Sedimentary facies constraint simulation method and device based on three-dimensional dynamic data, electronic equipment and medium

Abstract

The invention discloses a sedimentary facies constraint simulation method and device based on three-dimensional dynamic data, electronic equipment and a medium. The method comprises the steps of determining a three-dimensional grid space range, and performing data preprocessing through phase data; determining a calculation diameter and a phase control value range of the three-dimensional spherical model through a variation function; carrying out normal transformation on the phase-by-phase logging data and the seismic data, and then carrying out Bayesian-sequential Gaussian simulation; and performing normal inverse transformation on a simulation result in the grid to finish simulation calculation. Spatial sample points are expanded to the spatial three-dimensional sphere model, so that the value range of to-be-simulated points can dynamically change, and the correlation of sample point values is improved; on the basis of a variation function, under the condition of considering the source direction, data in the three-dimensional sphere model is screened, sedimentary facies are introduced into an algorithm for constraint, the accuracy of sampling point value taking can be guaranteed, geological cognition and sedimentary modes can be followed, and more accurate input data are provided for simulation prediction.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, and more specifically, to a method, apparatus, electronic device, and medium for sedimentary facies-constrained simulation based on three-dimensional dynamic data. Background Technology

[0002] Reservoir prediction technology is a powerful tool that can comprehensively utilize multiple types of data to accurately predict and describe changes in lithology, thickness, and physical properties. This technology encompasses various methods, including seismic attribute analysis, pre-stack and post-stack inversion, geostatistical simulation, and cutting-edge deep learning. Among these, geological modeling technology based on geostatistical principles has received widespread attention and in-depth research from the academic community due to its ability to integrate multidisciplinary data.

[0003] Traditional geostatistical methods often struggle to adequately account for the accuracy and precision challenges posed by spatial variations between sampling points when integrating well logging and seismic data, given the characteristics of clastic reservoirs. In 1999, Ronald A. Behrens et al. pioneered a sequential Gaussian simulation method based on Bayesian theory, which effectively integrates well logging and seismic information. They successfully constructed a three-dimensional model using two layers of seismic attributes as constraints and validated the method's significant effectiveness and feasibility through practical applications.

[0004] Subsequently, in 2005, He Weisheng et al. further discovered that directly using three-dimensional seismic attribute volumes as constraints could also achieve excellent results. In the same year, Wu Shenghe et al. systematically introduced the theory, application, and prospects of multi-point geostatistics. In 2010, Wang Jiahua, based on multi-point geostatistics, conducted in-depth research on the integrated simulation of well logging and seismic data.

[0005] In the second decade of the 21st century, Kavousi et al. established a reservoir parameter simulation method under seismic attribute constraints using the sequential Gaussian simulation method in 2013, strongly demonstrating the feasibility of using seismic data in reservoir parameter simulation. In 2017, Reinaldo et al. proposed a new method combining frequency methods and phase models for the simulation and prediction of unconventional reservoirs, further validating the feasibility of combining seismic data with phase models for stochastic simulation. In 2018, Li et al. applied the Bayesian sequential Gaussian simulation method to velocity model simulation, successfully extending its application to seismic imaging data processing. In 2020, Dario Grana proposed a Bayesian elastic inversion method based on multiple prior information, demonstrating not only the ability of Bayesian methods to integrate multiple data sets but also showcasing its potential in simulating elastic parameters.

[0006] In the area of ​​facies-constrained simulation methods, scholars both domestically and internationally have conducted extensive research, resulting in various constraint methods, techniques, and evaluation methods. These methods have achieved significant application results in reservoirs of different sedimentary types and models. Methods and techniques for three-dimensional simulation based on geostatistical methods that integrate sedimentary facies, well logging, and seismic data have gradually matured thanks to the unremitting efforts of numerous scholars.

[0007] However, the above techniques are limited because the location of static data is fixed, while the points to be calculated are dynamically changing. Conventional methods cannot adapt to changes in the spatial location of the points to be calculated, resulting in low accuracy of simulation results.

[0008] Therefore, it is necessary to develop a sedimentation phase constraint simulation method, device, electronic equipment, and medium based on three-dimensional dynamic data.

[0009] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0010] This invention proposes a sedimentary facies-constrained simulation method, apparatus, electronic device, and medium based on three-dimensional dynamic data. By extending the selection of spatial sample points from static one-dimensional cylinders to a three-dimensional spherical model, the range of values ​​for the simulated points can dynamically change with their location, improving the correlation of sample point values. Furthermore, considering the source direction, the data within the three-dimensional spherical model is filtered based on the variogram, and sedimentary facies are introduced into the algorithm for constraint. This ensures the accuracy of sample point values ​​while adhering to geological understanding and sedimentary patterns, providing more accurate input data for simulation prediction.

[0011] In a first aspect, embodiments of this disclosure provide a sedimentary phase-constrained simulation method based on three-dimensional dynamic data, including:

[0012] Determine the spatial range of the three-dimensional mesh and perform data preprocessing using phase data;

[0013] The computational diameter and phase control value range of the three-dimensional spherical model are determined by using the variogram function.

[0014] Normal transformations were performed on each relative well logging data and seismic data, followed by Bayesian-sequential Gaussian simulations.

[0015] The simulation results within the grid are subjected to an inverse normal transformation to complete the simulation calculation.

[0016] As a specific implementation of this disclosure, data preprocessing includes:

[0017] The well logging data and various seismic data are labeled to determine the phase of each grid point.

[0018] As a specific implementation of this disclosure, the variation function is:

[0019]

[0020] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

[0021] As a specific implementation of this disclosure, the source direction and spatial value range are determined based on phase data, which is the phase control value range.

[0022] As a specific implementation of this disclosure, Bayesian sequential Gaussian simulation includes:

[0023] ① Define a random path such that each node is calculated only once;

[0024] ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method;

[0025] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0026] ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution;

[0027] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

[0028] As a specific implementation of this disclosure, it also includes:

[0029] Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

[0030] Secondly, embodiments of this disclosure also provide a sedimentary phase constraint simulation apparatus based on three-dimensional dynamic data, comprising:

[0031] The preprocessing module determines the spatial range of the three-dimensional mesh and performs data preprocessing using phase data.

[0032] The calculation module determines the calculation diameter of the three-dimensional spherical model and the range of phase control values ​​through the variogram function;

[0033] The simulation module performs normal transformations on well logging data and seismic data relative to each other, and then performs Bayesian-sequential Gaussian simulations.

[0034] The inverse transformation module performs a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

[0035] As a specific implementation of this disclosure, data preprocessing includes:

[0036] The well logging data and various seismic data are labeled to determine the phase of each grid point.

[0037] As a specific implementation of this disclosure, the variation function is:

[0038]

[0039] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

[0040] As a specific implementation of this disclosure, the source direction and spatial value range are determined based on phase data, which is the phase control value range.

[0041] As a specific implementation of this disclosure, Bayesian sequential Gaussian simulation includes:

[0042] ① Define a random path such that each node is calculated only once;

[0043] ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method;

[0044] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0045] ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution;

[0046] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

[0047] As a specific implementation of this disclosure, it also includes:

[0048] Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

[0049] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0050] Memory, which stores executable instructions;

[0051] A processor that executes the executable instructions in the memory to implement the depositional phase-constrained simulation method based on three-dimensional dynamic data.

[0052] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned depositional phase constraint simulation method based on three-dimensional dynamic data.

[0053] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0054] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0055] Figure 1 A flowchart illustrating the steps of a depositional phase constraint simulation method based on three-dimensional dynamic data according to an embodiment of the present invention is shown.

[0056] Figure 2 A schematic diagram of a sphere model considering the source direction is shown according to an embodiment of the present invention.

[0057] Figure 3 A schematic diagram of a deposition phase control according to an embodiment of the present invention is shown.

[0058] Figure 4a , Figure 4b Schematic diagrams showing a planar comparison before and after deposition phase control according to an embodiment of the present invention are shown.

[0059] Figure 5 A block diagram of a depositional phase constraint simulation apparatus based on three-dimensional dynamic data is shown according to an embodiment of the present invention.

[0060] Explanation of reference numerals in the attached figures:

[0061] 201. Preprocessing module; 202. Calculation module; 203. Simulation module; 204. Inverse transformation module. Detailed Implementation

[0062] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0063] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0064] Example 1

[0065] Figure 1 A flowchart illustrating the steps of a depositional phase constraint simulation method based on three-dimensional dynamic data according to an embodiment of the present invention is shown.

[0066] like Figure 1 As shown, the sedimentary facies-constrained simulation method based on three-dimensional dynamic data includes:

[0067] Step 101: Determine the spatial range of the three-dimensional mesh and perform data preprocessing using phase data;

[0068] Step 102: Determine the calculated diameter of the three-dimensional spherical model and the range of phase control values ​​using the variogram function;

[0069] Step 103: Perform normal transformation on each relative well logging data and seismic data, and then perform Bayesian-sequential Gaussian simulation;

[0070] Step 104: Perform an inverse normal transformation on the simulation results within the grid to complete the simulation calculation.

[0071] In one example, data preprocessing includes:

[0072] The well logging data and various seismic data are labeled to determine the phase of each grid point.

[0073] In one example, the variation function is:

[0074]

[0075] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

[0076] In one example, the source direction and spatial value range are determined based on phase data, which is the phase control value range.

[0077] In one example, a Bayesian-sequential Gaussian simulation includes:

[0078] ① Define a random path such that each node is calculated only once;

[0079] ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method;

[0080] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0081] ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution;

[0082] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

[0083] In one example, it also includes:

[0084] Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

[0085] Specifically, the research objective of stochastic simulation is to predict reservoirs and characterize their physical properties and spatial distribution. Reservoir formation and development are influenced by sedimentation, diagenesis, and tectonic processes; therefore, before simulation prediction, it is necessary to analyze the geological background and provenance of the specific study area. Based on provenance analysis, this invention employs variogram tools to select and weight spatial data.

[0086] The formula for calculating the function of variation is:

[0087]

[0088] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents two sampled values ​​with a step size (i.e., hysteresis) h. In practical applications, the sample point positions are not precisely measured in steps h, making it difficult to obtain samples with a step size h precisely. Giving the step size h a range makes it much easier to determine the value; a smaller range results in higher accuracy, while a larger range reduces accuracy. This range has different names in different contexts: when defining the step size, it's called the hysteresis range; when defining the azimuth angle, it's called the azimuth range; when defining the azimuth extension, it's called the bandwidth; when defining the horizontal angle, it's called the horizontal angle range; and when defining the vertical angle, it's called the vertical angle range. This invention uses the classic truncated wedge model to describe the range of values ​​for the variogram, and the coordinate system used is the Cartesian coordinate system.

[0089] This invention employs phasing modeling to perform phasing calculations on simulated data. The main purpose of constructing the phasing model is to constrain the stochastic simulation of sedimentary reservoirs based on the spatial distribution characteristics of sedimentary facies. This method can also be called a "two-step" model building: first, discrete variable modeling is used to simulate various geological bodies or sedimentary facies; then, based on the previous step of simulating different sedimentary facies, appropriate algorithms are used to construct a physical property model. The overall approach is based on sedimentary facies models, using mathematical statistics methods to quantitatively characterize the spatial distribution characteristics and heterogeneity of sedimentary bodies.

[0090] This invention employs a sequential Gaussian simulation method based on Bayesian theory. This method requires that the data conform to the basic conditions of the sequential Gaussian algorithm: the well logging data and various seismic data involved in the calculation must follow a normal distribution. If they do not, a normal transformation needs to be performed on the data, and a bivariate normality test is used for verification. Combining the principles of Bayesian three-dimensional simulation and phased-array data screening methods, the specific steps of the phased-array stochastic simulation method of this invention are as follows:

[0091] (1) Determine the scope of the three-dimensional grid space (work area), use phase data to mark the well logging data and various seismic data, and determine which phase each type of data of each grid point belongs to;

[0092] (2) The calculation diameter of the three-dimensional spherical model is determined by the step size of the variation function, and then the source direction and spatial value range are determined by phase separation based on the phase data, that is, the “phase control value range” is determined.

[0093] (3) Perform normal transformation on each relative well logging data and all seismic data involved in the calculation;

[0094] (4) Perform a binary normality test on the data after normal transformation. If it meets the test, proceed to the next step; otherwise, select other methods.

[0095] (5) Bayesian-sequential Gaussian simulations were performed using spatial sedimentary facies as a guide:

[0096] ① Define a random path such that each node is calculated only once;

[0097] ② For all points (sample points and simulated points) within the “phase control range”, the mean and variance of the cumulative conditional distribution function (ccdf) are calculated using the ordinary kriging method;

[0098] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0099] ④ The simulated values ​​are obtained by random sampling from the conditional distribution;

[0100] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited;

[0101] (6) Perform a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

[0102] It is important to note that in step ③, calculating the kriging parameters from the well logging data, especially when there are few wells in the work area, and particularly considering the source direction and using phase separation calculations, further reduces the number of well logging samples, leading to insufficient samples and making it difficult to obtain the kriging parameters. There are two solutions to this problem: one is to transform the seismic data into "pseudo-logging data" at the well logging scale through linear transformation before performing statistical calculations; the other is to use the kriging parameters from the seismic data instead of the well logging data for direct calculation. The common premise of both solutions is that the seismic data, as the constraint data, needs to undergo rock physical analysis with the well logging data during selection to ensure statistical correlation between the two. Under further facies control conditions, the two data sets should have related characteristic expressions for the same lithology.

[0103] Example 2

[0104] The present invention also provides a sedimentary phase constraint simulation device based on three-dimensional dynamic data, comprising:

[0105] The preprocessing module determines the spatial range of the three-dimensional mesh and performs data preprocessing using phase data.

[0106] The calculation module determines the calculation diameter of the three-dimensional spherical model and the range of phase control values ​​through the variogram function;

[0107] The simulation module performs normal transformations on well logging data and seismic data relative to each other, and then performs Bayesian-sequential Gaussian simulations.

[0108] The inverse transformation module performs a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

[0109] In one example, data preprocessing includes:

[0110] The well logging data and various seismic data are labeled to determine the phase of each grid point.

[0111] In one example, the variation function is:

[0112]

[0113] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

[0114] In one example, the source direction and spatial value range are determined based on phase data, which is the phase control value range.

[0115] In one example, a Bayesian-sequential Gaussian simulation includes:

[0116] ① Define a random path such that each node is calculated only once;

[0117] ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method;

[0118] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0119] ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution;

[0120] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

[0121] In one example, it also includes:

[0122] Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

[0123] Specifically, the research objective of stochastic simulation is to predict reservoirs and characterize their physical properties and spatial distribution. Reservoir formation and development are influenced by sedimentation, diagenesis, and tectonic processes; therefore, before simulation prediction, it is necessary to analyze the geological background and provenance of the specific study area. Based on provenance analysis, this invention employs variogram tools to select and weight spatial data.

[0124] The formula for calculating the function of variation is:

[0125]

[0126] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents two sampled values ​​with a step size (i.e., hysteresis) h. In practical applications, the sample point positions are not precisely measured in steps h, making it difficult to obtain samples with a step size h precisely. Giving the step size h a range makes it much easier to determine the value; a smaller range results in higher accuracy, while a larger range reduces accuracy. This range has different names in different contexts: when defining the step size, it's called the hysteresis range; when defining the azimuth angle, it's called the azimuth range; when defining the azimuth extension, it's called the bandwidth; when defining the horizontal angle, it's called the horizontal angle range; and when defining the vertical angle, it's called the vertical angle range. This invention uses the classic truncated wedge model to describe the range of values ​​for the variogram, and the coordinate system used is the Cartesian coordinate system.

[0127] This invention employs phasing modeling to perform phasing calculations on simulated data. The main purpose of constructing the phasing model is to constrain the stochastic simulation of sedimentary reservoirs based on the spatial distribution characteristics of sedimentary facies. This method can also be called a "two-step" model building: first, discrete variable modeling is used to simulate various geological bodies or sedimentary facies; then, based on the previous step of simulating different sedimentary facies, appropriate algorithms are used to construct a physical property model. The overall approach is based on sedimentary facies models, using mathematical statistics methods to quantitatively characterize the spatial distribution characteristics and heterogeneity of sedimentary bodies.

[0128] This invention employs a sequential Gaussian simulation method based on Bayesian theory. This method requires that the data conform to the basic conditions of the sequential Gaussian algorithm: the well logging data and various seismic data involved in the calculation must follow a normal distribution. If they do not, a normal transformation needs to be performed on the data, and a bivariate normality test is used for verification. Combining the principles of Bayesian three-dimensional simulation and phased-array data screening methods, the specific steps of the phased-array stochastic simulation method of this invention are as follows:

[0129] (1) Determine the scope of the three-dimensional grid space (work area), use phase data to mark the well logging data and various seismic data, and determine which phase each type of data of each grid point belongs to;

[0130] (2) The calculation diameter of the three-dimensional spherical model is determined by the step size of the variation function, and then the source direction and spatial value range are determined by phase separation based on the phase data, that is, the “phase control value range” is determined.

[0131] (3) Perform normal transformation on each relative well logging data and all seismic data involved in the calculation;

[0132] (4) Perform a binary normality test on the data after normal transformation. If it meets the test, proceed to the next step; otherwise, select other methods.

[0133] (5) Bayesian-sequential Gaussian simulations were performed using spatial sedimentary facies as a guide:

[0134] ① Define a random path such that each node is calculated only once;

[0135] ② For all points (sample points and simulated points) within the “phase control range”, the mean and variance of the cumulative conditional distribution function (ccdf) are calculated using the ordinary kriging method;

[0136] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0137] ④ The simulated values ​​are obtained by random sampling from the conditional distribution;

[0138] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited;

[0139] (6) Perform a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

[0140] Example 3

[0141] Figure 2 A schematic diagram of a sphere model considering the source direction is shown according to an embodiment of the present invention.

[0142] By introducing a variogram function, the value range of the original three-dimensional sphere model is transformed into a sphere model that considers the source direction, such as... Figure 2 As shown. The green arrow indicates the source direction, which is defined within a three-dimensional sphere by the azimuth and tolerance, while also considering the step size, horizontal and vertical angles, and their respective tolerances. Only values ​​falling within the range (red sample points within the blue rectangle) are included in the calculation; values ​​outside the range (black sample points) are excluded. This allows for effective sample selection, and the selection of sample points for calculation can be controlled by adjusting the step size, angle, and tolerance.

[0143] Figure 3 A schematic diagram of a deposition phase control according to an embodiment of the present invention is shown.

[0144] like Figure 3 As shown, yellow represents the fluvial facies of two river channels. The point to be estimated is located within the river channel. Following the aforementioned facies modeling approach, through screening, all sample points participating in the calculation of the point to be estimated are data from within the river channel (blue sample points), while data from outside the river channel (black sample points) are not included in the calculation. Similarly, if the point to be estimated is located outside the river channel, the sample points participating in the calculation are black sample points. Furthermore, if a river channel (fluvial facies) passes through... Figure 2 The blue cuboid shown divides the region into fluvial and non-fluvial facies. During calculations, due to the facies control effect, red sample points are used when calculating the estimated points within the fluvial facies; this is referred to in this invention as the facies control value range. Blue sample points are used when calculating the estimated points within the non-fluvial facies.

[0145] Guided by spatial sedimentary facies, Bayesian-sequential Gaussian simulations were performed, and the simulation results within the grid were subjected to inverse normal transformation to complete the simulation calculations.

[0146] Figure 4a , Figure 4b Schematic diagrams showing a planar comparison before and after deposition phase control according to an embodiment of the present invention are shown.

[0147] like Figure 4a , Figure 4bAs shown, the application in actual work areas demonstrates that the introduction of phase control results in a clear and continuous river channel on the profile, and the resolution is also improved. By comparing the prediction results of four wells with the logging data, the improved results are more consistent with the logging data, verifying the effectiveness of this method.

[0148] Example 4

[0149] Figure 5 A block diagram of a depositional phase constraint simulation apparatus based on three-dimensional dynamic data is shown according to an embodiment of the present invention.

[0150] like Figure 5 As shown, the sedimentary facies-constrained simulation device based on three-dimensional dynamic data includes:

[0151] Preprocessing module 201 determines the spatial range of the three-dimensional mesh and performs data preprocessing using phase data;

[0152] The calculation module 202 determines the calculation diameter of the three-dimensional spherical model and the range of phase control values ​​through the variogram function;

[0153] Simulation module 203 performs normal transformations on well logging data and seismic data relative to relative, and then performs Bayesian-sequential Gaussian simulations.

[0154] The inverse transformation module 204 performs a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

[0155] In one example, data preprocessing includes:

[0156] The well logging data and various seismic data are labeled to determine the phase of each grid point.

[0157] In one example, the variation function is:

[0158]

[0159] Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

[0160] In one example, the source direction and spatial value range are determined based on phase data, which is the phase control value range.

[0161] In one example, a Bayesian-sequential Gaussian simulation includes:

[0162] ① Define a random path such that each node is calculated only once;

[0163] ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method;

[0164] ③ Calculate the kriging parameters for well logging data and seismic data respectively;

[0165] ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution;

[0166] ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

[0167] In one example, it also includes:

[0168] Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

[0169] Example 5

[0170] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned depositional phase constraint simulation method based on three-dimensional dynamic data.

[0171] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0172] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0173] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0174] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0175] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0176] Example 6

[0177] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned depositional phase constraint simulation method based on three-dimensional dynamic data.

[0178] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0179] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0180] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0181] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A sedimentary facies-constrained simulation method based on three-dimensional dynamic data, characterized in that, include: Determine the spatial range of the three-dimensional mesh and perform data preprocessing using phase data; The computational diameter and phase control value range of the three-dimensional spherical model are determined by using the variogram function. Normal transformations were performed on each relative well logging data and seismic data, followed by Bayesian-sequential Gaussian simulations. The simulation results within the grid are subjected to an inverse normal transformation to complete the simulation calculation.

2. The sedimentary facies-constrained simulation method based on three-dimensional dynamic data according to claim 1, wherein, Data preprocessing includes: The well logging data and various seismic data are labeled to determine the phase of each grid point.

3. The sedimentary facies-constrained simulation method based on three-dimensional dynamic data according to claim 1, wherein, The variation function is: Where N(h) is the number of data pairs with a step size of h, and Z(x) is the number of data pairs with a step size of h. i ) and Z(x i +h) represents the two sampled values ​​with step size h.

4. The sedimentary facies-constrained simulation method based on three-dimensional dynamic data according to claim 1, wherein, The direction of the material source and the spatial range of values ​​are determined by phase data, which is the phase control value range.

5. The sedimentary facies-constrained simulation method based on three-dimensional dynamic data according to claim 1, wherein, Bayesian-sequential Gaussian simulations include: ① Define a random path such that each node is calculated only once; ②For all points within the phase control range, the mean and variance of the cumulative conditional distribution function are calculated using the ordinary kriging method; ③ Calculate the kriging parameters for well logging data and seismic data respectively; ④ Simulated values ​​are obtained by randomly sampling from the conditional distribution; ⑤ Add the simulated values ​​to the values ​​already simulated in step ②, and return to step ② until all grid points have been visited.

6. The sedimentary facies-constrained simulation method based on three-dimensional dynamic data according to claim 1, wherein, Also includes: Perform a binary normality test on the data after normal transformation; if it does not meet the requirements, do not perform Bayesian-sequential Gaussian simulation.

7. A sedimentary phase constraint simulation device based on three-dimensional dynamic data, characterized in that, include: The preprocessing module determines the spatial range of the three-dimensional mesh and performs data preprocessing using phase data. The calculation module determines the calculation diameter of the three-dimensional spherical model and the range of phase control values ​​through the variogram function; The simulation module performs normal transformations on well logging data and seismic data relative to each other, and then performs Bayesian-sequential Gaussian simulations. The inverse transformation module performs a normal inverse transformation on the simulation results within the grid to complete the simulation calculation.

8. The sedimentary phase constraint simulation device based on three-dimensional dynamic data according to claim 1, wherein, The direction of the material source and the spatial range of values ​​are determined by phase data, which is the phase control value range.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the depositional phase-constrained simulation method based on three-dimensional dynamic data as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the depositional phase constraint simulation method based on three-dimensional dynamic data as described in any one of claims 1-6.

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