Shale reservoir grading evaluation method

Abstract

The invention provides a shale reservoir grading evaluation method. The shale reservoir grading evaluation method comprises the following steps: acquiring petrology, geochemistry and pore structure parameters of different shale reservoirs by utilizing at least two experimental test methods; calculating a multi-scale fractal parameter of the pore structure of the shale reservoir; according to the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters, reservoir evaluation is conducted, and shale reservoir grading is completed. According to the method, shale reservoir petrology, geochemistry and pore structure parameters are obtained by comprehensively utilizing multiple means, pore structure multi-scale fractal parameters are calculated, reservoir characteristics are evaluated by combining multiple types of parameters, and reservoir grading evaluation is completed; the problem that a single test method or a single calculation method cannot systematically analyze the pore structure of the reservoir, divide the type of the reservoir and carry out reservoir evaluation is avoided, and the accuracy, effectiveness and comprehensiveness of grading evaluation of the shale reservoir are improved.

Description

Technical Field

[0001] The present invention belongs to the field of unconventional oil and gas exploration and development. Specifically, it relates to a method for hierarchical evaluation of shale reservoirs. Background Art

[0002] Shale oil mainly exists in free and adsorbed states, and a small amount is miscible with kerogen and exists in a dissolved state. The complexity of pore types, morphologies, and structures in shale reservoirs is one of the key factors controlling the occurrence state of shale oil. Shale reservoir classification can effectively guide the selection of sweet spots for shale oil and the division of development strata. Therefore, hierarchical evaluation of reservoirs is crucial.

[0003] Currently, for the characterization of pore structures, including FE-SEM, FIB-SEM, nano-CT, which can characterize shale pore morphologies, and CO2 adsorption, low-temperature N2 adsorption, HPMI, NMR, SAXS, and SANS, etc., the dimensions and scales characterized by different methods are different and have segmentation. A single method cannot perform full-scale characterization or the characterization method will damage the sample. Shale pores have strong heterogeneity. A single fractal model has limitations. There are cases where the fractal dimensions are the same, but there are obvious differences in information such as porosity and pore size distribution, making it difficult to effectively depict all the characteristics of shale. Therefore, a comprehensive method for classifying shale oil reservoirs needs to be established to effectively divide reservoir grades.

[0004] Chinese Patent CN116297111A discloses a method for predicting the permeability of tight sandstone based on mercury intrusion and fractal theory. It includes: sample classification, pretreatment; porosity detection, permeability detection, and mercury intrusion experiment; drawing a fractal curve; segmenting the fractal curve; calculating the fractal dimension; classifying pore throats and determining the pore throat radius with the smallest contribution to effective permeability; establishing the relationship between the comprehensive fractal dimension Df and the pore throat radius rmin with the smallest contribution to effective permeability; based on the analysis of permeability control factors, fitting a permeability prediction model with porosity, fractal dimension, and pore throat radius as independent variables; and testing the applicability of the model.

[0005] Chinese Patent CN117607000A discloses a method and device for obtaining the permeability of reservoir porous media. The method includes: obtaining the initial parameters of the capillary; obtaining the first fractal dimension of the porous media after water flooding according to the initial fractal dimension of the porous media before water flooding in the preset fractal dimension model; obtaining the first porosity of the porous media after water flooding according to the initial fractal dimension of the porous media and the first fractal dimension of the porous media after water flooding; obtaining the first fractal dimension of the capillary tortuosity after water flooding and the initial fractal dimension of the fine tube tortuosity before water flooding according to the first porosity and the preset capillary length change function; obtaining the permeability of the porous media before water flooding according to the parameters before water flooding and the initial fractal dimension of the fine tube tortuosity; and simultaneously, obtaining the permeability of the porous media after water flooding according to the parameters after water flooding and the first fractal dimension of the capillary tortuosity after water flooding.

[0006] Chinese Patent CN117705675A discloses a comprehensive characterization method for the full-scale and multi-dimensional pores of coal and rock. By using the combined measurement method of low-temperature gas adsorption, high-pressure mercury intrusion, and micron CT, the pore radius, surface roughness, and pore connectivity of coal and rock are comprehensively characterized on a full scale and in multiple dimensions, and the pore connectivity index L is calculated to judge the degree of coal and rock connectivity.

[0007] Chinese Patent CN115542420A discloses a method for determining the lithofacies division scheme of continental mixed sedimentary mudstone shale. It includes: core observation: observing according to the sedimentation principle of the formation in the order from deep to shallow in depth; formation division: using the results of core observation and combining with the characteristics of logging curves to conduct formation division; small layer evaluation: sampling each small layer, carrying out pyrolysis and physical property analysis of irregular samples, and conducting small layer evaluation according to the analysis results; step 4, component determination: conducting X-ray diffraction whole-rock analysis on the core corresponding to each small layer to analyze the rock mineral components; step 5, scheme determination: determining the target division scheme according to the above analysis results.

[0008] Chinese Patent ZL202110780202.2 authorizes a method for dividing the effective lithofacies types of carbonate rock interlayered mudstone shale. It includes: qualitatively dividing the bedding structure through core observation and thin section analysis; respectively using hydrochloric acid solution to determine the development degree of calcite, dolomite, and quartz in the light-colored bands in different structures, and determining that the main component of the band is carbonate rock; analyzing the mudstone shale with carbonate rock minerals as interlayers through the characteristics of the logging curves corresponding to the cored well section; according to the results of hydrochloric acid solution determination and logging curve analysis, sampling for experiments, and using X-ray diffraction whole-rock experiment analysis to determine the dominant mineral components; dividing the organic matter abundance according to the total organic carbon TOC content; determining the effective lithofacies types of carbonate rock interlayered mudstone shale according to the structure type, dominant mineral components, and organic carbon content.

[0009] The article "Characteristics of Pores and Fractures in Low-Evolution Continental Shales and Their Significance for Shale Oil Occurrence - Taking the Shahejie Formation in Qingnan Sag, Outer Periphery of Jiyang Depression as an Example" published by Wang Weiqing et al. in Bulletin of Geological Science and Technology, Vol. 43, No. 3, 2024 states that: The low-evolution shale oil resources in the Jiyang Depression have great potential and are one of the important fields after the successful breakthrough of medium- to high-maturity shale oil. In order to clarify the characteristics of pores and fractures in low-evolution continental shales and their significance for shale oil occurrence, taking the upper submember of the lower third to upper fourth members of the Shahejie Formation in Qingnan Sag, outer periphery of the Jiyang Depression as an example, a variety of techniques such as thin-section observation, organic carbon testing, X-ray diffraction (XRD) analysis, solvent extraction, low-temperature N2 adsorption, high-pressure mercury injection, scanning electron microscopy, and energy spectrum elements were comprehensively used. On the basis of dividing shale lithofacies, the pore types, sizes, fractal characteristics, and influencing factors of low-evolution shales were explored, the fracture development characteristics were clarified, and the important significance of shale pores and fractures for shale oil occurrence was expounded. The results show that: The total organic carbon mass fraction w(TOC) of the shales in the study area is mostly between 1.0% and 4.0%. The mineral components are mainly felsic minerals, followed by clay minerals and carbonate minerals. The pore types are mainly inkbottle pores and flat slit pores, including intergranular pores between quartz grains, interlamellar pores between clay mineral flakes, and intercrystalline pores of dolomite. The pore diameters are mostly less than 200 nm, showing a multi-peak distribution, mainly concentrated in 2 - 50, 50 - 80, and 100 - 200 nm. The shale reservoir develops many horizontal bedding fractures, high-angle fractures, and network fractures, which are mostly filled or impregnated with asphalt. Fractures are beneficial to the occurrence and migration of shale oil. The felsic mineral-rich lithofacies usually have higher pore volume and specific surface area than the clay mineral-rich lithofacies. Felsic minerals make a positive contribution to pores, and with the increase of thermal evolution degree, the pore volume and specific surface area show a trend of first decreasing and then increasing. When the vitrinite reflectance R O > 0.6%, the shale oil content increases significantly, mainly related to the start of a large amount of hydrocarbon generation from organic matter. Horizontal bedding fractures, intergranular pores between quartz grains, and intercrystalline pores of dolomite and calcite are favorable reservoir spaces for shale oil accumulation and occurrence.

[0010] The paper "Analysis of Fractal Characteristics and Influencing Factors of Tight Reservoirs in the Shanxi Formation in the Southern Ordos Basin" published by Zhao Hang et al. in the 11th volume, issue 2 of Unconventional Oil & Gas in 2024 records that in order to study the microscopic physical properties of tight reservoirs in the southern Ordos Basin, taking the Shanxi Formation in the study area as an example, using mercury injection data, cast thin sections and core data, introducing the fractal theory, the microscopic physical properties and pore-throat characteristics of tight reservoirs in the study area were quantitatively analyzed. The results show that: 1) The fractal dimension of the Shanxi Formation in the study area is mainly distributed between 2.15 and 2.81, indicating strong heterogeneity of the microscopic pores in the reservoir; 2) There is a good correlation between the fractal dimension in the study area and various physical property parameters of reservoir rocks. The fractal dimension can quantitatively characterize the microscopic pore characteristics of the reservoir, that is, the smaller the fractal dimension, the better the physical properties and heterogeneity of the reservoir, and the stronger the seepage capacity; 3) The fractal dimension can quantitatively characterize the microscopic physical property characteristics of the reservoir, and the fractal dimension can be used to classify and evaluate the reservoir; 4) The main factors affecting the physical properties of tight reservoirs are sedimentary facies and mineral composition of rocks.

[0011] The paper "Research on Permeability Model of Ultra-low Permeability Sandstone Reservoir Based on Reservoir Classification" published by Wang Zhonghao et al. in the Journal of Yangtze University (Natural Science Edition) in 2023 records that due to the influence of the sedimentary environment, the ultra-low permeability sandstone reservoir in the Dongfang 1-1 gas field in the Yinggehai Basin has strong reservoir heterogeneity and large differences in pore structure. The permeability model established by traditional methods has low interpretation accuracy and is difficult to meet the requirements of fine reservoir evaluation. There are mainly two types of sedimentary facies developed in the study area, namely the main erosion channel and the branch channel. Due to different sedimentary environments, there are obvious differences in the pore structure of the reservoir. Through the comparative analysis of reservoir physical properties, mercury injection, logging and other data, it is clear that the pore structure is the key factor affecting permeability. Through the interactive analysis of the capillary pressure curve and its fractal dimension, the reservoir is divided into two types according to the pore structure type, and the dimensionality reduction processing of conventional logging data is carried out based on the principal component analysis technology, and the classification standard is established according to the pore structure. The coincidence rate of sedimentary facies identified by this standard is 90.5%, which proves the reliability of this classification standard. On this basis, corresponding permeability interpretation models are established for different types of reservoirs. According to the processing results of actual well data, compared with the unclassified permeability model, the percentage of samples with a calculation error of the optimized permeability model within half an order of magnitude is increased from 70.3% to 83.6%, verifying the credibility of this classification method and permeability model, and providing guidance for the subsequent reservoir productivity evaluation in the study area.

[0012] Ren Haiying et al. recorded in the article "Analysis of the Applicability of the Fractal Model of Pore-Fracture in Coal Reservoirs Based on Low-Field Nuclear Magnetic Resonance Technology" published in Special Oil & Gas Reservoirs in 2024: To study the heterogeneity of pore-fracture distribution in coal reservoirs and reveal the occurrence state and transmission characteristics of coalbed methane reservoirs, taking the middle-high rank coal samples of the Permian Longtan Formation in the upper 9 blocks in the multi-coal seam development area in western Guizhou as an example, the nuclear magnetic resonance (NMR) test of coal samples was carried out by using the saturation-centrifugation test method to clarify the distribution of mobile water and bound water in the samples and the characteristics of pore-fracture structure; through the single and multifractal theories, the quantification of the heterogeneity of pore-fracture distribution in coal samples was characterized, and the correlation between different fractal dimension values and pore structure parameters was discussed. The research shows that the single fractal model can better characterize the heterogeneity of different fluid states or pore range distributions, while the multifractal model is more suitable for characterizing the heterogeneity of pore-fracture distributions; among the single fractal parameters, the fractal dimension value of seepage pores (D2) and the total fractal dimension value (DT) show a weak negative correlation; among the multifractal parameters, as the pore volume of adsorption pores increases, the spectral width D -10 —D 10 increases linearly, and the change of the spectral width D -10 —D0 is small; the samples can be divided into two types: adsorption pore-developed type and seepage pore-developed type. The former generally develops small pores (14%-43%), and the pore size distribution shows a "double-peak state", while the latter generally develops large pores (38%-62%), and the pore size distribution shows a "single-peak or double-peak state"; as the pore volume of seepage pores decreases, the heterogeneity of the overall distribution of seepage pores and pores increases; the adsorption pore porosity is the main factor affecting the pore-fracture distribution and has an obvious control effect on the multifractal characteristics of the pore-fracture structure of coal reservoirs. There are certain differences in the physical meanings between the single fractal model and the multifractal model, but both provide important theoretical support for the study of the pore-fracture structure of coal reservoirs.

[0013] As recorded in the article "Prediction of Fractal Dimension of Shale CT Images Based on Convolutional Neural Network and Its Application in Anti-Interference Ability" published by Sun Dingwei et al. in the Journal of Taiyuan University of Technology in 2024: The development of shale oil and gas often requires in-depth understanding of the pore and fracture distribution law inside the shale reservoir to optimize the development plan and improve production capacity. The fractal dimension is of great significance for reflecting the pore and fracture distribution law inside the shale. A method for predicting the fractal dimension of shale CT images based on convolutional neural network is proposed. A convolutional neural network model suitable for oil shale CT images is independently built. The CT slices of oil shale samples under different temperature pyrolysis and their corresponding fractal dimensions are used as the dataset and labels respectively. The built convolutional neural network is trained and predicted to achieve the extraction of the fractal dimension of shale CT images. Applying the trained model to various actual situations and comparing it with the box-counting method, the results show that the fractal dimension of shale CT images predicted by convolutional neural network is very close to the fractal dimension calculated by the box-counting method, with a difference of about 0.01. Moreover, the noise and artifacts in the CT images can be largely ignored when the calculation speed is faster. Therefore, it can be considered that the new method effectively captures the structural features of the image, can reliably estimate the fractal dimension of the image, and has good anti-interference ability.

[0014] As recorded in the article "Classification and Evaluation of Tight Sandstone Reservoirs Based on Lithofacies Constraint - Taking the He 8 Member Tight Sandstone Reservoir in the Second Area of Eastern Sulige as an Example" published by Li Bin et al. in the Journal of Xi'an Shiyou University (Natural Science Edition), Volume 39, Issue 1 in 2024: Aiming at the problems of poor physical properties, complex microscopic pore structure and heterogeneity characteristics of the tight sandstone reservoir in the second area of eastern Sulige, the reservoir characteristics research with lithofacies as the basic unit is carried out, and the reservoir is comprehensively classified and evaluated by combining macroscopic structure and microscopic characteristics. The results show that there are 6 lithofacies types developed in the lower submember of the target layer He 8 in the study area, namely massive bedding conglomerate facies, massive bedding medium-coarse sandstone facies, cross-bedded medium-coarse sandstone facies, parallel-bedded fine sandstone facies, wavy bedding / massive bedding siltstone and fine sandstone facies, and horizontal bedding / massive bedding mudstone facies. Among them, the physical properties, pore throat size and connectivity of the cross-bedded medium-coarse sandstone facies and the massive bedding medium-coarse sandstone facies are relatively good, and the heterogeneity is weak. Based on lithofacies, pore structure and their heterogeneity characterization parameters, a comprehensive classification and evaluation standard for the reservoir is established, and the target layer reservoir is divided into Class I - IV. Based on the single-well reservoir classification and evaluation results, 4 reservoir development patterns, namely gradual change type, thick layer alternating type, thin layer frequent alternating type, and thick and thin interbedded type, are summarized.

[0015] The article "Porosity Difference Characteristics and Influencing Mechanism of Different Lithofacies in Continental Mixed Shales - A Case Study of the Permian Lucaogou Formation in Jimusar Sag" published by He Xiaobiao et al. in the Journal of China University of Mining and Technology, Vol. 53, 2024, states: Lithofacies plays a decisive role in the distribution of high-quality reservoirs and favorable oil-bearing areas. In order to find out the reservoir characteristics and influencing mechanisms of different lithofacies or lithofacies combinations of shale oil reservoirs in the Permian Lucaogou Formation in Jimusar Sag, the whole-rock mineral X-ray diffraction analysis (XRD), total organic carbon content determination (TOC), rock thin section identification, vitrinite reflectance (R O ), field emission scanning electron microscopy, low-field nuclear magnetic resonance, high-pressure mercury injection experiment, low-temperature nitrogen adsorption experiment and physical property analysis were used to study the pore difference characteristics and influencing mechanisms of different lithofacies of the Lucaogou Formation. The results show that the main lithofacies developed in the study area include high-carbon massive silty dolomite (HK-Ⅱ1), high-carbon laminated silty shale (HW-Ⅰ2), carbon-rich massive silty dolomite (RK-Ⅱ1), and carbon-rich massive argillaceous siltstone (RK-Ⅲ2). The organic acid and carbonic acid dissolution associated with the generation of hydrocarbons in shale organic matter is significant, which has led to the large-scale development of intergranular pores, intragranular pores and dissolution fractures. There are obvious differences in the pore structure size and pore structure complexity of different shale lithofacies. The coupling and matching relationship between sedimentary environment and diagenesis is the key to the differential development of pores.

[0016] Wang Qingzhen et al. published an article titled "Pore Structure and Fractal Characteristics of Gulong Shale in Songliao Basin" in the Journal of Yangtze University (Natural Science Edition) Vol. 21, No. 3, 2024. The article states that the Qingshankou Formation shale in the Gulong Sag of the Songliao Basin is a continental pure shale reservoir with high organic abundance, high maturity, and high clay content. In order to reveal the pore structure characteristics and heterogeneity of the shale in the study area, the field emission scanning electron microscope, XRD whole rock mineral analysis, nitrogen adsorption and other technical methods were used to analyze the micro-nano pores and fracture storage space types and pore structures of the shale in the study area, classify the lamellae, and study the fractal characteristics of the shale reservoir in the study area through the FHH model. The results show that the mineral composition of Gulong shale is mainly clay minerals, quartz, and feldspar. The lamellae are mainly divided into long strip-shaped through-fractures, short cluster-shaped pinch-out fractures, bifurcated interlayer fractures, and organic-filled fractures, with short cluster-shaped pinch-out fractures being the main ones. Gulong shale has typical fractal characteristics, with the fractal dimension of large pores being 2.528-2.555 and that of small and medium pores being 2.7912.829. Mesopores of 2-50 nm in Gulong shale are the main reservoir space, with an average pore volume accounting for 79.85%. Organic carbon content is positively correlated with specific surface area, pore volume, and fractal dimension, while average pore size is negatively correlated with fractal dimension. Organic matter and clay content are the main controlling factors affecting fractal structural characteristics. The study of shale pore structure and fractal characteristics is of great significance for the evaluation of shale oil and gas content and development reserves.

[0017] In 2024, He Junhao et al. published an article titled "Study on the heterogeneity of tight sandstone reservoirs and their causes - Taking the Chang 6 oil layer in the Dalugou area of ​​Jing'an Oilfield as an example" in the Journal of Shenzhen University (Science and Engineering). The article states: The heterogeneity characteristics and causes of the Chang 6 tight sandstone reservoir in the Dalugou area of ​​Jing'an Oilfield were clarified. The macroscopic heterogeneity and microscopic pore structure fractal characteristics of the Chang 6 reservoir were analyzed with the guidance of fractal theory by using cast thin sections, scanning electron microscopy, physical property testing, mercury injection experiments, drilling and logging data, and the causes of the reservoir heterogeneity were discussed. The results show that the sandstone type of the Chang 6 reservoir is mainly feldspar sandstone, and the pore structure types are divided into type I (medium pore-medium and fine throat type), type II (medium and small pore-fine throat type) and type III (small pore-micro throat type) based on the mercury injection experiment parameters. The macroscopic heterogeneity of Chang 6 reservoir was studied from three aspects: intra-layer, inter-layer and plane. The evaluation of permeability heterogeneity coefficient showed that each sub-section of Chang 6 was highly heterogeneous, and the heterogeneity gradually increased from Chang 61 to Chang 63. The microscopic heterogeneity of reservoir based on fractal theory showed that Chang 6 reservoir samples had three-segment fractal characteristics. The average values ​​of fractal dimensions D1, D2 and D3 of macropores, mesopores and micropores were 2.9892, 2.6931 and 2.1772 respectively. Macropores and mesopores were the main sources of permeability and microscopic pore structure heterogeneity of Chang 6 reservoir. The heterogeneity of macropores and micropores had a more obvious effect on the maximum mercury saturation. The causes of Chang 6 reservoir heterogeneity were analyzed. Among them, sedimentary microfacies controlled macroscopic heterogeneity factors such as sand body and physical properties. Compaction greatly reduced the primary intergranular pores of the reservoir, which had a great influence on the heterogeneity of macropores and micropores. Chlorite cementation and lauzenite dissolution had a strong effect on pore structure transformation and had a great influence on the heterogeneity of reservoir pore structure.

[0018] Although the above classification scheme solves some problems, it still has certain limitations when applied to shale reservoir classification and evaluation. Summary of the invention

[0019] The embodiment of the present application provides a method for grading and evaluating shale reservoirs, which improves the accuracy, effectiveness and comprehensiveness of grading and evaluating shale reservoirs.

[0020] In a first aspect, an embodiment of the present application provides a method for grading and evaluating a shale reservoir, comprising:

[0021] At least two experimental test methods are used to obtain petrological, geochemical and pore structure parameters of different shale reservoirs; multi-scale fractal parameters of the pore structure of the shale reservoir are calculated; reservoir evaluation is performed based on the obtained petrological, geochemical, pore structure and multi-scale fractal parameters to complete the shale reservoir classification.

[0022] Among them, at least two experimental testing methods are used to obtain petrological, geochemical, and pore structure parameters of different shale reservoirs, including: for shale samples in the study area, the mineral component content of shale is obtained through X-ray diffraction whole-rock experimental testing to determine the lithology type; the total organic carbon (TOC) content of shale with different lithologies is determined through total organic carbon experimental testing, and the free oil S 1-1 +S 1-2 content is determined through programmed heating pyrolysis experimental testing, and the Tmax value is determined through pyrolysis experimental testing; the structural characteristics of shale are obtained through core observation and thin section identification, and the lithofacies type is jointly determined by the lithology type, organic carbon content, and structural type.

[0023] Among them, at least two experimental testing methods are used to obtain petrological, geochemical, and pore structure parameters of different shale reservoirs, including: the pore types of different shales are determined through scanning electron microscopy experimental testing; the hysteresis loop type of the adsorption-desorption curve of shale samples is obtained through low-temperature nitrogen adsorption experimental testing, the pore surface area SSA is calculated using the BET equation, and the pore size distribution d Na and pore volume dv Na are calculated using the BJH model.

[0024] Among them, at least two experimental testing methods are used to obtain petrological, geochemical, and pore structure parameters of different shale reservoirs, including: the total pore volume dv Ma and pore throat size distribution d Ma are obtained through high-pressure mercury intrusion experiments; the saturated oil T2 spectrum and T 2,gm 、T 2,35 、T 2,50 parameters are obtained through nuclear magnetic resonance T2 spectrum testing, where T 2,gm is the geometric mean of the T2 spectrum, and T 2,35 the pore structure parameter of the nuclear magnetic resonance T2 spectrum refers to the T2 value corresponding to 35% of the cumulative pore volume curve when the T2 relaxation time decreases from large to small; T 2,50 the pore structure parameter of the nuclear magnetic resonance T2 spectrum refers to the T2 value corresponding to 50% of the cumulative pore volume curve when the T2 relaxation time decreases from large to small.

[0025] Among them, the multi-fractal parameters of the pore structure of the shale reservoir are calculated, including: calculating the multi-fractal parameters of the pore structure of the shale reservoir based on nuclear magnetic resonance T2 spectrum parameters, and the parameters include: capacity dimension D0, information dimension D1, correlation dimension D2, dispersion degree of porosity relative to pore size D1 / D0, generalized dimension D q , singularity index α0, multi-fractal spectrum f(α), and skewness A of the multi-fractal spectrum.

[0026] Among them, reservoir evaluation is carried out based on the obtained petrological, geochemical, pore structure, and multi-fractal parameters to complete the classification of shale reservoirs, including:

[0027] Characteristics of Type I shale: T 2,gm , T 2,35 and T 2,50 are all greater than 1 ms, T 2,gm is greater than T 2,50 , the T2 spectrum has a three-peak characteristic, the p2 and p3 peaks are both greater than the p1 peak. Compared with other types of shale, T 2,gm , T 2,35 , T 2,50 , pore throat size d Ma , TOC content, free oil content, Tmax value are the largest, the BET pore surface area SSA is the smallest, and it has a bimodal pore throat size d Ma distribution characteristic, which is the most favorable type of shale;

[0028] Characteristics of Type II shale: T 2,gm , T 2,35 and T 2,50 are all greater than 1 ms, T 2,gm is less than T 2,50 , the T2 spectrum has a bimodal or unimodal characteristic, the p2 peak is the largest and the p3 peak is the smallest. Compared with other types of shale, the TOC content is smaller than that of Type I shale and greater than that of Type III and Type IV shale. The BET pore surface area SSA is greater than that of Type I shale and less than that of Type III and Type IV shale, which is the second most favorable type of shale;

[0029] Characteristics of Type III shale: T 2,35 is greater than 1 ms, T 2,gm is greater than T 2,50 , the T2 spectrum has a bimodal characteristic, the p1 peak and the p2 peak are the same, and the p3 peak is the smallest. The BET pore surface area SSA is greater than that of Type IV shale, which is the third most favorable type of shale;

[0030] Characteristics of Type IV shale: T 2,gm , T 2,35 and T 2,50 are all less than 1 ms, the T2 spectrum has a unimodal characteristic, and the p1 peak is the largest. Compared with other types of shale, T 2,gm , T 2,35 , T 2,50 , TOC content, free oil content, Tmax value, pore size distribution d Na is the smallest, the BET pore surface area SSA is the largest, and it has a unimodal pore size d Na distribution characteristic, which is the least favorable type of shale.

[0031] Among them, reservoir evaluation is carried out according to the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters, and the classification of shale reservoirs is completed, including: D0 > D1 > D2, indicating multi-fractal characteristics, where D0 is the capacity dimension, D1 is the information dimension, and D2 is the correlation dimension; for Type I and Type IV shales, the D0 value first decreases and then increases, and the D0 value of Type II shale is the lowest; for Type II shale, D1 / D0 is the smallest, and the difference in pore content at different scales is the largest. For Type II shale, Δα is the smallest, and Δα is α max -α min , α min is the minimum value of the singularity index, and α max is the maximum value of the singularity index; for Type IV shale, α0 is the largest, and the skewness A of the multi-scale fractal spectrum of Type I, Type II, Type III, and Type IV shales is less than 1; for Type I shale, the pore structure is the most complex and the heterogeneity is the strongest. For Type II shale, the pore structure is the simplest and the heterogeneity is the weakest. For Type III and Type IV shales, they are between Type I and Type II. The pore structure of Type IV shale is more complex and the heterogeneity is stronger than that of Type III shale.

[0032] Among them, the capacity dimension D0 characterizes the average characteristics of the pore structure distribution, reflects the complexity of the pore structure, and the larger the value, the more complex it is. D1 / D0 reflects the dispersion degree of porosity relative to the pore size, and the larger the value, the smaller the difference in pore content at different scales; the singularity index α0 reflects the heterogeneity of the pore structure, and the larger the value, the stronger the heterogeneity. α min is the minimum value of the singularity index, α max is the maximum value of the singularity index, and Δα is α max -α min , and Δα reflects the heterogeneity of the pore structure, and the larger the value, the stronger the heterogeneity.

[0033] In the second aspect, the present application provides a method for optimizing sweet spots of shale oil, which is selected based on the method for classifying and evaluating any of the above shale reservoirs.

[0034] In the third aspect, the present application provides a method for dividing development series of shale oil, which is divided based on the method for classifying and evaluating any of the above shale reservoirs.

[0035] The present application has carried out a large number of studies on the problems in the prior art. By using nuclear magnetic resonance, the full-scale pore parameter characteristics can be quickly and nondestructively obtained, and by using multi-scale fractal, the complexity and heterogeneity of pores can be analyzed. The purpose is to solve the problems of nondestructive full-scale characterization, reservoir classification and evaluation of pore structures, and to improve the accuracy, effectiveness and comprehensiveness of shale reservoir classification and evaluation to a certain extent.

[0036] Under the guidance of the above idea, the technical means adopted by the present invention are as follows: Through experimental tests such as X-ray diffraction of whole rock, total organic carbon, pyrolysis, temperature-programmed pyrolysis, low-temperature nitrogen adsorption, high-pressure mercury injection, and nuclear magnetic resonance, petrological, geochemical, and pore structure parameters of shale are obtained. The multi-scale fractal parameters of the pore structure of the shale reservoir are calculated using the one-dimensional nuclear magnetic resonance T2 parameters of saturated oil. According to the obtained petrological, geochemical, pore structure, and multi-scale fractal parameters, reservoir evaluation is carried out to complete the classification of the shale reservoir.

[0037] The method for grading and evaluating the shale reservoir in the embodiment of the present application has the following beneficial effects:

[0038] The present application comprehensively uses multiple means to obtain petrological, geochemical, and pore structure parameters of the shale reservoir, calculates the multi-scale fractal parameters of the pore structure, and combines the "four types of parameters" to evaluate the reservoir characteristics, completing the grading and evaluation of the reservoir, avoiding the inability of a single test method or a single calculation method to systematically analyze the pore structure of the reservoir, divide the reservoir types, and carry out reservoir evaluation. The accuracy of the reservoir grading and evaluation has been increased by 60 percentage points, reaching 98%; the work efficiency has been improved, avoiding ineffective work, reducing the workload of one person for 30 days to one person for 10 days to complete; accurately and efficiently completing the reservoir grading and evaluation provides important support for the optimization of the target window of horizontal wells and efficient development, facilitating the successful drilling of horizontal wells for shale oil and meeting the needs of exploration and production. The method of the present application is applied to 13 wells in a certain sag of a certain basin, with a total peak daily oil production of 224 tons and a cumulative oil and gas equivalent production exceeding 150,000 tons. Description of the Drawings

[0039] Figure 1 It is a schematic flow chart of the method for grading and evaluating the shale reservoir in the embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of the lithology distribution of shale;

[0041] Figure 3 It is a schematic diagram of the structural characteristics of shale;

[0042] Figure 4 It is the type of reservoir space of shale;

[0043] Figure 5 It is the characteristic of the hysteresis loop of the low-temperature nitrogen adsorption-desorption curve of shale (a, Type I shale; b, Type II shale; c, Type III shale; d, Type IV shale);

[0044] Figure 6 It is the pore size d of the low-temperature nitrogen adsorption of shale Na Distribution characteristics (a, Type I shale; b, Type II shale; c, Type III shale; d, Type IV shale);

[0045] Figure 7For the pore throat size d of shale by high-pressure mercury intrusion Ma Distribution characteristics (a, type I shale; b, type II shale; c, type III shale; d, type IV shale);

[0046] Figure 8 For the distribution characteristics of the nuclear magnetic resonance T2 spectrum of shale (a, b are type I shale; c, d are type II shale; e, f are type III shale; g, h are type IV shale);

[0047] Figure 9 For the distribution characteristics of the pore structure parameters of the nuclear magnetic resonance T2 spectrum of shale;

[0048] Figure 10 For the distribution characteristics of the multi-scale fractal parameters of shale Specific implementation mode

[0049] The present application will be further introduced below in conjunction with the accompanying drawings and embodiments.

[0050] The following introduction provides multiple embodiments of the present invention. Different embodiments can be replaced or combined. Therefore, the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, C, and another embodiment includes features B, D, then the present application should also be considered to include embodiments containing one or more all other possible combinations of features A, B, C, D, even though such embodiments may not be explicitly described in the following content. In the present application, "shale" and "mud shale" have the same meaning.

[0051] Embodiment 1

[0052] As Figure 1 shown, the method for hierarchical evaluation of the mud shale reservoir in the present application includes: S1, obtaining petrological, geochemical, and pore structure parameters of different mud shale reservoirs by using at least two experimental testing methods; S2, calculating the multi-scale fractal parameters of the pore structure of the mud shale reservoir; S3, performing reservoir evaluation according to the obtained petrological, geochemical, pore structure, and multi-scale fractal parameters to complete the hierarchical classification of the mud shale reservoir.

[0053] The present application improves the accuracy, effectiveness, and comprehensiveness of the hierarchical evaluation of the mud shale reservoir.

[0054] Embodiment 2

[0055] The method for hierarchical evaluation of the mud shale reservoir in the present application includes:

[0056] The first step is to carry out X-ray diffraction whole rock, total organic carbon, pyrolysis, temperature-programmed pyrolysis, low-temperature nitrogen adsorption, high-pressure mercury intrusion, and nuclear magnetic resonance experimental tests on the mud shale samples in the study area.

[0057] In the second step, the content of different mineral components of the shale can be obtained by using the X-ray diffraction whole-rock experiment, and the lithology type of the shale is determined by using the publicly disclosed method in Chinese Patent 202211179373.0, "A method for determining the lithofacies division scheme of continental mixed shale".

[0058] In the third step, the total organic carbon (TOC) content of shales with different lithologies is determined by using the total organic carbon experiment, and the free oil S 1-1 +S 1-2 content of the shale is determined by using the temperature-programmed pyrolysis experiment, and the Tmax value and S1 are determined by using the pyrolysis experiment.

[0059] In the fourth step, the structural characteristics of the shale can be obtained by using core observation and thin-section identification, and the structural type of the shale is determined by using the authorized method in Chinese Patent ZL202110780202.2, "A method for dividing effective lithofacies types of carbonate-interbedded shale layers". The lithofacies type is jointly determined by using the lithology type, organic carbon content, and structural type.

[0060] In the fifth step, for the scanning electron microscope experiment, the FEI Quanta 200F field emission electron microscope is used. Samples are taken vertically along the bedding plane. After washing with oil and drying, a flat surface is obtained by polishing with silicon carbide (SiC) sandpaper, and then argon ion polishing is carried out. The scanning electron microscope images are magnified 200 - 10,000 times to obtain backscattered images in different fields of view with a resolution of 20 - 320 nm to determine the pore types of the shale.

[0061] In the sixth step, the low-temperature nitrogen adsorption experiment is completed by using the Micromeritics ASAP 2460 specific surface area and porosity analyzer of the American Micromeritics company. After washing with oil and drying, 4 - 5 g of powdered samples (40 - 60 mesh) are degassed under vacuum at 105 °C for 12 h, and then at liquid nitrogen temperature, the pressure is increased to the saturated vapor pressure of liquid nitrogen and then gradually decreased to obtain the type of hysteresis loop of the adsorption - desorption curve of the shale sample with the pressure ranging from 0.01 to 0.993. The pore surface area SSA is calculated by using the BET equation based on the adsorption curve, and the pore size distribution d Na The single-point pore volume is the pore volume dv Na The characteristic parameters of different types of reservoirs are different.

[0062] In the seventh step, for the high-pressure mercury intrusion experiment, the Micromeritics AutoPore 9520 mercury intrusion instrument of the American Micromeritics company is used. After washing with oil for 7 days, the plunger samples are dried at 110 °C under vacuum for 24 h, and the sample mass and volume are measured. The maximum mercury intrusion pressure is 240 MPa. The corresponding minimum pore throat diameter of 7 nm is calculated by using the Washburn equation, and the total pore volume dv is obtained according to the mercury intrusion curve.Ma and the pore throat size distribution d Ma , and the characteristics of reservoir parameters of different types are different.

[0063] Step 8: The nuclear magnetic resonance T2 spectrum test is completed using a MesoMR-060H-I nuclear magnetic resonance analyzer and imager, with a waiting time of 3000 ms, an echo spacing of 0.07 ms, 64 times of stacking, and 6000 echo numbers. The two states of oil-washing and drying and saturated oil are tested. Before the test, a mixture of dichloromethane and acetone with a volume ratio of 3:1 is used to wash the oil at 80 °C and 0.25 MPa for 7 days, then vacuum heat at 110 °C for 72 h, cool to room temperature, and test the nuclear magnetic resonance spin echo train and T2 spectrum of the dry sample. Then, the dry sample is evacuated for 24 h, pressurized to 10 MPa to saturate with n-dodecane for 24 h, and the nuclear magnetic resonance spin echo train of the saturated oil shale is tested. The de-based inversion method is used to obtain the saturated oil T2 spectrum and T 2,gm 、T 2,35 、T 2,50 . T 2,gm is the geometric mean of the T2 spectrum, T 2,35 and T 2,50 The pore structure parameters of the nuclear magnetic resonance T2 spectrum respectively refer to the T2 values corresponding to 35% and 50% of the cumulative pore volume curve when the T2 relaxation time decreases from large to small. The characteristics of reservoir parameters of different types are different.

[0064] Step 9: Calculate the multi-scale fractal parameters of the pore structure of the shale reservoir based on the nuclear magnetic resonance T2 spectrum. The number of relaxation time points of the T2 spectrum is set to 256 (2 k , k = 8). Since q varies from -10 to 10 at an interval of 0.5, D -10 is D min , D 10 is D max . D0, D1, and D2 are the capacity dimension, information dimension, and correlation dimension respectively. D1 / D0 is the dispersion degree of porosity relative to pore size. α0 is the singularity index, α min is the minimum value of the singularity index, α max is the maximum value of the singularity index, f(α) is the multi-scale fractal spectrum, D q is the generalized dimension, Δα is α max -α min , A is the skewness of the multi-scale fractal spectrum (α0 - α min ) / (α max -α0).

[0065] Step 10: Use the above test parameters to evaluate the reservoir characteristics by grading. Characteristics of Type I shale: T 2,gm 、T 2,35 and T 2,50 are all greater than 1 ms, and T 2,gm is greater than T 2,50, the T2 spectrum has a three-peak feature, where the p2 and p3 peaks are both larger than the p1 peak. Compared with other types of shale, T 2,gm 、T 2,35 、T 2,50 、pore throat size d Ma 、TOC content, free oil content, and Tmax value are the largest, and the BET pore surface area SSA is the smallest. It has a bimodal pore throat size d Ma distribution characteristic and is the most favorable type of shale.

[0066] Characteristics of Type II shale: T 2,gm 、T 2,35 and T 2,50 are all greater than 1 ms, and T 2,gm is less than T 2,50 . The T2 spectrum has a bimodal or unimodal feature, with the p2 peak being the largest and the p3 peak being the smallest. Compared with other types of shale, the TOC content is smaller than that of Type I shale and greater than that of Type III and Type IV shales. The BET pore surface area SSA is greater than that of Type I shale and smaller than that of Type III and Type IV shales, making it the second most favorable type of shale.

[0067] Characteristics of Type III shale: T 2,35 is greater than 1 ms, and T 2,gm is greater than T 2,50 . The T2 spectrum has a bimodal feature, where the p1 and p2 peaks are similar or identical (identical indicating a high degree of similarity between the p1 and p2 peaks), and the p3 peak is the smallest. The BET pore surface area SSA is greater than that of Type IV shale, making it the third most favorable type of shale.

[0068] Characteristics of Type IV shale: T 2,gm 、T 2,35 and T 2,50 are all less than 1 ms. The T2 spectrum has a unimodal feature, with the p1 peak being the largest. Compared with other types of shale, T 2,gm 、T 2,35 、T 2,50 、TOC content, free oil content, Tmax value, and pore size distribution d Na are the smallest, and the BET pore surface area SSA is the largest. It has a unimodal pore size d Na distribution characteristic and is the least favorable type of shale.

[0069] In the eleventh step, D0 > D1 > D2 indicates the presence of multifractal characteristics. For type I and type IV shales, the D0 value first decreases and then increases. The D0 value of type I to type IV shales is relatively large, and the pore structure is more complex. The D0 value of type II shale is the lowest, and the pore structure is the simplest. The D1 / D0 of type II shale is the smallest, and the difference in pore content at different scales is the largest, followed by type IV shale. The Δα of type II shale is the smallest, followed by type III shale. The Δα of type I and type IV shales is not much different. The α0 of type IV shale is the largest, and the α0 of type I, type II, and type III shales is not much different. The A of type I, type II, type III, and type IV shales is less than 1, and the skewness of the multifractal spectrum fluctuates slightly. The pore structure of type I shale is the most complex and the heterogeneity is the strongest. The pore structure of type II shale is the simplest and the heterogeneity is the weakest. Type III and type IV shales are between type I and type II. The pore structure of type IV shale is more complex and the heterogeneity is stronger than that of type III. Compared with using a single-scale fractal model, using a multifractal spectrum can better characterize the uniformity of the pore structure.

[0070] This application can quickly and nondestructively obtain the full-scale pore parameter characteristics by using nuclear magnetic resonance, and analyze the complexity and heterogeneity of pores by using multifractal, solving the problems of nondestructive full-scale characterization of pore structure, reservoir classification, and evaluation, and improving the accuracy, effectiveness, and comprehensiveness of the classification and evaluation of shale reservoirs.

[0071] Example Three

[0072] The method for classifying and evaluating shale reservoirs in this application includes:

[0073] In the first step, 25 samples were selected from the second member of the Funing Formation of Well SY1 in the Gaoyou Sag of the Subei Basin for X-ray diffraction whole rock, total organic carbon, pyrolysis, temperature-programmed pyrolysis, low-temperature nitrogen adsorption, high-pressure mercury injection, and nuclear magnetic resonance experimental tests.

[0074] In the second step, as Figure 2 shown, the content of different shale mineral components can be obtained by using X-ray diffraction whole rock experimental tests to determine the shale lithology type. I 1 - Grey cloud shale; I 2 - Feldspathic grey cloud shale; I 3 - Clayey grey cloud shale; II 1 - Feldspathic shale; II 2 - Clayey feldspathic shale; II 3 - Grey cloud feldspathic shale; III 1 - Clay shale; III 2 - Feldspathic clay rock; III 3 - Grey cloud clay rock; IV 1 - Feldspathic-clayey mixed shale; IV 2 - Feldspathic-grey cloud mixed shale; IV 3- Argillaceous - dolomitic mixed shale.

[0075] In the third step, use the total organic carbon experiment to test and determine the organic carbon TOC content of shale with different lithologies. Use the programmed pyrolysis experiment to test and determine the free oil S 1-1 +S 1-2 content of shale. Use the pyrolysis experiment to test and determine the Tmax value and S1, as shown in Table 1.

[0076] Table 1 Organic carbon content, Tmax value, S1 content, S 1-1 content and S 1-2 content

[0077]

[0078] In the fourth step, the structural characteristics of shale can be obtained by core observation and thin section identification. The lithology type, organic carbon content and structural type jointly determine the lithofacies type, such as Figure 3 shown in the thin section identification photos are respectively: (a) massive felsic - dolomitic mixed shale; (b) laminated felsic shale; (c) laminated dolomitic argillaceous felsic shale; (d) laminated felsic - dolomitic mixed shale; (e) laminated dolomitic shale; (f) laminated felsic shale; (g) laminated argillaceous felsic shale; (h) laminated dolomitic felsic shale; (i) laminated felsic - dolomitic mixed shale. The organic carbon content of the present invention is divided according to the boundaries of low carbon TOC < 0.6%, medium carbon 0.6% ≤ TOC ≤ 1.5%, and high carbon TOC > 1.5%.

[0079] In the fifth step, the FEI Quanta 200F field emission electron microscope is used for the scanning electron microscope experiment. Samples are taken perpendicular to the bedding. After washing with oil and drying, a flat surface is obtained by polishing with silicon carbide (SiC) sandpaper, and then argon ion polishing is carried out. The scanning electron microscope images are magnified 200 - 10000 times to obtain backscattered images in different fields of view with a resolution of 20 - 320 nm to determine the pore types of shale, such as Figure 4As shown in the figure, (a) SY1-6, massive felsic-calcareous mixed shale, with intragranular pores in clay minerals and marginal pores in felsic and calcareous minerals (oil-bearing); (b) SY1-24, laminated calcareous shale, with intergranular pores between quartz and dolomite, less developed; (c) SY1-39, laminated felsic-calcareous shale, with intercrystalline pores in calcite, and visible intergranular pores in quartz (oil-bearing); (d) SY1-27 laminated felsic shale, with a small amount of intragranular pores in clay minerals; (e) SY1-34, laminated felsic shale, with marginal pores in quartz, and intercrystalline pores (oil-bearing) developed when calcareous minerals are enriched; (f) SY1-31, laminated clay-rich felsic shale, with strong pore heterogeneity, developed pores in felsic-rich laminae, including intergranular pores, marginal pores in quartz and intragranular pores in clay minerals (oil-bearing); (g) SY1-36, laminated clay-rich felsic shale, with intragranular pores in clay minerals, visible intercrystalline pores in pyrite and microfractures; (h) SY1-18, laminated calcareous-felsic shale, with intercrystalline pores in calcareous minerals, partially filled with clay minerals; (i) SY1-32, laminated felsic-calcareous mixed shale, with intragranular pores in clay minerals, and visible marginal pores in brittle minerals; (j) SY1-40, laminated felsic-calcareous mixed shale, with intragranular pores in clay minerals, and visible marginal pores in brittle minerals.

[0080] In the sixth step, the low-temperature nitrogen adsorption experiment was completed using a Micromeritics ASAP 2460 specific surface area and porosity analyzer from the United States. After washing and drying 4 - 5 g of powdered samples (40 - 60 mesh), they were degassed under vacuum at 105 °C for 12 h, and then at liquid nitrogen temperature, the pressure was increased to the saturated vapor pressure of liquid nitrogen and then gradually decreased to obtain the type of hysteresis loop of the adsorption-desorption curve of the shale sample with a pressure range of 0.01 - 0.993. The BET equation was used to calculate the pore surface area SSA from the adsorption curve, and the BJH model was used to calculate the pore size distribution d Na , and the single-point pore volume is the pore volume dv Na , and the characteristics of reservoir parameters of different types are different, as shown in Table 2.

[0081] Table 2 Distribution of pore structure parameters of mud shale

[0082]

[0083] In the seventh step, the high-pressure mercury intrusion experiment was tested using a Micromeritics AutoPore 9520 mercury intrusion instrument from the United States. After washing the plug sample with oil for 7 days, it was dried at 110 °C for 24 h under vacuum, the sample mass and volume were measured, the maximum mercury intrusion pressure was about 240 MPa, and the corresponding minimum pore throat diameter was calculated to be about 7 nm through the Washburn equation. The total pore volume dv Ma and the pore throat size distribution dMa , as shown in Table 2, the characteristics of reservoir parameters of different types are different.

[0084] Step 8: The nuclear magnetic resonance T2 spectrum test is completed using a MesoMR-060H-I nuclear magnetic resonance analyzer and imager, with a waiting time of 3000 ms, an echo spacing of 0.07 ms, 64 stacking times, and 6000 echo numbers. Two states of oil washing and drying and saturated oil are tested. Before the test, a mixed solution of dichloromethane and acetone with a volume ratio of 3:1 is used to wash the oil at 80 °C and 0.25 MPa for 7 days, then vacuum heat at 110 °C for 72 h, cool to room temperature to test the nuclear magnetic resonance spin echo train and T2 spectrum of the dry sample. Then, the dry sample is evacuated for 24 h and pressurized with 10 MPa of n-dodecane for 24 h to test the nuclear magnetic resonance spin echo train of the saturated oil shale, and the de-based inversion method is used to obtain the saturated oil T2 spectrum and T 2,gm 、T 2,35 、T 2,50 . T 2,gm is the geometric mean of the T2 spectrum, and T 2,35 and T 2,50 The pore structure parameters of the nuclear magnetic resonance T2 spectrum respectively refer to the T2 values corresponding to 35% and 50% of the cumulative pore volume curve when the T2 relaxation time decreases from large to small. As shown in Table 2, the characteristics of reservoir parameters of different types are different.

[0085] Step 9: Calculate the multi-scale fractal parameters of the pore structure of the shale reservoir based on the nuclear magnetic resonance T2 spectrum using the multi-scale fractal measure. The number of relaxation time points of the T2 spectrum is set to 256 (2 k , k = 8). Since q varies from -10 to 10 at an interval of 0.5, D -10 is D min , D 10 is D max . D0 is the capacity dimension, which characterizes the average characteristics of the pore structure distribution, reflects the complexity of the pore structure, and the larger the value, the more complex it is. D1 and D2 are the information dimension and the correlation dimension respectively. D1 / D0 reflects the dispersion degree of porosity relative to the pore size. The larger the value, the smaller the difference in pore content at different scales, that is, pores of all scales are developed. α0 is the singularity index, which reflects the heterogeneity of the pore structure. The larger the value, the stronger the heterogeneity. α min is the minimum value of the singularity index, α max is the maximum value of the singularity index, f(α) is the multi-scale fractal spectrum, D q is the generalized dimension, Δα is α max -α min , which reflects the heterogeneity of the pore structure. The larger the value, the stronger the heterogeneity. A is the skewness of the multi-scale fractal spectrum (α0 - α min ) / (α max -α0), as shown in Table 3.

[0086] Table 3 Distribution of Multiscale Fractal Parameters of Nuclear Magnetic Resonance T2 Spectrum

[0087]

[0088]

[0089] Step 10: Evaluate the reservoir characteristics using the above test parameters. Generally speaking, the T2 spectrum of shale has a three-peak distribution characteristic. The p1 peak is distributed at less than 0.6 ms, the p2 peak is distributed at 0.6 - 20 ms, and the p3 peak is greater than 20 ms, corresponding to micropores (<100 nm), mesopores (100 - 1000 nm), and macropores (>1000 nm) respectively. The nuclear magnetic porosity ranges from 0.25% to 4.40%, with an average value of 2.17%, as shown in Table 2.

[0090] Characteristics of Type I shale: As Figure 8 shown in (a), (b) and Figure 9 as shown, T 2,gm (average 4.06 ms), T 2,35 (average 11.85 ms) and T 2,50 (average 2.77 ms) are all greater than 1 ms, T 2,gm is higher than T 2,50 , the T2 spectrum has a three-peak characteristic, the p2 and p3 peaks are both greater than the p1 peak, the average porosity component of the p1 peak is 0.59%, the average porosity component of the p2 peak is 0.94%, and the average porosity component of the p3 peak is 0.74%, indicating the existence of a large number of mesopores, medium macropores and a small number of micropores. Compared with other types of shale, it has the largest T 2,gm (average 4.06 ms), the largest T 2,35 (average 11.58 ms), the largest T 2,50 (average 2.77 ms), the largest TOC content (average 2.07%), the largest Tmax value (average 448.3 °C), a relatively large nuclear magnetic porosity (average 2.28%), the largest pore throat radius d Ma (average 326.7 nm), the largest free oil content S 1-1 +S 1-2 (average 2.01 mg / g), the smallest BET pore surface area SSA (average 5.88 m 2 / g), the pore size distribution d Na average 9.66 nm, the pore volume dv Na average 10.8×10 -3 cm 3 / g. As Figure 5 shown in (a), the type of low-temperature nitrogen adsorption hysteresis loop is H2-H3 type, as Figure 6 shown in (a), it has a bimodal pore size dNa Distribution characteristics, such as Figure 7 As shown in (a) of Ma Distribution characteristics, with the left peak at 10 - 20 nm and the right peak at 300 - 4000 nm, being the most favorable shale type.

[0091] Characteristics of Type II shale: As Figure 8 shown in (c), (d) of Figure 9 and 2,gm T 2,35 (average 2.45 ms) and T 2,50 are both greater than 1 ms, T 2,gm (average 1.53 ms) is less than T 2,50 (average 1.66 ms), the T2 spectrum has a bimodal or unimodal characteristic, with the p2 peak being the largest, the p3 peak being the smallest, the average porosity component of the p1 peak being 0.19%, the average porosity component of the p2 peak being 0.75%, and the average porosity component of the p3 peak being 0.01%, indicating the development of mesopores and almost no development of micropores. Compared with other types of shale, the average TOC content of 1.48% is smaller than that of Type I shale and greater than that of Type III (average 1.04%) and Type IV shale (average 0.77%). The average Tmax value of 437.7 °C is smaller than that of Type I shale and greater than that of Type III (average 436.0 °C) and Type IV shale (average 433.3 °C). The BET pore surface area SSA of 6.51 m 2 / g is larger than that of Type I shale and smaller than that of Type III (average 8.62 m 2 / g) and Type IV shale (average 12.49 m 2 / g). The free oil content S 1-1 +S 1-2 is on average 1.36 mg / g, the pore size distribution d Na is on average 10.46 nm, the pore volume dv Na is on average 12.63×10 -3 cm 3 / g, the pore throat radius d Ma is on average 5.67 nm, and the porosity is on average 0.95%. As Figure 5 shown in (b) of Figure 6 the low-temperature nitrogen adsorption hysteresis loop type is mainly H2 - H3 type, and H3 type is also developed. As Na shown in (b) of Figure 7 it has a bimodal pore size d Ma distribution characteristic. As

[0092] Characteristics of Type III shale: As Figure 8 shown in (e), (f) of Figure 9 and2,35 (Average 1.37 ms) is greater than 1 ms, T 2,gm (Average 0.75 ms) is greater than T 2,50 (Average 0.66 ms), the T2 spectrum has a bimodal feature, the p1 peak and p2 peak are similar, the p3 peak is the smallest, the average porosity component of the p1 peak is 1.13%, the average porosity component of the p2 peak is 1.15%, and the average porosity component of the p3 peak is 0.11%, indicating that micropores and mesopores are developed and macropores are hardly developed. The TOC content is on average 1.04%, the Tmax value is on average 436 °C, and the free oil content S 1-1 +S 1-2 is on average 1.46 mg / g, the BET pore surface area SSA (average 8.62 m 2 / g) is greater than that of type IV shale (average 12.49 m 2 / g), the pore size distribution d Na is on average 9.06 nm, the pore volume dv Na is on average 15.07×10 -3 cm 3 / g, the pore throat radius d Ma is on average 10.0 nm, the porosity is on average 2.39%, as Figure 5 shown in (c) below, the type of low-temperature nitrogen adsorption hysteresis loop is H2-H3 type, as Figure 6 shown in (c) below, it has a bimodal pore size d Na distribution feature, as Figure 7 shown in (c) below, it has a unimodal pore throat size d Ma distribution feature, with a peak value of 10 - 40 nm, which is a relatively unfavorable shale type.

[0093] Characteristics of type IV shale: As Figure 8 shown in (g), (h) below and Figure 9 shown, T 2,gm (Average 0.49 ms), T 2,35 (Average 0.68 ms) and T 2,50 (Average 0.39 ms) are all less than 1 ms, the T2 spectrum has a unimodal feature, the p1 peak is the largest, the average porosity component of the p1 peak is 1.46%, the average porosity component of the p2 peak is 0.76%, and the average porosity component of the p3 peak is 0.10%, indicating that micropores are developed and mesopores and macropores are hardly developed. Compared with other types of shale, T 2,gm , T 2,35 , T 2,50 , the TOC content (average 0.77%), the free oil content S 1-1 +S 1-2 (Average 0.62 mg / g), the Tmax value (average 433 °C), the pore size distribution d Na(Average 6.02 nm) is the smallest, and the BET pore surface area SSA (average 12.49 m 2 / g) is the largest. As shown in (d) of Figure 6 , it has a unimodal pore size d Na distribution characteristic. As shown in (d) of Figure 5 , the type of low-temperature nitrogen adsorption hysteresis loop is mainly H2-H3 type, and at the same time, H2 type is also developed. As shown in (d) of Figure 7 , it has a unimodal pore throat size d Ma distribution characteristic, with a peak value of 10 - 30 nm, and the pore volume dv Na average of 18.69×10 - 3 cm 3 / g, the pore throat radius d Ma average of 11.46 nm, and the porosity average of 2.32%, which is the most unfavorable shale type.

[0094] The eleventh step, as shown in Table 3, the D0 of the mud shale is distributed from 0.65 to 0.97, with an average of 0.90, indicating that the pore structure of the mud shale in the study area is complex. The D1 is distributed from 0.52 to 0.88, with an average of 0.77, and the D2 is distributed from 0.49 to 0.85, with an average of 0.73. As Figure 10 shown, all samples show the same trend, and the distribution trends of D1 and D2 are similar, D0 > D1 > D2, indicating that the T2 spectrum has multi-scale fractal characteristics. The D0 values of type I to type IV shales first decrease and then increase. The D0 values of type I shale (average 0.94) and type IV shale (average 0.92) are relatively large, and the pore structure is more complex. The D0 value of type II shale is the smallest, and the pore structure is the simplest. The D min is distributed from 1.97 to 3.64, with an average of 3.28, and the D max is distributed from 0.45 to 0.82, with an average of 0.68. The D1 / D0 is distributed from 0.77 to 0.92, with an average of 0.85, indicating that there are pores with different pore sizes in the shale. The D1 / D0 of type II shale is the smallest, with an average of 0.78, and the difference in the pore content of different scales is the largest, that is, mainly single-scale pores are developed, followed by type IV shale with an average D1 / D0 of 0.84.

[0095] The Δα of mud shale ranges from 1.74 to 3.37, with an average of 2.95, indicating that the pores have a heterogeneous structure. The minimum average Δα of type II shale is 2.43, followed by type III shale with an average of 2.93. The Δα of type I (average 3.04) and type IV (average 3.06) shales is not much different. The α0 of type IV shale is the largest, with an average of 1.31, and the α0 of type I (average 1.19), type II (average 1.23), and type III (average 1.22) shales is not much different. The heterogeneity of type II shale is the weakest. The A of mud shale ranges from 0.16 to 0.48, with an average of 0.27, all less than 1, indicating that the skewness of the multifractal spectrum has only slight fluctuations. Compared with using a single fractal model, using the multifractal spectrum can better characterize the uniformity of the pore structure.

[0096] The method of this application uses experimental tests such as X-ray diffraction of whole rock, rock pyrolysis, low-temperature nitrogen adsorption, high-pressure mercury intrusion, and nuclear magnetic resonance to obtain petrological, geochemical, and pore structure parameters of the mud shale reservoir, calculate multifractal parameters, conduct systematic evaluations, form a reservoir classification evaluation method, and complete reservoir classification.

[0097] The method adopted in this application can comprehensively use multiple means to obtain petrological, geochemical, and pore structure parameters of the mud shale reservoir, calculate the multifractal parameters of the pore structure using the nuclear magnetic resonance T2 spectrum parameters, combine the "four types of parameters" to evaluate the reservoir characteristics, complete the reservoir classification evaluation, and avoid the inability of a single test method or a single calculation method to systematically analyze the reservoir pore structure, divide the reservoir types, and conduct reservoir evaluations. The accuracy of the reservoir classification evaluation has increased by 60 percentage points, reaching 98%; the work efficiency has been improved, avoiding ineffective work, reducing the workload of one person for 30 days to one person for 10 days to complete; accurately and efficiently completing the reservoir classification evaluation provides important support for the optimization of the target window of horizontal wells and efficient development, helps the successful drilling of shale oil horizontal wells, and meets the needs of exploration and production. The method of this application has been applied to 13 wells in a certain sag of a certain basin, with a total peak daily oil production of 224 tons and a cumulative oil and gas equivalent production exceeding 150,000 tons.

[0098] This application also provides a method for optimizing sweet spots of shale oil, which is selected based on any one of the above mud shale reservoir classification evaluation methods.

[0099] This application also provides a method for dividing shale oil development series, which is divided based on any one of the above mud shale reservoir classification evaluation methods.

[0100] The above introduction is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for grading and evaluating shale reservoirs, characterized in that: include: Use at least two experimental test methods to obtain petrology, geochemistry and pore structure parameters of different shale reservoirs; Calculate the multi-scale fractal parameters of the pore structure of the shale reservoir; conduct reservoir evaluation and complete shale reservoir classification based on the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters.

2. The method for grading and evaluating shale reservoirs according to claim 1, characterized in that: At least two experimental test methods are used to obtain the petrology, geochemistry and pore structure parameters of different shale reservoirs, including: for the shale samples in the study area, the X-ray diffraction whole-rock experimental test is used to obtain the shale mineral component content and determine the lithology type; the total organic carbon experimental test is used to determine the TOC content of shale with different lithologies; the gradient temperature pyrolysis experimental test is used to determine the free oil S 1-1 +S 1-2 The content is determined by pyrolysis experiment test, and the Tmax value is determined; the structural characteristics of shale are obtained by core observation and thin section identification, and the lithofacies type is determined by lithology type, organic carbon content and structural type.

3. The method for grading and evaluating shale reservoirs according to claim 1 or 2, characterized in that: Use at least two experimental test methods to obtain the petrology, geochemistry and pore structure parameters of different shale reservoirs, including: using scanning electron microscope experimental test to determine the pore types of different shale; using low-temperature nitrogen adsorption experimental test to obtain the hysteresis loop type of shale sample adsorption-desorption curve, using BET equation to calculate the pore surface area SSA, and using BJH model to calculate the pore size distribution d Na 、pore volume dv Na .

4. The method for grading and evaluating shale reservoirs according to claim 1 or 2, characterized in that: Use at least two experimental test methods to obtain different shale reservoir petrology, geochemistry and pore structure parameters, including: high pressure mercury injection test to obtain the total pore volume dv Ma and pore throat size distribution d Ma ; Through the nuclear magnetic resonance T2 spectrum test, the saturated oil T2 spectrum and T 2,gm 、T 2,35 、T 2,50 Parameters, T 2,gm is the geometric mean of the T2 spectrum, T 2,35 The pore structure parameter of the nuclear magnetic resonance T2 spectrum refers to the T2 value corresponding to 35% of the cumulative pore volume curve of T2 relaxation time from large to small; 2,50 The pore structure parameter of the nuclear magnetic resonance T2 spectrum refers to the T2 value corresponding to 50% of the cumulative pore volume curve of T2 relaxation time from large to small.

5. The method for grading and evaluating shale reservoirs according to claim 1 or 2, characterized in that: Calculate the multi-scale fractal parameters of the pore structure of shale reservoirs, including: Calculate the multi-scale fractal parameters of the pore structure of shale reservoirs based on nuclear magnetic resonance T2 spectrum parameters, the parameters include: capacity dimension D0, information dimension D1, correlation dimension D2, porosity dispersion relative to pore size D1 / D0, generalized dimension D q , singular index α0, multi-scale fractal spectrum f(α), multi-scale fractal spectrum skewness A.

6. The method for grading and evaluating shale reservoirs according to claim 1 or 2, characterized in that: Based on the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters, reservoir evaluation is carried out to complete the shale reservoir classification, including: Characteristics of Type I shale: T 2,gm 、T 2,35 and T 2,50 Both are greater than 1ms, T 2,gm Greater than T 2,50 The T2 spectrum has three peaks, and the p2 and p3 peaks are both larger than the p1 peak. Compared with other types of shales, T 2,gm 、T 2,35 、T 2,50 、pore throat size d Ma , TOC content, free oil content, Tmax value is the largest, BET pore surface area SSA is the smallest, and has a bimodal pore throat size d Ma distribution characteristics, the most favorable shale type; Characteristics of Type II shale: T 2,gm , T 2,35 and T 2,50 Both are greater than 1ms, T 2,gm Less than T 2,50 , T2 spectrum has a double-peak or single-peak feature, with the p2 peak being the largest and the p3 peak being the smallest. Compared with other types of shales, the TOC content is smaller than that of type I shale, but larger than that of type III and type IV shale. The BET pore surface area SSA is larger than that of type I shale, but smaller than that of type III and type IV shale, making it the second most favorable shale type; Characteristics of Type III shale: T 2,35 Greater than 1ms, T 2,gm Greater than T 2,50 , the T2 spectrum has a bimodal feature, the p1 peak is consistent with the p2 peak, the p3 peak is the smallest, and the BET pore surface area SSA is greater than that of type IV shale, which is the third favorable shale type; Characteristics of Type IV shale: T 2,gm 、T 2,35 and T 2,50 The T2 spectrum is single-peaked, with the p1 peak being the largest. Compared with other types of shales, T 2,gm 、T 2,35 、T 2,50 , TOC content, free oil content, Tmax value, pore size distribution d Na The smallest, BET pore surface area SSA is the largest, with a single peak pore size d Na Distribution characteristics make it the most unfavorable shale type.

7. The method for grading and evaluating shale reservoirs according to claim 6, characterized in that: According to the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters, reservoir evaluation was carried out and shale reservoir classification was completed, including: D0>D1>D2, indicating that it has multiple fractal characteristics, D0 is the capacity dimension, D1 is the information dimension, and D2 is the correlation dimension; the D0 value of type I and type IV shales first decreases and then increases, and the D0 value of type II shale is the lowest; type II shale has the smallest D1 / D0 and the largest difference in pore content at different scales, and type II shale has the smallest Δα, Δα is α max -α min , α min is the minimum value of the singular index, α max is the maximum value of the singularity index; Type IV shale has the largest α0, and the multi-scale fractal spectrum skewness A of Type I, Type II, Type III, and Type IV shale is less than 1; Type I shale has the most complex pore structure and the strongest heterogeneity, Type II shale has the simplest pore structure and the weakest heterogeneity, Type III and Type IV shale are between Type I and Type II, and Type IV shale has a more complex pore structure and stronger heterogeneity than Type III.

8. The method for grading and evaluating shale reservoirs according to claim 5, characterized in that: The capacity dimension D0 characterizes the average characteristics of the pore structure distribution and reflects the complexity of the pore structure. The larger the value, the more complex it is. D1 / D0 reflects the dispersion of porosity relative to pore size. The larger the value, the smaller the difference in pore content of different scales. The singularity index α0 reflects the heterogeneity of the pore structure. The larger the value, the stronger the heterogeneity. min is the minimum value of the singular index, α max is the maximum value of the singular index, Δα is α max -α min , Δα reflects the heterogeneity of the pore structure, and the larger the value, the stronger the heterogeneity.

9. A method for optimizing a shale oil sweet spot layer, characterized in that: The selection is made based on the method for shale reservoir classification evaluation according to any one of claims 1 to 8.

10. A method for dividing shale oil development strata, characterized in that: The classification is performed based on the method for grading and evaluating shale reservoirs according to any one of claims 1 to 8.

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