the elastic parameter of the coupling between the microcrack and the fluid, and ρ represents the density of the fluid phase 1 within the microcrack. 33 φ represents the density of the fluid phase 2 within the microcrack. 20 φ represents the local porosity of the body skeleton. 10 This indicates the local porosity of microcracks. R represents the first derivative of ζ. 12 Let η represent the radius of the microcrack, η represent the fluid viscosity, κ1 represent the fluid permeability, and b1 and b2 both represent dissipation parameters. Based on the second, third, fourth and fifth formulas, the wave response characteristics of carbonate rocks containing fluids are calculated, and a physical point model of carbonate rocks containing fluids is established. S4. Repeat S1 to S3 for multiple well logs to obtain the physical point model corresponding to each well log. S5. Using Markov chains, the physical point models of the carbonate rocks containing fluids corresponding to each well logging are unified to obtain the physical surface model of the carbonate rocks containing fluids. The process of obtaining the elastic modulus of carbonate rock along the logging axis includes: The content of each preset mineral component of carbonate rock along the logging axis is obtained from the logging curve. Based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component, the elastic modulus of the carbonate rock along the logging axis is obtained. The acquisition of parameter data of fluid in carbonate rock along the logging axis includes: The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, flow velocity and density of the fluid in the carbonate rock along the logging axis based on the logging curves. It also includes: mineral composition analysis based on density, sonic transit time, and neutron porosity logging curves to obtain the content of each component vertically above ground, characterized by the sixth formula, which is: ρ r =φ×ρ f +V clay ×ρ clay +V quar ×ρ quar +V cal ×ρ cal φ CNL =φ×φ f +V clay ×φ clay +V quar ×φ quar +V cal ×φ cal Δt=φ×Δt f +V clay ×Δt clay +V quar ×Δt quar +V cal ×Δt cal 1=φ+V clay +V quar +V cal φ represents porosity, V clay V represents the clay content. quar V indicates the quartz content. cal ρ represents the calcite content. r φ represents the density logging value. CNL ρ represents the compensated neutron logging value, Δt represents the sonic transit time logging value; clay ρ represents the density value of clay minerals. quar ρ represents the density value of quartz minerals. cal φ represents the density value of calcite; f φ represents the neutron value in the fluid. clay φ represents the neutron value of clay minerals. quar φ represents the neutron value of quartz minerals. cal Δt represents the neutron value in calcite. f Δt represents the time difference of fluid acoustic waves. clay The time difference of sound waves in clay minerals, Δt quar The transit time of sound waves in quartz minerals, Δt cal This indicates the time difference of sound waves from calcite.

2. The rock physical surface modeling method according to claim 1, characterized in that, The fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

3. A rock physical surface modeling system, characterized in that, It includes a first acquisition and addition module, a second acquisition and addition module, a plane wave analysis module, a calling module, and a data unification module; The first acquisition and addition module is used to: acquire the elastic modulus of carbonate rock along the logging axis, acquire the rock matrix corresponding to the carbonate rock along the logging axis, and assign the elastic modulus of carbonate rock along the logging axis to the rock matrix. Using the self-compatibility theory, the preset pores and / or preset fractures of carbonate rock along the logging axis are added to the rock matrix to obtain the rock skeleton. The second acquisition and addition module is used to: acquire parameter data of fluid in carbonate rock along the logging axis and add it to the rock skeleton; The plane wave analysis module is used to: perform plane wave analysis on the rock skeleton after adding fluid parameters using a dual-hole model, calculate the wave response characteristics of carbonate rocks containing fluid, and establish a physical point model of carbonate rocks containing fluid. The process of establishing a physical point model for carbonate rocks containing fluids is as follows: Plane wave analysis was performed on the rock skeleton after adding fluid parameters using a two-pore model. The wave response characteristics of the carbonate rock containing fluid were calculated, and a cubic equation in wavenumber was obtained: among them,c 11 =A+2N+i(Q2φ1-Q1φ2)x1,d 11 =-ρ 11 oh 2 +iω(b1+b2),c 12 =Q1+i(Q2φ1-Q1φ2)x2,d 12 =-ρ 12 oh 2 -iωb1,c 13 =Q2+i(Q2φ1-Q1φ2)x3,d 13 =-ρ 13 oh 2 -iωb2,c 21 =Q1-iR1φ2x1,d 21 =-ρ 12 oh 2 -iωb1,c 22 =R1-iR1φ2x2,d 22 =-ρ 22 oh 2 +iωb1,c 23 =-iR1φ2x3,d 23 =0,c 31 =Q2+iR2φ1x1,d 31 =-ρ 13 oh 2 -iωb2,c 32 =iR2φ1x2,d 32 =0,c 33 =R2+iR2φ1x3,d 33 =-ρ 33 oh 2 +iωb2,x1=i(Q1φ2-Q2φ1) / Z,x2=iφ2R1 / Z,x3=-iφ1R2 / Z, get The double-hole model is shown in the second, third, fourth, and fifth formulas. The second formula is: The third formula is: The fourth formula is: The fifth formula is: Where N represents the dry rock shear modulus, u represents the rock shear modulus, A represents the rock elastic parameter, ε represents the solid displacement field; Q1 represents the rock elastic parameter, ζ (2) The flow phase 2 displacement divergence field of the fluid in the microfracture is represented by φ1, which represents the absolute porosity of the skeleton, and ζ represents the local fluid deformation increment generated during seismic wave excitation. 11 Represents the combined density parameter. Let ρ represent the second derivative of u. 12 The coupling density of the flow phase 1 between the solid and the fluid in the microcracks. U (1) The second derivative, ρ 13 This represents the coupling density between the solid and the fluid phase 2. U (2) The second derivative, This represents the first derivative of u. U (1) The first derivative of , b2 represents the dissipation parameter of the fluid in the microcrack. U (2) The first derivative, ζ (1) φ2 represents the displacement divergence field of the fluid phase 1 in the microfracture, φ2 represents the absolute porosity of the fracture, and ρ represents the displacement divergence field of the fluid phase 1 in the microfracture. 22 Q1 represents the density of the fluid phase 1 within the microcrack, Q2 represents the elastic parameter of the coupling between the microcrack and the skeleton, R2 represents the elastic parameter of the coupling between the microcrack and the fluid, and ρ represents the density of the fluid phase 1 within the microcrack. 33 φ represents the density of the fluid phase 2 within the microcrack. 20 φ represents the local porosity of the body skeleton. 10 This indicates the local porosity of microcracks. R represents the first derivative of ζ. 12 Let η represent the radius of the microcrack, η represent the fluid viscosity, κ1 represent the fluid permeability, and b1 and b2 both represent dissipation parameters. Based on the second, third, fourth and fifth formulas, the wave response characteristics of carbonate rocks containing fluids are calculated, and a physical point model of carbonate rocks containing fluids is established. The calling module is used to repeatedly call the first acquisition and addition module, the second acquisition and addition module, and the plane wave analysis module for multiple well logs to obtain the physical point model corresponding to each well log. The data unification module is used to: unify the physical point models of the carbonate rocks containing fluids corresponding to each well log through a Markov chain, and obtain the physical surface model of the carbonate rocks containing fluids. The process by which the first acquisition and addition module acquires the elastic modulus of carbonate rock along the logging axis includes: The content of each preset mineral component of carbonate rock along the logging axis is obtained from the logging curve. Based on the Voigt-Ruess-Hill formula, the content of each preset mineral component along the logging axis, and the elastic modulus of each preset mineral component, the elastic modulus of the carbonate rock along the logging axis is obtained. The process by which the second acquisition and addition module acquires parameter data of the fluid in the carbonate rock along the logging axis includes: The Batzle-Wang equation was used to obtain the temperature, pressure, bulk modulus, flow velocity and density of the fluid in the carbonate rock along the logging axis based on the logging curves. It also includes: mineral composition analysis based on density, sonic transit time, and neutron porosity logging curves to obtain the content of each component vertically above ground, characterized by the sixth formula, which is: ρ r =φ×ρ f +V clay ×ρ clay +V quar ×ρ quar +V cal ×ρ cal φ CNL =φ×φ f +V clay ×φ clay +V quar ×φ quar +V cal ×φ cal Δt=φ×Δt f +V clay ×Δt clay +V quar ×Δt quar +V cal ×Δt cal 1=φ+V clay +V quar +V cal φ represents porosity, V clay V represents the clay content. quar V indicates the quartz content. cal ρ represents the calcite content. r φ represents the density logging value. CNL ρ represents the compensated neutron logging value, Δt represents the sonic transit time logging value; clay ρ represents the density value of clay minerals. quar ρ represents the density value of quartz minerals. cal φ represents the density value of calcite; f φ represents the neutron value in the fluid. clay φ represents the neutron value of clay minerals. quar φ represents the neutron value of quartz minerals. cal Δt represents the neutron value in calcite. f Δt represents the time difference of fluid acoustic waves. clay The time difference of sound waves in clay minerals, Δt quar The transit time of sound waves in quartz minerals, Δt cal This indicates the time difference of sound waves from calcite.

4. The rock physical surface modeling system according to claim 3, characterized in that, The fluid parameters include temperature, pressure, bulk modulus, flow rate, and density.

5. A storage medium, characterized in that, The storage medium stores instructions that, when read by a computer, cause the computer to execute a rock physical surface modeling method as described in any one of claims 1 to 2.

6. An electronic device, characterized in that, It includes a processor and the storage medium of claim 5, wherein the processor executes instructions in the storage medium.

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