A method for evaluating fine-grained rock reservoirs based on particle geometry
Patent Information
- Application Number
- CN202611535302.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-30
- Publication Date
- 2026-11-03
AI Technical Summary
Existing technologies lack systematic analysis of mineral particle geometry and the establishment of its correlation with pore type and coordination number, resulting in insufficient accuracy in shale reservoir evaluation. Furthermore, the control effect of pore connectivity on reservoir properties is unclear, mathematical fitting lacks constraints from geophysical laws, and the model's extrapolation and generalization capabilities are insufficient.
The proportion of mineral particles was identified by X-ray diffraction and scanning electron microscopy analysis. A corresponding classification relationship between the geometric morphology of mineral particles and the type of inorganic pores was established. A pore network model was constructed by combining CT scans, the pore coordination number was extracted, and a functional mapping relationship between the coordination number and the number of pores was established under geophysical constraints.
It enables cross-scale quantitative evaluation from microscopic particle geometry characteristics to macroscopic reservoir connectivity, improving the relevance and accuracy of reservoir evaluation, overcoming the fragmented analysis defects of traditional methods, and possessing good extrapolation and generalization capabilities.
Smart Images
Figure CN123029068A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of rock formation evaluation, and in particular relates to a method for evaluating fine-grained rock reservoirs based on particle geometry. Background Technology
[0002] Shale reservoirs are characterized by rich organic matter, strong heterogeneity, extremely low porosity and permeability, and nanoscale pore throats, making their reservoir evaluation methods distinct from conventional reservoirs. Currently, shale reservoir evaluation mainly covers mineral composition, organic matter, pore structure, and fracturing capability, with the integration of multi-scale and multi-type technologies becoming an important development direction. Existing technologies utilize BIB-SEM combined with energy-dispersive X-ray spectroscopy and capillary modeling to predict the permeability of organic-rich shale, and can also combine modeling techniques to calculate the fluid transport properties of heterogeneous shale reservoirs; micron-CT, FIB-SEM, and HIM technologies are used to characterize the multi-scale pore structure of shale, and gas transport simulation methods are established, including microscopic pore structure models, various pore spatial distribution models, and core-scale models; triaxial compression deformation experiments are combined with X-CT to characterize the dynamic process of in-situ rock fracturing. In recent years, with the continuous advancement of shale gas exploration and development in China, the specificity, accuracy, and applicability of existing characterization and evaluation methods can no longer meet the needs of industrial production.
[0003] Existing technologies still have the following shortcomings: they lack systematic analysis of the geometric morphology of mineral particles, such as platy, granular, and columnar shapes, and have not established a corresponding classification relationship between particle geometry and confined pore type; they have failed to establish a quantitative function mapping between coordination number and pore number under different pore types, and the controlling effect of pore connectivity on reservoir properties is unclear; when establishing mathematical relationships between pore parameters, they lack constraints from geophysical laws, and mostly use pure mathematical fitting, which makes the prediction results prone to deviating from the actual geological conditions, resulting in insufficient extrapolation and generalization ability of the model. Summary of the Invention
[0004] In view of the above-mentioned shortcomings in the prior art, the present invention provides a fine-grained rock reservoir evaluation method based on particle geometry, which solves the problem that the existing technology lacks systematic analysis of mineral particle geometry and the establishment of its correlation with pore type and coordination number, resulting in insufficient accuracy of reservoir evaluation.
[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for evaluating fine-grained rock reservoirs based on particle geometry, comprising the following steps: Fine-grained rock mineral samples were obtained, and the proportion of mineral grains in the samples was identified by X-ray diffraction experiments and scanning electron microscopy image analysis to obtain mineral composition identification results. Based on the mineral composition identification results, the geometric morphological features of mineral particles are extracted, the mineral particle type is identified based on the geometric morphological features, and the inorganic pore type of fine-grained rock mineral samples is determined by the pore space enclosed by the mineral particles. Based on CT scan data of fine-grained rock mineral samples, spatial distribution information of pores and pore throats was extracted, a pore network model was established, and the coordination number of pores in the fine-grained rock mineral samples was extracted based on the pore network model. Based on the inorganic pore type and pore coordination number of fine-grained rock mineral samples, and combined with geophysical constraints, a functional mapping relationship between coordination number and pore number under different inorganic pore types is established. Based on the functional mapping relationship between coordination number and pore number under different inorganic pore types, and the coordination number of the fine-grained rock sample to be evaluated, the evaluation results of the fine-grained rock reservoir to be evaluated are obtained.
[0006] Furthermore: the obtained mineral composition identification results specifically include: Fine-grained rock mineral samples were obtained, and several high-resolution electron microscope images of the fine-grained rock mineral samples were obtained by scanning electron microscopy. These high-resolution electron microscope images were then stitched together to form a large field-of-view electron microscope image. Based on the large field-of-view electron microscope image, a numerical grayscale electron microscope image is obtained by performing grayscale recognition and threshold setting; Based on the standard grayscale values of different minerals, the mineral particle type is identified in the numerical grayscale electron microscopy image, the type and proportion of mineral particles are identified, and the mineral composition identification result is obtained after filtering and denoising.
[0007] The further beneficial effects mentioned above are as follows: This invention acquires several high-precision electron microscope images by scanning electron microscope and stitches them into a large field of view image, which can simultaneously acquire the morphology and distribution information of mineral particles in a large field of view, avoiding the limitations of single field of view analysis; combined with grayscale recognition, threshold setting and standard grayscale value discrimination, accurate identification of mineral particles can be achieved.
[0008] Further: Determining the inorganic pore type of the fine-grained rock mineral sample specifically includes: Based on the mineral composition identification results, using pixel values, image X coordinates, and image Y coordinates as a basis, the shape and parameters of mineral particles are identified, and the geometric morphological features of mineral particles are obtained. Based on the geometric morphological characteristics of mineral particles, the pore space enclosed by mineral particles is selected through supplementary screening. Based on the geometric morphology of mineral particles in the pore space, the inorganic pores in the pore space are divided into slit-wedge-shaped brittle mineral intergranular pores, clay-brittle mineral intergranular pores, and wedge-polygonal mineral intergranular pores. The corresponding classification relationship between geometric morphology and inorganic pore type is established to obtain the inorganic pore type of fine-grained rock mineral samples.
[0009] The further beneficial effects mentioned above are as follows: This invention accurately locates the spatial position and geometric parameters of mineral particles through pixel values and image coordinates, accurately obtaining the geometric morphological characteristics of mineral particles such as platy, granular, and columnar shapes; through complementation screening, it precisely separates the pore space enclosed by mineral particles, and classifies inorganic pores into three types according to the geometric morphology of the enclosed particles. By establishing a corresponding classification relationship between the geometric morphology of mineral particles and the type of inorganic pores, it overcomes the shortcomings of traditional methods that analyze particles and pores separately, providing a classification basis for subsequently establishing a quantitative function mapping between coordination number and pore number based on different pore types, thereby improving the pertinence and accuracy of reservoir evaluation.
[0010] Further: obtaining the coordination number of pores in the fine-grained rock mineral sample specifically includes: CT scans were performed on fine-grained rock mineral samples to obtain three-dimensional CT scan data. Based on 3D CT scan data, the pores and pore throats of fine-grained rock mineral samples were segmented and identified, and a pore network model was constructed based on the pores and pore throats. Based on the pore network model, the coordination number of each pore in the pore network model is extracted to obtain the coordination number of pores in the fine-grained rock mineral sample.
[0011] The further beneficial effects mentioned above are as follows: This invention obtains three-dimensional data by performing CT scanning on fine-grained rock mineral samples and segments and identifies pores and pore throats, which can extend the traditional two-dimensional pore structure analysis to three-dimensional space, truly reflect the three-dimensional distribution and connectivity of pores, overcome the shortcomings of traditional methods in accurately depicting the topological structure of pore space, and improve the scientificity and accuracy of reservoir connectivity evaluation.
[0012] Furthermore: the establishment of the functional mapping relationship between coordination number and pore number under different inorganic pore types specifically includes: Based on the inorganic pore types of the fine-grained rock mineral samples and the proportion of each inorganic pore type in the fine-grained rock mineral samples, calculate the number of pores corresponding to each inorganic pore type. Based on the number of pores and the coordination number of pores corresponding to each inorganic pore type, theoretical prediction equations for each inorganic pore type are established. Based on the theoretical prediction equations for each inorganic porosity type, a nonlinear optimization objective function is constructed by applying geophysical constraint boundaries and based on the least squares principle. Within the geophysical constraints, the nonlinear optimization objective function is adaptively iteratively solved to determine the optimal solution for each undetermined parameter in the theoretical prediction equation; By substituting the optimal solution of the undetermined parameters into the theoretical prediction equation, the functional mapping relationship between coordination number and pore number under different inorganic pore types is obtained.
[0013] Furthermore, the theoretical prediction equations for each inorganic pore type are expressed as follows:
[0014] in, For the first Inorganic-like pores with a coordination number of Pore number at time For the first The theoretical maximum number of interconnected pores in inorganic-like pores. It is a natural exponential function. Coordination number, For the first The optimal average coordination number of inorganic-like pores This is a sensitivity parameter for the porosity as a function of the coordination number. For inorganic pore type index, Corresponding to slit-wedge-shaped intermineral pores in brittle minerals, Corresponding to the interpores between clay and brittle minerals, Corresponding to wedge-shaped to polygonal mineral intercrystalline pores; The expression for the geophysical constraint boundary is as follows:
[0015]
[0016]
[0017] in, For the first Inorganic pores in the first The actual number of observed porosity in each fine-grained rock mineral sample The first of all observed data The maximum number of pores in inorganic-like pores; The expression for the nonlinear optimization objective function is as follows:
[0018] in, For nonlinear optimization objective function, In order to satisfy , Under the condition of the first Theoretical pore number prediction for inorganic-like pores. For the first The overall average coordination number of each fine-grained rock mineral sample For the parameters to be determined, .
[0019] Furthermore, the optimal solution for each undetermined parameter in the theoretical prediction equation is expressed as follows:
[0020] in, This represents the optimal solution for each undetermined parameter. The physical boundary matrix; The expressions for the functional mapping relationship between coordination number and pore number under different inorganic pore types are as follows:
[0021]
[0022] in, For the first The optimal solution for the theoretical maximum number of connected pores in inorganic-like pores. For the first The optimal solution for the optimal average coordination number of inorganic-like pores. The optimal solution for the sensitivity parameter of pore number as a function of coordination number. To find the optimal solution from each undetermined parameter Extract The value of the component, To find the optimal solution from each undetermined parameter Extract The value of the component, To find the optimal solution from each undetermined parameter Extract The value of the component.
[0023] The further beneficial effects mentioned above are as follows: By constructing theoretical prediction equations for different inorganic pore types and establishing a nonlinear optimization objective function based on the least squares principle for adaptive iterative solution, this invention can accurately determine key parameters such as the optimal coordination number and maximum pore number for each type of pore. In the optimization process, geophysical constraint boundaries are introduced to ensure that the fitting results always follow the actual rock physics laws. The final established function mapping relationship has good generalization ability and can provide a scientific basis for the connectivity evaluation and reservoir quality prediction of shale reservoirs under different geological conditions.
[0024] The beneficial effects of this invention are: This invention provides a method for evaluating fine-grained rock reservoirs based on particle geometry. By constructing a technical process involving mineral particle geometry identification, inorganic pore type classification, pore network coordination number extraction, and coordination number and pore number function mapping modeling under geological constraints, it achieves cross-scale quantitative evaluation from microscopic particle geometry characteristics to macroscopic reservoir connectivity and reservoir performance. This invention incorporates the platy, granular, and columnar geometric morphology characteristics of mineral particles as independent evaluation parameters into the reservoir evaluation system, establishing a corresponding classification relationship between particle geometry and three types of inorganic pores, thus overcoming the shortcomings of traditional methods that analyze particles and pores separately. Through CT 3D reconstruction and pore network modeling to extract coordination numbers, pore connectivity is elevated from a qualitative description to a quantitative characterization, and a function mapping relationship between coordination number and pore number is established under different pore types. Simultaneously, geophysical constraint boundaries are introduced during parameter optimization, ensuring that the fitting results always conform to actual rock physics laws, overcoming the shortcomings of pure mathematical fitting which easily deviates from geological reality. The model possesses good extrapolation and generalization capabilities. This invention provides a scientific and reliable quantitative basis for evaluating the connectivity and predicting the quality of shale reservoirs and reservoirs with complex pore structures such as tight sandstone and carbonate rocks. Attached Figure Description
[0025] Figure 1 This is a schematic diagram of a method for evaluating fine-grained rock reservoirs based on particle geometry. Figure 2 The results of mineral composition identification for different geometric shapes; Figure 3 The identification results for pore types with different geometric shapes; Figure 4 Images from 3D CT scan data. Detailed Implementation
[0026] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.
[0027] Example 1, as Figure 1 The diagram shows a flowchart of a fine-grained rock reservoir evaluation method based on particle geometry, including the following steps: Fine-grained rock mineral samples were obtained, and the proportion of mineral grains in the samples was identified by X-ray diffraction experiments and scanning electron microscopy image analysis to obtain mineral composition identification results. Based on the mineral composition identification results, the geometric morphological features of mineral particles are extracted, the mineral particle type is identified based on the geometric morphological features, and the inorganic pore type of fine-grained rock mineral samples is determined by the pore space enclosed by the mineral particles. Based on CT scan data of fine-grained rock mineral samples, spatial distribution information of pores and pore throats was extracted, a pore network model was established, and the coordination number of pores in the fine-grained rock mineral samples was extracted based on the pore network model. Based on the inorganic pore type and pore coordination number of fine-grained rock mineral samples, and combined with geophysical constraints, a functional mapping relationship between coordination number and pore number under different inorganic pore types is established. Based on the functional mapping relationship between coordination number and pore number under different inorganic pore types, and the coordination number of the fine-grained rock sample to be evaluated, the evaluation results of the fine-grained rock reservoir to be evaluated are obtained.
[0028] Existing shale reservoir evaluation methods mostly focus on single or local indicators such as mineral composition, organic matter abundance, and pore structure, lacking a systematic consideration of the geometric morphology of mineral particles, such as platy, granular, and columnar shapes. They fail to incorporate particle geometry as an evaluation parameter into the reservoir evaluation system, and have not established a corresponding classification relationship between particle geometry and confined pore type. This leads to the need for redeployment when evaluating different fine-grained rock reservoirs, resulting in low evaluation efficiency. At the same time, existing methods mostly remain at the level of qualitative description when characterizing pore connectivity, failing to establish a quantitative function mapping between coordination number and pore number under different pore types. Furthermore, the fitting process lacks constraints from geophysical laws, causing the prediction results to deviate from the actual geological conditions, making it difficult to meet the engineering requirements for accurate evaluation of complex shale reservoirs. To address this, this invention proposes a method for evaluating fine-grained rock reservoirs based on particle geometry. It uses the geometric morphology of mineral particles (plate-like, granular, columnar, etc.) as independent evaluation parameters, establishing a correspondence between geometric morphology and three types of inorganic pores: slotted-wedge-shaped brittle mineral intergranular pores, clay-brittle mineral intergranular pores, and wedge-shaped-polygonal mineral intercrystalline pores. By extracting the coordination number of each pore through CT 3D reconstruction and a pore network model, quantitative characterization of pore connectivity is achieved. Theoretical prediction equations for coordination number and pore number under different pore types are constructed, and geophysical constraint boundaries are introduced in parameter optimization. A functional mapping relationship between coordination number and pore number is established through nonlinear adaptive iterative solution, thereby achieving cross-scale quantitative evaluation of shale reservoir connectivity and reservoir performance.
[0029] In a specific embodiment of the present invention, a fine-grained rock mineral sample is obtained, and the proportion of mineral grains in the fine-grained rock mineral sample is identified by X-ray diffraction experiments and scanning electron microscopy image analysis to obtain the mineral composition identification result, specifically including: Fine-grained rock mineral samples were acquired, and several high-resolution electron microscope (SEM) images of the samples were obtained. These high-resolution images were then stitched together to form a large-field-of-view (FROM) image. Specifically, the fine-grained rock mineral samples were placed under an SEM, and 81 high-resolution images were continuously captured. Each image's field of view covered different areas of the sample, with overlapping areas between adjacent images. Using CorelDRAW (CDR) software, these 81 high-resolution images were stitched together according to their spatial positions to form a 9×9 FROM image, thus achieving simultaneous acquisition of mineral grain morphology and distribution information over a large field of view. This large-field-of-view stitching method allows for the simultaneous acquisition of mineral grain morphology and distribution information at a large scale, avoiding the inaccuracies in evaluation caused by the limitations of single-field-of-view analysis.
[0030] Based on large-field-of-view electron microscope (EM) images, numerical grayscale EEM images are obtained through grayscale recognition and threshold setting. Specifically, the large-field-of-view EEM images can be imported into ImageJ software, where grayscale recognition processing is performed, and a grayscale threshold is set to quantify the image. The quantified grayscale values are between 0 and 255. The contrast and brightness of the image are adjusted to highlight the difference between mineral grains and the background, thus obtaining a numerical grayscale EEM image. The numerical processing of large-field-of-view EEM images through grayscale recognition and threshold setting facilitates subsequent identification of mineral grain types based on grayscale values.
[0031] Based on the standard grayscale values of different minerals, mineral grain types are identified in numerical grayscale electron microscopy (NUMBER) images. The types and proportions of mineral grains are determined, and the mineral composition is then filtered and denoised to obtain the final mineral composition identification results. Specifically, combining conventional microscopic mineral grayscale features with pre-calibrated stable standard grayscale values corresponding to different minerals, mineral types are rapidly identified in each pixel region of the NUMBER image. Major mineral types such as quartz, feldspar, and clay minerals are distinguished, and the proportions of each type of mineral grain are statistically analyzed. A filtering algorithm is then used to denoise the image, eliminating noise interference introduced during image acquisition or processing, ultimately yielding the mineral composition identification results. Figure 2 As shown, this invention identifies mineral components with different geometric shapes. Since different minerals have relatively stable grayscale characteristics in electron microscope images, it uses the standard grayscale values of different minerals to determine the mineral particle type, thereby achieving rapid quantitative identification of mineral components and overcoming the problems of strong subjectivity and low efficiency in traditional manual microscopic identification.
[0032] In a specific embodiment of the present invention, determining the inorganic pore type of a fine-grained rock mineral sample specifically includes: Based on the mineral composition identification results, and using pixel values, image X-coordinates, and image Y-coordinates as a basis, the shape and parameters of mineral particles are identified, yielding their geometric morphological characteristics. Building upon the mineral particle type identification, this invention uses the pixel values of each pixel in the numerical grayscale electron microscope image, along with the image X and Y coordinates, as data to determine the spatial location and boundary range of each mineral particle in the image, and calculates its diameter, grain size, roundness, and other geometric parameters. Based on these geometric parameters, the geometric morphology of the mineral particles is determined to be platy, granular, or columnar, etc. This invention accurately locates the spatial position and geometric parameters of mineral particles through pixel values and image coordinates, enabling precise acquisition of the geometric morphological characteristics of each mineral particle, providing a basis for subsequent pore type classification.
[0033] Based on the geometric morphological characteristics of mineral particles, the pore space enclosed by mineral particles is selected through supplementary screening. Based on the geometric morphology of mineral particles within the pore space, the inorganic pores are classified into slit-wedge-shaped brittle mineral intergranular pores, clay-brittle mineral intergranular pores, and wedge-polygonal mineral intergranular pores. A correspondence between geometric morphology and inorganic pore type is established to obtain the inorganic pore type of fine-grained rock mineral samples. Specifically, for pore spaces enclosed by platy mineral particles, due to the directional or semi-directional arrangement of the platy minerals, narrow slit-shaped or wedge-shaped gaps are often formed between the particles, thus classifying them as slit-wedge-shaped brittle mineral intergranular pores. For pore spaces jointly enclosed by granular mineral particles and platy clay mineral particles, they are classified as clay-brittle mineral intergranular pores. For polygonal or wedge-shaped pore spaces enclosed by granular or columnar mineral particles through point or surface contact, they are classified as wedge-polygonal mineral intergranular pores. Figure 3 The image shows the identification results of pore types with different geometric shapes.
[0034] The three inorganic pore types of this invention correspond to different combinations of confined particle geometry, thereby establishing a correspondence between mineral particle geometry and inorganic pore types. This overcomes the problem of traditional methods analyzing particles and pores separately, and improves the pertinence and accuracy of reservoir evaluation.
[0035] In a specific embodiment of the present invention, obtaining the coordination number of pores in a fine-grained rock mineral sample specifically includes: CT scans were performed on fine-grained rock mineral samples to obtain three-dimensional CT scan data, such as... Figure 4 The image shown is an image of a three-dimensional CT scan. Based on 3D CT scan data, the pores and pore throats of fine-grained rock mineral samples are segmented and identified, and a pore network model is constructed based on the pores and pore throats. The AVIZO 3D reconstruction software platform can be used to divide the pore space into pores (expanded parts) and pore throats (narrow connecting parts). The identified pore bodies are used as nodes and pore throats are used as connecting edges to construct a pore network model that reflects the topological structure of the pore space. The pore network model fully includes the spatial location, volume and interconnection information of each pore. Based on a pore network model, the coordination number of each pore in the pore network model is extracted to obtain the coordination number of pores in the fine-grained rock mineral sample. In the pore network model, for each pore node, the number of adjacent pores directly connected to it through the pore throat is counted; this number is the coordination number of that pore. Summarizing the coordination numbers of all pores yields the coordination number dataset of pores in the fine-grained rock mineral sample. The coordination number is used to characterize the degree of spatial connectivity of pores. This invention, by constructing a pore network model and extracting the coordination number of each pore, achieves a quantitative characterization of pore connectivity, providing input for subsequently establishing a functional mapping relationship between coordination number and pore number.
[0036] In a specific embodiment of the present invention, based on the inorganic pore type and coordination number of the fine-grained rock mineral sample, and combined with geophysical constraints, a functional mapping relationship between coordination number and pore number under different inorganic pore types is established, specifically including: Based on the inorganic pore types of fine-grained rock mineral samples and the proportion of each inorganic pore type in the samples, the number of pores corresponding to each inorganic pore type is calculated. Under different mineral grain compositions, minerals with different geometric shapes correspond to different inorganic pore types, possessing corresponding coordination numbers and porosities. In one embodiment of this invention, the dataset is defined as follows: Let j be the overall average coordination number of the j-th fine-grained rock mineral sample. For the first Total number of connected pores in a fine-grained rock mineral sample For the first The proportion of inorganic pores in the sample, then the first Inorganic pores in the first The number of pores in each fine-grained rock mineral sample is: Furthermore, the proportions of the three types of inorganic pores satisfy the normalization condition. .
[0037] Based on the pore number and coordination number corresponding to each inorganic pore type, theoretical prediction equations for each inorganic pore type are established. In this embodiment, it is assumed that the pore number of each type of inorganic pore follows a Gaussian normal distribution with respect to the coordination number, and the first... The theoretical prediction equation for inorganic-like pores is expressed as follows:
[0038] in, For the first Inorganic-like pores with a coordination number of Pore number at time For the first The theoretical maximum number of interconnected pores in inorganic-like pores. It is a natural exponential function. Coordination number, For the first The optimal average coordination number of inorganic-like pores This is a sensitivity parameter for the porosity as a function of the coordination number. For inorganic pore type index, Corresponding to slit-wedge-shaped intermineral pores in brittle minerals, Corresponding to the interpores between clay and brittle minerals, Corresponding to wedge-shaped to polygonal mineral intercrystalline pores.
[0039] Based on the theoretical prediction equations for various inorganic porosity types, a nonlinear optimization objective function is constructed by applying geophysical constraint boundaries and based on the least squares principle. This invention applies physical constraint boundaries to the latent variable parameters to be inverted, based on actual rock physics laws. The expressions for the geophysical constraint boundaries are as follows:
[0040]
[0041]
[0042] in, For the first Inorganic pores in the first The actual number of observed porosity in each fine-grained rock mineral sample The first of all observed data The maximum number of pores in inorganic-like pores; by applying geophysical constraint boundaries, it is ensured that the theoretical maximum number of pores is not lower than the actual observed value and does not exceed the geological upper limit of 5000, the optimal coordination number conforms to the low porosity and permeability characteristics of shale, and the sensitivity parameter reflects the actual dispersion of pore distribution, so that the fitting results always follow the actual rock physical laws. Based on the least squares principle, the expression for the nonlinear optimization objective function is constructed as follows:
[0043] in, For nonlinear optimization objective function, In order to satisfy , Under the condition of the first Theoretical pore number prediction for inorganic-like pores. For the first The overall average coordination number of each fine-grained rock mineral sample For the parameters to be determined, .
[0044] Within the geophysical constraints, an adaptive iterative solution is performed on the nonlinear optimization objective function to determine the optimal solution for each undetermined parameter in the theoretical prediction equation. A quasi-Newton optimization algorithm with boundary constraints (such as the L-BFGS-B algorithm) can be used for adaptive iterative solution, finding the global minimum of the objective function in the multidimensional parameter space. The gradient of the objective function with respect to the parameter set θ is calculated iteratively, and the search direction is updated in conjunction with the geophysical constraints until the convergence condition is met, at which point the optimal parameter solution θ is extracted. :
[0045] in, This represents the optimal solution for each undetermined parameter. Given the physical boundary matrix, extract each component from the optimal parameter solution to obtain... , σ .
[0046] Substituting the optimal solution of the undetermined parameters into the theoretical prediction equation, the functional mapping relationship between coordination number and pore number under different inorganic pore types is obtained. The expression for the functional mapping relationship between coordination number and pore number under different inorganic pore types is as follows:
[0047]
[0048] in, For the first The optimal solution for the theoretical maximum number of connected pores in inorganic-like pores. For the first The optimal solution for the optimal average coordination number of inorganic-like pores. The optimal solution for the sensitivity parameter of pore number as a function of coordination number. To find the optimal solution from each undetermined parameter Extract The value of the component, To find the optimal solution from each undetermined parameter Extract The value of the component, To find the optimal solution from each undetermined parameter Extract The values of the components. This invention introduces geophysical constraint boundaries during the optimization process, ensuring that the fitting results always follow the actual rock physics laws. The established function mapping relationship has good extrapolation and generalization capabilities, providing a reliable mathematical and physical basis for the quantitative evaluation of shale reservoir connectivity.
[0049] The beneficial effects of this invention are as follows: This invention provides a method for evaluating fine-grained rock reservoirs based on particle geometry. By constructing a technical process involving mineral particle geometry identification, inorganic pore type classification, pore network coordination number extraction, coordination number under geological constraints, and pore number function mapping modeling, it achieves cross-scale quantitative evaluation from microscopic particle geometry characteristics to macroscopic reservoir connectivity and reservoir performance. This invention incorporates the platy, granular, and columnar geometric morphological characteristics of mineral particles as independent evaluation parameters into the reservoir evaluation system, establishing a corresponding classification relationship between particle geometry and three types of inorganic pores, thus overcoming the shortcomings of traditional methods that analyze particles and pores separately. Through CT three-dimensional reconstruction and pore network model extraction of coordination numbers, pore connectivity is elevated from a qualitative description to a quantitative characterization, and a function mapping relationship between coordination number and pore number is established under different pore types. Simultaneously, geophysical constraint boundaries are introduced during parameter optimization, ensuring that the fitting results always conform to actual rock physics laws, overcoming the shortcomings of pure mathematical fitting which easily deviates from geological reality. The model possesses good extrapolation and generalization capabilities. This invention provides a scientific and reliable quantitative basis for evaluating the connectivity and predicting the quality of shale reservoirs and reservoirs with complex pore structures such as tight sandstone and carbonate rocks.
Claims
1. A method for evaluating fine-grained rock reservoirs based on the geometric morphology of the particles, characterized by, Includes the following steps: Fine-grained rock mineral samples were obtained, and the proportion of mineral grains in the samples was identified by X-ray diffraction experiments and scanning electron microscopy image analysis to obtain mineral composition identification results. Based on the mineral composition identification results, the geometric morphological features of mineral particles are extracted, the mineral particle type is identified based on the geometric morphological features, and the inorganic pore type of fine-grained rock mineral samples is determined by the pore space enclosed by the mineral particles. Based on CT scan data of fine-grained rock mineral samples, spatial distribution information of pores and pore throats was extracted, a pore network model was established, and the coordination number of pores in the fine-grained rock mineral samples was extracted based on the pore network model. Based on the inorganic pore type and pore coordination number of fine-grained rock mineral samples, and combined with geophysical constraints, a functional mapping relationship between coordination number and pore number under different inorganic pore types is established. Based on the functional mapping relationship between coordination number and pore number under different inorganic pore types, and the coordination number of the fine-grained rock sample to be evaluated, the evaluation results of the fine-grained rock reservoir to be evaluated are obtained.
2. The method for evaluating fine-grained rock reservoirs based on the geometry of the grains according to claim 1, characterized in that, The obtained mineral composition identification results specifically include: Fine-grained rock mineral samples were obtained, and several high-resolution electron microscope images of the fine-grained rock mineral samples were obtained by scanning electron microscopy. These high-resolution electron microscope images were then stitched together to form a large field-of-view electron microscope image. Based on the large field-of-view electron microscope image, a numerical grayscale electron microscope image is obtained by performing grayscale recognition and threshold setting; Based on the standard gray values of different minerals, the mineral particle type is identified in the numerical grayscale electron microscopy image, the type and proportion of mineral particles are identified, and the mineral composition identification result is obtained after filtering and denoising.
3. The method for evaluating fine-grained rock reservoirs based on the geometry of the grains according to claim 1, characterized in that, The determination of the inorganic pore type of fine-grained rock mineral samples specifically includes: Based on the mineral composition identification results, using pixel values, image X coordinates, and image Y coordinates as a basis, the shape and parameters of mineral particles are identified, and the geometric morphological characteristics of mineral particles are obtained. Based on the geometric morphological characteristics of mineral particles, the pore space enclosed by mineral particles is selected through supplementary screening. Based on the geometric morphology of mineral particles in the pore space, the inorganic pores in the pore space are divided into slit-wedge-shaped brittle mineral intergranular pores, clay-brittle mineral intergranular pores, and wedge-polygonal mineral intergranular pores. The corresponding classification relationship between geometric morphology and inorganic pore type is established to obtain the inorganic pore type of fine-grained rock mineral samples.
4. The method for evaluating fine-grained rock reservoirs based on the geometry of the grains according to claim 1, characterized in that, The coordination number of the pores in the fine-grained rock mineral sample is obtained, specifically including: CT scans were performed on fine-grained rock mineral samples to obtain three-dimensional CT scan data. Based on 3D CT scan data, the pores and pore throats of fine-grained rock mineral samples were segmented and identified, and a pore network model was constructed based on the pores and pore throats. Based on the pore network model, the coordination number of each pore in the pore network model is extracted to obtain the coordination number of pores in the fine-grained rock mineral sample.
5. The method for evaluating fine-grained rock reservoirs based on the geometry of the grains according to claim 1, characterized in that, The establishment of the functional mapping relationship between coordination number and pore number under different inorganic pore types specifically includes: Based on the inorganic pore types of the fine-grained rock mineral samples and the proportion of each inorganic pore type in the fine-grained rock mineral samples, calculate the number of pores corresponding to each inorganic pore type. Based on the number of pores and the coordination number of pores corresponding to each inorganic pore type, theoretical prediction equations for each inorganic pore type are established. Based on the theoretical prediction equations for each inorganic porosity type, a nonlinear optimization objective function is constructed by applying geophysical constraint boundaries and based on the least squares principle. Within the geophysical constraints, the nonlinear optimization objective function is adaptively iteratively solved to determine the optimal solution for each undetermined parameter in the theoretical prediction equation; By substituting the optimal solution of the undetermined parameters into the theoretical prediction equation, the functional mapping relationship between coordination number and pore number under different inorganic pore types is obtained.
6. The method for fine-grained rock reservoir evaluation based on particle geometry according to claim 5, characterized in that, The theoretical prediction equations for each inorganic porosity type are expressed as follows: wherein, is the number of pores of the first class of inorganic pores, is the number of pores of the first class of inorganic pores, is the theoretical maximum number of connected pores of the first class of inorganic pores, is a natural exponential function, is the coordination number, is the optimal average coordination number of the first class of inorganic pores, is a sensitivity parameter of the number of pores as a function of the coordination number, corresponds to slit-shaped-wedge-shaped inter-mineral pores of brittle minerals, corresponds to clay-brittle mineral inter-mineral pores, corresponds to wedge-shaped-polygonal inter-mineral pores of minerals. The expression for the geophysical constraint boundary is as follows: wherein, is the class of inorganic pores in the actual observed pore count of the first class of inorganic pores in the sample of fine-grained rock mineral, is the maximum pore count of the first class of inorganic pores in all observed data; The expression for the nonlinear optimization objective function is as follows: wherein, is a nonlinear optimization objective function, is a constraint function, , is a first theoretical pore number prediction value of the inorganic pores of the first is a second overall average coordination number of the second fine-grained rock mineral sample, is each undetermined parameter to be solved, .
7. The method for evaluating fine-grained rock reservoirs based on the geometry of the grains according to claim 5, characterized in that, The optimal solution for each undetermined parameter in the theoretical prediction equation is expressed as follows: wherein, is the optimal solution for each pending parameter, is a physical boundary matrix; The expressions for the functional mapping relationship between coordination number and pore number under different inorganic pore types are as follows: wherein, is the optimal solution of the maximum number of connected pores for the first class of inorganic pores, is the optimal solution of the average coordination number for the first class of inorganic pores, is the optimal solution of the sensitivity parameter of the number of pores with respect to the coordination number, is the value of the component extracted from the optimal solution of each undetermined parameter , is the value of the component extracted from the optimal solution of each undetermined parameter , is the value of the component extracted from the optimal solution of each undetermined parameter , is the value of the component extracted from the optimal solution of each undetermined parameter , is the value of the component extracted from the optimal solution of each undetermined parameter