Search

CN-122016602-A - Reservoir pore structure full-scale characterization method, device, equipment and medium based on accumulated fractal

CN122016602ACN 122016602 ACN122016602 ACN 122016602ACN-122016602-A

Abstract

The application discloses a reservoir pore structure full-scale characterization method, device, equipment and medium based on accumulated fractal, and relates to the technical field of computers. The method comprises the steps of obtaining multisource measurement data of a rock sample to be detected, correcting high-pressure mercury-pressing experimental data by utilizing pore geometry information of macropores in the rock sample to be detected to construct a capillary pressure-saturation curve, establishing a cumulative fractal equation, performing inversion calculation by utilizing a preset algorithm to obtain a fractal dimension spectrum, performing index-changing conversion on nuclear magnetic resonance transverse relaxation time T 2 spectrum data by utilizing the fractal dimension spectrum based on a nonlinear mapping relation to obtain a full-scale pore diameter distribution curve, and performing data fusion on three-dimensional pore topology network structural parameters, multidimensional fractal geometric features and the full-scale pore diameter distribution curve extracted on the basis of CT scanning image data to construct a comprehensive characterization model. Therefore, multisource information can be organically fused, and continuous heterogeneous full-scale precise characterization of the pore structure of the reservoir is realized.

Inventors

  • LV XIAOCONG
  • FENG HUI
  • LIU HUIQING
  • WANG JING

Assignees

  • 中国石油大学(北京)

Dates

Publication Date
20260512
Application Date
20260306

Claims (10)

  1. 1. A reservoir pore structure full-scale characterization method based on cumulative fractal, comprising: The method comprises the steps of obtaining multisource measurement data of a rock sample to be measured, wherein the multisource measurement data comprise target high-pressure mercury-pressing experimental data, nuclear magnetic resonance transverse relaxation time T 2 spectrum data and CT scanning three-dimensional image data; extracting pore geometry information of a target pore in the rock sample to be detected based on the CT scanning three-dimensional image data, wherein the target pore is a pore with a size meeting the preset macropore judgment condition, and the pore geometry information comprises pore radius distribution and topological structure parameters; Correcting the target large-aperture section data caused by the pore shielding effect in the target high-pressure mercury-pressing experimental data by utilizing the pore geometric information of the target pore so as to construct a capillary pressure-saturation curve by utilizing the corrected data; Establishing a cumulative fractal equation based on the capillary pressure-saturation curve, and carrying out inversion calculation on the cumulative fractal equation by utilizing a preset iteration truncated singular value decomposition algorithm to obtain a fractal dimension spectrum related to pore-throat radius of a rock sample to be detected; Establishing a nonlinear mapping relation between the nuclear magnetic resonance transverse relaxation time T 2 spectrum data and the pore radius of the rock sample to be detected by utilizing the fractal dimension spectrum, and performing variable index conversion on the nuclear magnetic resonance transverse relaxation time T 2 spectrum data based on the nonlinear mapping relation so as to obtain a full-scale pore diameter distribution curve; Extracting three-dimensional pore topology network structure parameters and multidimensional fractal geometrical characteristics of the rock sample to be detected based on the CT scanning three-dimensional image data, and carrying out trans-scale data fusion on the three-dimensional pore topology network structure parameters, the multidimensional fractal geometrical characteristics and the full-scale pore diameter distribution curve so as to construct a comprehensive characterization model for characterizing the rock full-scale pore structure, wherein the multidimensional fractal geometrical characteristics comprise three-dimensional fractal dimension sequences and two-dimensional fractal dimension sequences.
  2. 2. The method of full-scale characterization of a reservoir pore structure based on accumulated fractal of claim 1, wherein the accumulated fractal equation is used to describe fractal characteristics of pore structures in the capillary pressure-saturation curve in the form of weighted sums of a plurality of fractal components, wherein each fractal component is determined by a fractal dimension and a weight coefficient corresponding to the fractal dimension.
  3. 3. The method for full-scale characterization of a reservoir pore structure based on accumulated fractal as recited in claim 2, wherein the performing inversion calculation on the accumulated fractal equation by using a preset iterative truncated singular value decomposition algorithm to obtain a fractal dimension spectrum related to pore-throat radius of the rock sample to be measured comprises: Converting the accumulated fractal equation into a linear equation set, and constructing a sparse kernel matrix corresponding to the linear equation set, wherein a solution vector of the linear equation set is composed of weight coefficients corresponding to fractal components; Performing singular value decomposition on the sparse kernel matrix to obtain a corresponding singular value decomposition result, and determining a singular value cutoff parameter based on a preset information criterion formula so as to intercept the singular value decomposition result by using the singular value cutoff parameter to obtain an intercept result; And generating an initial solution vector based on the truncation result, and performing iterative optimization and negative constraint processing based on the initial solution vector to obtain the fractal dimension spectrum.
  4. 4. The method for full-scale characterization of reservoir pore structure based on cumulative fractal as recited in claim 1, wherein the establishing a nonlinear mapping relationship between the nuclear magnetic resonance transverse relaxation time T 2 spectrum data and the pore radius of the rock sample to be measured using the fractal dimension spectrum, and performing a variable exponential transformation on the nuclear magnetic resonance transverse relaxation time T 2 spectrum data based on the nonlinear mapping relationship, to obtain a full-scale pore size distribution curve, comprises: Establishing a variable index conversion model between the pore radius and the transverse relaxation time according to fractal dimension values corresponding to different pore intervals in the fractal dimension spectrum, wherein the conversion index of the variable index conversion model is related to the fractal dimension value of the corresponding pore interval; Taking the cumulative aperture distribution corresponding to the capillary pressure-saturation curve as a target reference, and determining a surface relaxation intensity reference coefficient by using a preset optimization algorithm based on the target reference; And converting the nuclear magnetic resonance transverse relaxation time T 2 spectrum data into the full-scale pore size distribution curve by using the variable exponential conversion model and the surface relaxation intensity reference coefficient.
  5. 5. The reservoir pore structure full-scale characterization method based on cumulative fractal as recited in claim 1, wherein the extracting three-dimensional pore topology network parameters and multi-dimensional fractal geometric features of the rock sample to be tested based on the CT scan three-dimensional image data comprises: And carrying out segmentation and connectivity analysis on the CT scanning three-dimensional image to obtain a three-dimensional connected pore space model, and extracting three-dimensional pore topology network structural parameters and multidimensional fractal geometric features of the rock sample to be detected based on the three-dimensional connected pore space model.
  6. 6. The reservoir pore structure full-scale characterization method based on cumulative fractal of claim 5, wherein the extracting three-dimensional pore topology network parameters and multi-dimensional fractal geometrical features of the rock sample to be measured based on the three-dimensional connected pore space model comprises: Identifying and constructing a pore network model by using a preset pore network model extraction algorithm based on the three-dimensional connected pore space model, and extracting three-dimensional pore topology network structure parameters of the rock sample to be detected based on the pore network model, wherein the pore network model comprises pore units and throat units connected with the pore units; Calculating the three-dimensional fractal dimension of a target pore system of the rock sample to be tested based on the three-dimensional connected pore space model; And calculating a two-dimensional fractal dimension layer by layer on continuous two-dimensional slices of the CT scanning three-dimensional image so as to generate the two-dimensional fractal dimension sequence.
  7. 7. The reservoir pore structure full-scale characterization method based on cumulative fractal as recited in claim 1, wherein the correcting the target large pore diameter section data of the target high pressure mercury experimental data caused by pore shielding effect by using pore geometry information of the target pore comprises: And based on a preset pore radius threshold, splicing the target high-pressure mercury injection experimental data with pore radius distribution data extracted based on the CT scanning three-dimensional image data to obtain corresponding corrected data.
  8. 8. Reservoir pore structure full-scale characterization device based on accumulated fractal, which is characterized by comprising: The system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring multi-source measurement data of a rock sample to be measured, and the multi-source measurement data comprise target mercury-pressing experimental data, nuclear magnetic resonance transverse relaxation time T 2 spectrum data and CT scanning three-dimensional image data; The information extraction module is used for extracting pore geometry information of a target pore in the rock sample to be detected based on the CT scanning three-dimensional image data, wherein the target pore is a pore with a size meeting the preset macropore judgment condition, and the pore geometry information comprises pore radius distribution and topological structure parameters; the data correction module is used for correcting the target large-aperture section data caused by the pore shielding effect in the target high-pressure mercury-pressing experimental data by utilizing the pore geometric information of the target pore so as to construct a capillary pressure-saturation curve by utilizing the corrected data; The fractal inversion module is used for establishing a cumulative fractal equation based on the capillary pressure-saturation curve, and carrying out inversion calculation on the cumulative fractal equation by utilizing a preset iteration truncated singular value decomposition algorithm so as to obtain a fractal dimension spectrum related to the pore-throat radius of the rock sample to be detected; The spectrogram conversion module is used for establishing a nonlinear mapping relation between the nuclear magnetic resonance transverse relaxation time T 2 spectral data and the pore radius of the rock sample to be detected by utilizing the fractal dimension spectrum, and carrying out variable index conversion on the nuclear magnetic resonance transverse relaxation time T 2 spectral data based on the nonlinear mapping relation so as to obtain a full-scale pore diameter distribution curve; The model construction module is used for extracting three-dimensional pore topological network structure parameters and multi-dimensional fractal geometric features of the rock sample to be detected based on the CT scanning three-dimensional image data, and carrying out trans-scale data fusion on the three-dimensional pore topological network structure parameters, the multi-dimensional fractal geometric features and the full-scale pore diameter distribution curve so as to construct a comprehensive characterization model for characterizing the full-scale pore structure of the rock, wherein the multi-dimensional fractal geometric features comprise three-dimensional fractal dimension sequences and two-dimensional fractal dimension sequences.
  9. 9. An electronic device, comprising: A memory for storing a computer program; a processor for executing the computer program to implement the cumulative fractal-based reservoir pore structure full-scale characterization method as recited in any one of claims 1-7.
  10. 10. A computer readable storage medium for storing a computer program, wherein the computer program when executed by a processor implements the cumulative fractal-based reservoir pore structure full-scale characterization method of any one of claims 1 to 7.

Description

Reservoir pore structure full-scale characterization method, device, equipment and medium based on accumulated fractal Technical Field The invention relates to the technical field of computers, in particular to a reservoir pore structure full-scale characterization method, device, equipment and medium based on accumulated fractal. Background With the continuous expansion of global oil and gas resource exploration and development to deep, deep and unconventional fields, reservoir evaluation faces unprecedented challenges. Unlike conventional hypertonic reservoirs, unconventional reservoir rock undergoes strong compaction and diagenetic effects, with pore systems ranging from nanoscale matrix pores to microslits, and features of strong heterogeneity and complex connectivity. The occurrence state and migration mechanism of the fluid in the fluid are obviously controlled by the microscopic pore throat structure, and complex non-Darcy seepage and slipping effects are shown. Therefore, conventional macroscopic physical parameters have been difficult to meet the accuracy requirements for reservoir analysis. At present, in the research field of rock micro-pore structure and physical property characterization, a plurality of technical schemes aiming at combining various experimental means to characterize the pore structure are presented, but the existing methods still have obvious limitations. 1. The data fusion mode is simple, and the deep fusion of the mechanism level cannot be realized, wherein most of the prior art only carries out simple linear splicing or piecewise fitting on data such as high-pressure mercury injection (MICP, mercury Injection Capillary Pressure), nuclear magnetic resonance (NMR, nuclear Magnetic Resonance) and the like. 2. The inherent defect of the key experiment is not effectively solved, the 'pore shielding effect' existing in the MICP experiment is easy to cause macropore characterization distortion, and the CT scanning is limited by resolution ratio and is difficult to capture the nanoscale micropore information. The prior art lacks an effective mechanism to correct these systematic errors introduced by the experimental principles themselves. 3. The description of the heterogeneity of the pore structure is too simplified, and most methods are based on single fractal or simple piecewise fractal assumptions, and cannot finely describe the continuous and heterogeneous change rule of the pore structure in the full scale range. 4. The characterization dimension is single, and the comprehensive structural model is lacking, namely the prior method focuses on obtaining the pore size distribution curve, and the three-dimensional topological connection relation between the pore size information and the pore space, the surface geometry and the like cannot be organically integrated, so that the comprehensive structural model capable of comprehensively reflecting the seepage physical essence of the reservoir cannot be constructed. From the above, how to organically fuse multi-source information and realize continuous heterogeneous full-scale accurate characterization of a reservoir pore structure is a problem to be solved urgently. Disclosure of Invention In view of the above, the invention aims to provide a reservoir pore structure full-scale characterization method, device, equipment and medium based on accumulated fractal, which can organically fuse multi-source information and realize continuous heterogeneous full-scale accurate characterization of the reservoir pore structure. The specific scheme is as follows: in a first aspect, the application provides a reservoir pore structure full-scale characterization method based on cumulative fractal, which comprises the following steps: The method comprises the steps of obtaining multisource measurement data of a rock sample to be measured, wherein the multisource measurement data comprise target high-pressure mercury-pressing experimental data, nuclear magnetic resonance transverse relaxation time T 2 spectrum data and CT scanning three-dimensional image data; extracting pore geometry information of a target pore in the rock sample to be detected based on the CT scanning three-dimensional image data, wherein the target pore is a pore with a size meeting the preset macropore judgment condition, and the pore geometry information comprises pore radius distribution and topological structure parameters; Correcting the target large-aperture section data caused by the pore shielding effect in the target high-pressure mercury-pressing experimental data by utilizing the pore geometric information of the target pore so as to construct a capillary pressure-saturation curve by utilizing the corrected data; Establishing a cumulative fractal equation based on the capillary pressure-saturation curve, and carrying out inversion calculation on the cumulative fractal equation by utilizing a preset iteration truncated singular value decomposition algorithm to obtain a