Shale oil geological engineering dessert evaluation method and device based on geological modeling

Abstract

The invention discloses a shale oil geological engineering dessert evaluation method and device based on geological modeling, and the method comprises the steps: determining a shale oil facies classification scheme and shale oil dessert evaluation key parameter values according to the shale oil geological engineering logging interpretation data, determining the corresponding scores of different shale rocks, and obtaining the shale oil dessert evaluation key parameter values of the shale oil facies classification scheme and the shale oil dessert evaluation key parameter values of the shale oil facies classification scheme; and determining a target sweet spot evaluation parameter weight based on sweet spot evaluation attributes of the lithofacies three-dimensional model and actual well drilling productivity data, establishing a shale oil geological engineering integrated comprehensive sweet spot factor, and determining a shale oil geological engineering comprehensive sweet spot evaluation result based on the sweet spot factor and the target sweet spot evaluation parameter value. Through the technical scheme of the embodiment of the invention, comprehensive evaluation of shale oil geological engineering desserts is realized, complexity of shale oil geological features can be adapted, correlation between geological and engineering dessert evaluation parameters is improved, and fine evaluation of shale oil dessert space distribution is ensured.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method and apparatus for evaluating the sweet spot of shale oil geological engineering based on geological modeling. Background Technology

[0002] With the continuous growth of global energy demand, the supply of conventional oil and gas resources is becoming increasingly tight. As an unconventional oil and gas resource, the accurate evaluation of shale oil reservoirs and the identification of sweet spots have become important issues in energy exploration and development.

[0003] Currently, traditional geological engineering sweet spot evaluation mainly combines geological exploration and geophysical analysis results to evaluate the exploitation conditions of sweet spot areas, such as measuring and evaluating parameters like permeability and oil content. However, traditional geological engineering sweet spot evaluation methods cannot adapt to the complexity of shale oil reservoirs, and the poor correlation between various parameters makes it difficult to conduct a detailed evaluation of shale oil sweet spots, thus hindering the selection of favorable target areas for shale oil exploration and development. Summary of the Invention

[0004] This invention provides a method and apparatus for evaluating shale oil geological engineering sweet spots based on geological modeling, so as to achieve a comprehensive evaluation of geological engineering sweet spots, adapt to the complexity of shale oil reservoirs, improve the correlation between various parameters, ensure the fine evaluation of shale oil sweet spots, and improve the accuracy of selecting favorable target areas for shale oil exploration and development.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating the geological sweet spot of shale oil based on geological modeling, including:

[0006] Based on the original logging data and core data of single wells in the target area, determine the logging interpretation results of shale oil geological engineering evaluation corresponding to different depth intervals in the target area;

[0007] Based on the well logging interpretation results of the shale oil geological engineering evaluation, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are determined. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0008] Based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth intervals of the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals of the target area are determined.

[0009] Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores for different depth ranges in the target area are determined.

[0010] Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data, and seismic data.

[0011] Based on the comprehensive sweet spot factor and target sweet spot evaluation parameter values ​​of the geological engineering, the comprehensive sweet spot evaluation result of shale oil geological engineering is determined.

[0012] Secondly, embodiments of the present invention also provide a shale oil geological engineering sweet spot evaluation device based on geological modeling, comprising:

[0013] The interpretation result determination module is used to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area based on the original logging data and original core data of a single well in the target area.

[0014] The classification scheme determination module is used to determine the shale oil lithofacies classification scheme based on the well logging interpretation results of the shale oil geological engineering evaluation, and to determine the key parameters for shale oil sweet spot evaluation. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0015] The parameter value determination module is used to determine the key parameter values ​​for shale oil sweet spot evaluation corresponding to different depth ranges in the target area based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth ranges in the target area and the key parameters for shale oil sweet spot evaluation.

[0016] The lithofacies score determination module is used to determine the lithofacies type and the corresponding lithofacies score of the target area at different depth intervals based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation.

[0017] The sweet spot factor determination module is used to determine the weights corresponding to the target sweet spot evaluation parameters based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, and to establish a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data and seismic data.

[0018] The evaluation result determination module is used to determine the comprehensive sweet spot evaluation result of shale oil geological engineering based on the comprehensive sweet spot factor of geological engineering and the target sweet spot evaluation parameter value.

[0019] Thirdly, embodiments of the present invention also provide an electronic device, characterized in that the electronic device comprises: at least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the shale oil geological engineering sweet spot evaluation method based on geological modeling provided in any embodiment of the present invention.

[0022] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, which are used to enable a processor to execute the shale oil geological engineering sweet spot evaluation method based on geological modeling provided in any embodiment of the present invention.

[0023] The technical solution of this invention, by determining the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area based on the original logging data and original core data of a single well in the target area, helps to more precisely characterize geological features. Based on the shale oil geological engineering evaluation logging interpretation results, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are identified. These key parameters include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness. Based on the shale oil geological engineering evaluation logging interpretation results and the relationship between the logging data curves at different depth intervals in the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals in the target area are determined, which can quantify geological features and provide specific indicators for sweet spot evaluation. Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores at different depth intervals in the target area are determined, which helps to more intuitively evaluate the production potential of different lithofacies types. Based on a three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established to comprehensively evaluate the production potential of the target exploration area. Based on the comprehensive geological engineering sweet spot factor and the target sweet spot evaluation parameter values, the comprehensive geological engineering sweet spot evaluation results for shale oil are determined. By conducting a comprehensive sweet spot evaluation of the target area, the complexity of shale oil reservoirs can be adapted, the correlation between various parameters can be improved, the detailed evaluation of shale oil sweet spots can be ensured, and the accuracy of selecting favorable target areas for shale oil exploration and development can be improved.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a shale oil geological engineering sweet spot evaluation method based on geological modeling, according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a schematic diagram of a shale oil facies classification strategy according to Embodiment 1 of the present invention;

[0028] Figure 3 This is a structural diagram of a fully connected neural network according to Embodiment 1 of the present invention;

[0029] Figure 4 This is a flowchart of a shale oil geological engineering sweet spot evaluation method based on geological modeling, according to Embodiment 2 of the present invention;

[0030] Figure 5 This is a schematic diagram of a shale oil geological engineering sweet spot evaluation device based on geological modeling, according to Embodiment 3 of the present invention.

[0031] Figure 6 This is a schematic diagram of the electronic device used to implement the shale oil geological engineering sweet spot evaluation method based on geological modeling, as described in this embodiment of the invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "target," "current," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 This invention provides a flowchart of a method for evaluating shale oil geological engineering sweet spots based on geological modeling, as described in Embodiment 1 of the present invention. This embodiment is applicable to situations requiring a comprehensive evaluation of shale oil geological engineering sweet spots. Figure 1 As shown, this method can be executed by a geological modeling-based shale oil geological engineering sweet spot evaluation device, which can be implemented in hardware and / or software and can be configured in electronic equipment. Figure 1 As shown, the method specifically includes the following steps:

[0036] S110. Based on the original logging data and original core data of single wells in the target area, determine the logging interpretation results for shale oil geological engineering evaluation corresponding to different depth intervals in the target area.

[0037] The target area can refer to a specific geological region where shale sweet spot evaluation is required. Raw logging data can refer to unprocessed logging data from a single well (i.e., a single well) obtained through drilling operations. Raw core data can refer to detailed descriptions and test results of core samples retrieved during drilling (single well) processes. Shale oil geological engineering evaluation logging interpretation results can refer to a series of data obtained through logging techniques reflecting the geological characteristics and engineering properties of shale oil reservoirs.

[0038] Specifically, the process involves acquiring raw well logging data and core data from individual wells in the target area. Based on geological stratification, structural features, or exploration requirements, the target area is divided into different depth intervals. Each interval represents a specific geological stratum or structural unit. The raw well logging data and core data are then segmented according to depth intervals. Further, based on exploration objectives and geological characteristics, logging data and core data corresponding to different depth intervals within the target exploration area are selected. Finally, logging interpretation results for shale oil geological engineering evaluation are extracted using logging techniques, thereby accurately describing the geological characteristics of the target exploration area.

[0039] For example, S110 may include: preprocessing the original logging data and original core data of a single well in the target area to obtain preprocessed original logging data and original core data; performing depth repositioning on the preprocessed original logging data and original core data to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area.

[0040] Specifically, the raw logging data and core data from individual wells are processed to remove outliers, missing values, or illogical data points. This can be achieved by setting reasonable thresholds, using interpolation methods (such as linear interpolation, Lagrange interpolation, etc.) to fill in missing values, or directly deleting invalid data. Data from different sources and in different formats are uniformly converted into a standard format suitable for analysis. This includes unifying units, data types, and storage formats to ensure consistency and accuracy in subsequent processing. Due to various errors that may exist during logging (such as instrument drift, wellbore irregularities, etc.), depth correction of the logging data is required. The corrected logging data is then depth-matched with the core data. Based on the core description and logging response characteristics, the logging data is finely divided to determine the logging data and core data corresponding to different depth intervals in the target exploration area. Furthermore, logging interpretation results for shale oil geological engineering evaluation corresponding to different depth intervals in the target exploration area are extracted based on logging techniques. Through depth repositioning, the lithology, physical properties, and other information of the formation can be described more accurately.

[0041] S120. Based on the well logging interpretation results of shale oil geological engineering evaluation, determine the shale oil lithofacies classification scheme and determine the key parameters for shale oil sweet spot evaluation. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0042] Shale oil facies classification schemes can refer to a method used to classify shale oil reservoirs into different facies types based on well logging interpretation data. Key parameters for shale oil sweet spot evaluation can refer to geological characteristics and engineering features related to shale oil sweet spot evaluation, reflecting formation reservoir capacity and oil and gas potential, and reflecting rock mechanics. Key parameters for shale oil evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and facies thickness. Porosity refers to the ratio of pore volume contributing to fluid storage and flow within a rock or lithology to the total volume. Facies thickness refers to the thickness of a specific facies (such as shale facies) within the formation. Organic matter abundance refers to the content of organic matter in shale, usually expressed as a weight percentage. Compressibility (brittleness index) is an important parameter describing shale strength, used to evaluate the brittle characteristics of shale reservoirs. In rock mechanics, brittleness refers to the property of rocks to break easily under stress with minimal deformation. Oil content refers to the amount or enrichment of oil in a shale reservoir. It reflects the total amount and distribution characteristics of oil resources within the shale formation. Mobility refers to the ability of oil resources in a shale reservoir to be effectively extracted under current technological conditions. It reflects the recoverability and economic potential of shale oil resources. Mineral content refers to the proportion of various mineral components in a shale reservoir. It reflects the petrological characteristics and geochemical properties of the shale.

[0043] Specifically, based on the well logging interpretation results of shale oil geological engineering evaluation, combined with geological knowledge and experience, a shale oil lithofacies classification strategy is formulated, dividing shale at different depths into different lithofacies types, refining the evaluation objects, improving the pertinence of the evaluation, and determining porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness as key parameters for shale oil sweet spot evaluation.

[0044] For example, a shale lithofacies classification strategy based on a combination of mineral content and total organic carbon (TOC) content can be established (e.g., Figure 2 As shown in the figure, the shale lithofacies type is determined based on the TOC content and mineral content.

[0045] S130. Based on the well logging interpretation results of shale oil geological engineering evaluation and the relationship between well logging data curves at different depth intervals in the target area and key parameters for shale oil sweet spot evaluation, determine the key parameter values ​​for shale oil sweet spot evaluation corresponding to different depth intervals in the target area.

[0046] Well logging data curves refer to a series of curves reflecting the physical properties of the formation, measured by well logging instruments during the drilling process. For example, well logging data curves can be resistivity curves, sonic transit time curves, etc. Key parameters for shale oil evaluation can refer to a series of specific values ​​or levels that reflect the formation's reservoir capacity and oil and gas potential.

[0047] Specifically, statistical methods are used to analyze the relationship between well logging data curves and key parameters for shale oil sweet spot evaluation. This includes calculating correlation coefficients and performing regression analysis to identify the sensitivity and predictive ability of well logging data curves to these key parameters. The shape, amplitude, and trend of the well logging data curves are analyzed to identify the response patterns of different key shale oil evaluation parameters on the logging curves. For example, high-porosity formations may correspond to lower resistivity and higher sonic transit time. Based on the identification results, a relationship between well logging data curves and key parameters for shale oil sweet spot evaluation is established to improve the accuracy of well logging data in predicting these parameters. The well logging interpretation results for shale oil geological engineering evaluation are input into the relationship between well logging data curves and key parameters for shale oil sweet spot evaluation at different depth intervals in the target area to calculate the corresponding key parameter values ​​for shale oil sweet spot evaluation at different depth intervals in the target area. These parameter values ​​include porosity, total organic carbon content, etc., and are important bases for evaluating shale sweet spots.

[0048] For example, a mathematical relationship is established between well logging data curves (such as resistivity, sonic transit time, natural gamma, etc.) and key parameters for shale oil sweet spot evaluation (such as porosity, total organic carbon content, etc.). The well logging interpretation results for shale oil geological engineering evaluation at different depth intervals in the target exploration area are matched with the established mathematical relationship between well logging data curves and key parameters for shale oil sweet spot evaluation. This determines the porosity, lithofacies thickness, total organic carbon content, and brittleness index corresponding to the well logging interpretation data for shale oil geological engineering evaluation at different depth intervals in the target exploration area, reducing labor costs and improving efficiency.

[0049] For example, porosity can be calculated using the relationship between resistivity and porosity; total organic carbon content can be calculated using the relationship between natural gamma and total organic carbon content; lithofacies thickness can be calculated using sonic transit time and lithology identification results; and brittleness index can be calculated using mineral composition and rock mechanical properties.

[0050] For example, the linear relationship between well logging data curves and porosity can be expressed as:

[0051] φ=41.28+0.027Δt-17.2681ρ+0.134φ CNL

[0052] Where φ is the shale porosity; Δt is the sonic transit time; ρ is the lithological density; φ CNL To compensate for neutron porosity.

[0053] The nonlinear relationship between well logging data curves and total organic carbon content can be expressed as:

[0054] TOC = 0.791136 + 3.28383 * (log10 LLD)-2.24127*(log 10 LLD) 2 +4.9029*(log 10 LLD) 3 -1.8378(log 10 LLD) 4

[0055] Wherein, TOC is the total organic carbon content of shale; LLD is the deep-seated resistivity.

[0056] S140. Based on the shale oil facies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, determine the facies types and corresponding facies scores for different depth ranges in the target area.

[0057] Here, lithofacies type refers to the type and combination characteristics of different rock components in a shale reservoir. Lithofacies score refers to the specific value assigned to different categories of shale lithofacies.

[0058] Specifically, based on the shale oil lithofacies classification scheme and combined with key parameters for shale oil evaluation, the shale lithofacies of different depth ranges in the target exploration area are classified. Then, based on the classification results and the degree of influence of key parameters for shale oil sweet spot evaluation on the classification results, lithofacies types in different depth ranges of the target area are assigned scores. Through an automated and standardized lithofacies classification and scoring process, the time and cost of manual interpretation can be significantly reduced, improving exploration efficiency.

[0059] For example, S140 may include: determining the lithofacies types corresponding to different depth ranges in the target area based on the shale oil lithofacies classification scheme, mineral content, and organic matter abundance; and determining the lithofacies scores corresponding to different shale lithofacies types in the target area based on lithofacies type, oil content, and mobility.

[0060] Specifically, based on the established shale oil facies classification scheme, which systematically classifies shale facies according to key parameters such as mineral content and organic matter abundance, the scheme determines the facies types corresponding to different depth ranges in the target area using the parameter values ​​corresponding to mineral content and organic matter abundance. For each facies type, its oil-bearing capacity and mobility are evaluated. This typically involves testing shale samples (e.g., determining parameters such as porosity, permeability, and oil saturation) and analyzing the actual shale oil production capacity in conjunction with production data. Based on the evaluation results of the oil-bearing capacity and mobility of each facies type, and their distribution proportions in different depth ranges of the target area, a facies score for each facies type is calculated. The facies score reflects the importance and development potential of that facies type in the target area.

[0061] S150. Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, determine the weights corresponding to the target sweet spot evaluation parameters, and establish a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on lithofacies type, key parameter values ​​of shale oil sweet spot evaluation, lithofacies score, well logging data and seismic data.

[0062] The 3D model for evaluating shale oil sweet spots can refer to a 3D visualization model built based on geological and engineering data, used to display the attribute characteristics of sweet spots in shale oil reservoirs. Actual drilling production data refers to measured data on drilling production obtained through actual drilling operations during shale oil exploration and development. This data typically includes key indicators such as drilling depth, drilling speed, oil production, and gas production. Target sweet spot evaluation parameters refer to a series of key parameters used to measure and characterize the characteristics of sweet spot areas during shale oil sweet spot evaluation. The comprehensive geological and engineering sweet spot factor refers to a comprehensive evaluation index that reflects the geological and engineering characteristics of sweet spot areas in shale oil reservoirs. Well logging data refers to the results of physical quantity measurements of the formation within the wellbore using well logging instruments during drilling. Seismic data refers to data obtained by artificially generating seismic waves and utilizing their propagation and reflection characteristics within the formation to acquire information about underground geological structures.

[0063] Specifically, by combining three-dimensional models of shale oil sweet spot evaluation attributes corresponding to different lithofacies types with actual drilling production data, the importance of each parameter to sweet spot evaluation is reflected, and the weights corresponding to the target sweet spot evaluation parameters are determined. Based on the weights corresponding to the target sweet spot evaluation parameters, a comprehensive geological and engineering sweet spot factor is established. This factor can comprehensively consider both geological and engineering factors and can fully reflect the sweet spot characteristics of shale oil reservoirs.

[0064] For example, S150 may include: determining target sweet spot evaluation parameters related to the evaluation of sweet spot areas based on a three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types, wherein the target sweet spot evaluation parameters include: porosity, compressibility, organic matter abundance, lithofacies thickness, and lithofacies fraction; determining the weights corresponding to the target sweet spot evaluation parameters in different depth ranges of the target area based on the target sweet spot evaluation parameters and actual drilling production data; and determining the comprehensive geological engineering sweet spot factor based on the weights corresponding to the target sweet spot evaluation parameters.

[0065] Specifically, based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types, target sweet spot evaluation parameters closely related to the evaluation of sweet spot areas are identified. These parameters typically include porosity, compressibility, organic matter abundance, lithofacies thickness, and lithofacies fraction. Actual well production data from the target areas are collected, and statistical analysis is performed on this data to identify the correlation between each target sweet spot evaluation parameter and production capacity. Statistical methods (such as regression analysis and principal component analysis) or expert experience methods are used to determine the weight of each target sweet spot evaluation parameter in evaluating sweet spot areas. The determination of weights should fully consider the importance and contribution of each parameter to sweet spot evaluation. Based on the determined target sweet spot evaluation parameters and their weights, a comprehensive geological and engineering sweet spot factor is constructed. This factor should be able to comprehensively consider both geological and engineering factors, fully reflecting the potential and value of shale oil reservoir sweet spot areas.

[0066] For example, a three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on lithofacies type, key parameter values ​​for shale oil sweet spot evaluation, lithofacies score, well logging data, and seismic data. This includes: constructing an original three-dimensional geological model based on lithofacies type, well logging data, and seismic data; and mapping the key parameter values ​​for shale oil sweet spot evaluation and lithofacies score to the original three-dimensional geological model to construct a three-dimensional model of shale oil sweet spot evaluation attributes.

[0067] Among them, the original three-dimensional geological model can refer to a three-dimensional visualization model that reflects the characteristics of underground geological structure, lithology distribution, and stratigraphic morphology, constructed through three-dimensional modeling technology.

[0068] Specifically, well logging data, including resistivity, density, sonic velocity, and natural gamma, is acquired, reflecting the physical properties of the formation. Seismic exploration data, including seismic wave travel time, amplitude, and frequency, is acquired, revealing the subsurface geological structure. Well logging and seismic data are fused, combining the high-resolution characteristics of well logging data with the wide-area coverage of seismic data through techniques such as seismic inversion. An original 3D geological model is constructed based on the fused data. This model should reflect the morphology, structure, and lithological distribution of the subsurface strata. Key parameters for shale oil sweet spot evaluation (such as porosity, compressibility, and organic matter abundance) are mapped into the original 3D geological model using interpolation and simulation methods. Lithofacies fractions are also mapped into the 3D geological model to characterize the distribution and proportion of different lithofacies in the reservoir, generating a 3D model of shale oil sweet spot evaluation attributes. By constructing this 3D model, the spatial distribution of the reservoir and key attribute parameters of the sweet spot area can be visually displayed, providing accurate basis and direction for exploration and development, thereby improving exploration efficiency.

[0069] S160. Based on the comprehensive sweet spot factor of geological engineering and the target sweet spot evaluation parameter value, determine the comprehensive sweet spot evaluation result of shale oil geological engineering.

[0070] Among them, the comprehensive evaluation result of the target sweet spot can refer to the conclusion or result obtained after comprehensively evaluating the shale reservoirs at different depth intervals within the target exploration area.

[0071] Specifically, based on the comprehensive sweet spot factor of geological engineering and combined with the target sweet spot evaluation parameter value, the comprehensive sweet spot evaluation result of shale oil geological engineering is determined, which improves the accuracy and reliability of shale oil sweet spot evaluation, allows for more rational allocation of exploration and development resources, avoids blind investment and waste, and improves resource utilization efficiency.

[0072] For example, S160 may include: normalizing the target sweet spot evaluation parameter value to obtain the normalized target sweet spot evaluation parameter value; and determining the shale oil geological engineering comprehensive sweet spot evaluation result corresponding to the target area based on the geological engineering comprehensive sweet spot factor and the normalized target sweet spot evaluation parameter value.

[0073] Specifically, because different target sweet spot evaluation parameters (such as porosity, compressibility, and organic matter abundance) have different dimensions and magnitudes, direct comparison and weighted calculation can lead to distorted results. Therefore, it is necessary to normalize these parameter values ​​to eliminate the influence of dimensions and magnitudes, obtaining normalized target sweet spot evaluation parameter values. Substituting the normalized target sweet spot evaluation parameter values ​​into the comprehensive geological engineering sweet spot factor, the comprehensive geological engineering sweet spot evaluation result for the target area is calculated. Normalization eliminates the differences in dimensions and magnitudes between different parameters, making the evaluation results more accurate and reliable.

[0074] For example, normalization is performed on five key indicators of shale: lithofacies fraction, TOC content, lithofacies thickness, porosity, and brittleness index. Normalization refers to converting the original parameter values ​​into dimensionless numerical values ​​through specific mathematical transformations, typically mapping them to the interval [0,1]. Normalized parameter values ​​not only retain the variation trends and relative magnitudes of the original data but also make different parameters comparable. The specific formula is as follows:

[0075]

[0076] In the formula, x represents the original values ​​of indicators such as TOC, lithofacies thickness, porosity, and brittleness index. max and x min These represent the maximum and minimum values ​​of each evaluation indicator, x. norm These are the normalized parameter values.

[0077] The Brittleness Index (BI) is a crucial parameter for evaluating shale oil extraction potential. Its value significantly influences the ease of fracturing and the direction of fracture propagation. The brittleness index of formation rocks is closely linked to the reservoir structure and seepage channel characteristics within shale and mudstone, directly affecting shale oil extraction efficiency. The brittleness index quantifies the brittleness of the target formation in the study area and can be calculated using the following formula:

[0078]

[0079] Based on the evaluation indicators for sweet spots in shale oil from different basins, TOC content and brittleness index are prioritized as parameters, with weights set at 30% and 25%, respectively. Secondly, shale lithofacies type, thickness, and physical properties have a significant impact on shale oil extraction; therefore, the weights for shale lithofacies fraction, lithofacies thickness, and porosity are set at 20%, 15%, and 10%, respectively. Finally, the comprehensive sweet spot index (SI) is calculated to comprehensively consider the weights of different geological factors. The formula is shown below:

[0080] SI = TOC × 30% + Brittleness Index × 25% + Lithofacies Fraction × 20% + Porosity × 15% + Lithofacies Thickness × 10%

[0081] The technical solution of this invention, by determining the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area based on the original logging data and core data of a single well in the target area, helps to more precisely characterize geological features. Based on the shale oil geological engineering evaluation logging interpretation results, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are identified. These key parameters include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness. Based on the relationship between the shale oil geological engineering evaluation logging interpretation results and the logging data curves at different depth intervals in the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals in the target area are determined. This quantifies geological features and provides specific indicators for sweet spot evaluation. Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores for different depth intervals in the target area are determined, which helps to more intuitively evaluate the production potential of different lithofacies types. Based on a three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established to comprehensively evaluate the production potential of the target exploration area. Based on the comprehensive geological engineering sweet spot factor and the target sweet spot evaluation parameter values, the comprehensive geological engineering sweet spot evaluation results for shale oil are determined. By conducting a comprehensive sweet spot evaluation of the target area, the complexity of shale oil reservoirs can be adapted, the correlation between various parameters can be improved, the detailed evaluation of shale oil sweet spots can be ensured, and the accuracy of selecting favorable target areas for shale oil exploration and development can be improved.

[0082] For example, a fully connected neural network can be constructed based on well logging interpretation data for shale oil geological engineering evaluation and key parameters for shale oil evaluation to connect well logging data curves and mineral content at different depth intervals in the target exploration area. The fully connected neural network can then be optimized and iterated based on a preset loss function and a preset optimization algorithm to determine the nonlinear relationship between well logging data curves and mineral content at different depth intervals in the target exploration area.

[0083] The process of constructing a fully connected neural network described above can be summarized as follows:

[0084] S1: A nonlinear relationship between well logging curves and mineral content is established using a fully connected neural network, such as... Figure 3 As shown.

[0085] S2: Using supervised learning, an artificial neural network group is established on the basis of a fully connected neural network to achieve the identification and prediction of mineral content in shale reservoirs.

[0086] S3: First, preprocess the core and well logging data. Then, divide all data into 5 groups for cross-validation. During each training iteration, use 4 groups as training samples and 1 group as the validation sample. Cross-validation is a statistical method or resampling procedure used to evaluate the performance of a machine learning model on a limited dataset. The basic idea is to train the model on the training samples and then evaluate its performance on a validation sample independent of the training samples. To reduce the randomness caused by data partitioning, cross-validation repeats this process multiple times, each time using a different subset of data as training and validation samples. The general steps of cross-validation are as follows:

[0087] ① Dataset partitioning: The original dataset is randomly divided into K subsets. Usually, one subset is used as the validation set, and the remaining K-1 subsets are used as the training set.

[0088] ② Model training and evaluation: Train the model on K-1 training sets, and then evaluate the model performance on the reserved validation set to obtain a model performance index;

[0089] ③ Cross-validation iteration: Repeat the above steps K times, each time using a different validation set, to obtain K performance metrics;

[0090] ④ Performance Evaluation: Calculate the average of K performance indicators as the final performance evaluation index of the model. The mean squared error and accuracy are usually used to evaluate the performance of the model.

[0091] Example 2

[0092] Figure 4 This is a flowchart of a shale oil geological engineering sweet spot evaluation method based on geological modeling, provided in Embodiment 2 of the present invention. This embodiment optimizes the step "determining the shale oil lithofacies classification scheme based on the well logging interpretation results of shale oil geological engineering evaluation" based on the above embodiments. Explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0093] See Figure 4 The alternative method for evaluating the geological sweet spot of shale oil based on geological modeling provided in this embodiment specifically includes the following steps:

[0094] S210. Based on the original logging data and original core data of single wells in the target area, determine the logging interpretation results for shale oil geological engineering evaluation corresponding to different depth intervals in the target area.

[0095] S220. Based on the well logging interpretation results of shale oil geological engineering evaluation, the mineral composition test data and organic matter abundance test data are sorted and analyzed to determine the correlation analysis results between different mineral compositions and organic matter abundance.

[0096] Among these, mineral composition test data refers to data obtained through a series of laboratory tests or well logging techniques, reflecting the composition and content of various minerals in shale reservoirs. Organic matter abundance test data refers to data on the organic matter content in shale reservoirs measured using specific methods. Correlation analysis results refer to the results of statistical analysis (such as correlation coefficients, regression analysis, etc.) of mineral composition test data and organic matter abundance test data, revealing the intrinsic relationship between them.

[0097] Specifically, based on the well logging interpretation results of shale oil geological engineering evaluation, mineral composition test data and organic matter abundance test data are determined. Statistical analysis methods (such as correlation coefficients and regression analysis) are used to conduct correlation analysis on the mineral composition test data and organic matter abundance test data to identify mineral components that are significantly correlated with organic matter abundance. The strength and direction of their correlation are analyzed to obtain the correlation analysis results between different mineral components and organic matter abundance, providing a reliable basis for the subsequent formulation of shale oil lithofacies classification schemes.

[0098] S230. Based on the correlation analysis results, determine the lithofacies types corresponding to different mineral content ranges and organic matter abundance ranges, obtain the lithofacies classification scheme for shale oil, and determine the key parameters for evaluating the sweet spot of shale oil.

[0099] Specifically, based on the correlation analysis results, mineral content ranges and organic matter abundance ranges are set as standards for lithofacies classification. Well logging interpretation results are compared with the set standards to determine the lithofacies type of each layer. The characteristics of different lithofacies types (such as mineral composition and organic matter abundance) are comprehensively analyzed to form a shale oil lithofacies classification scheme. This will enable more accurate identification of different lithofacies types in shale oil reservoirs and provide a reliable basis for subsequent exploration and development.

[0100] S240. Based on the well logging interpretation results of shale oil geological engineering evaluation and the relationship between well logging data curves at different depth intervals in the target area and key parameters for shale oil sweet spot evaluation, determine the key parameter values ​​for shale oil sweet spot evaluation corresponding to different depth intervals in the target area.

[0101] S250. Based on the shale oil facies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, determine the facies types and corresponding facies scores for different depth ranges in the target area.

[0102] S260. Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, determine the weights corresponding to the target sweet spot evaluation parameters, and establish a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling.

[0103] S270. Based on the comprehensive sweet spot factor of geological engineering and the target sweet spot evaluation parameter value, determine the comprehensive sweet spot evaluation result of shale oil geological engineering.

[0104] The technical solution of this invention, based on well logging interpretation results from shale oil geological engineering evaluation, organizes and analyzes mineral composition test data and organic matter abundance test data to determine the correlation analysis results between different mineral compositions and organic matter abundance. Based on the correlation analysis results, the lithofacies types corresponding to different mineral content ranges and organic matter abundance ranges are determined, resulting in a shale oil lithofacies classification scheme that can accurately identify shale oil reservoirs and their lithofacies types. Through refined classification and evaluation of shale oil lithofacies types, the development potential and risks of shale oil resources can be evaluated more accurately, helping to reduce uncertainties during development and improve economic efficiency.

[0105] Example 3

[0106] Figure 5 This is a schematic diagram of a shale oil geological engineering sweet spot evaluation device based on geological modeling, provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes: an interpretation result determination module 310, a classification scheme determination module 320, a parameter value determination module 330, a lithofacies fraction determination module 340, a sweet spot factor determination module 350, and an evaluation result determination module 360.

[0107] Among them, the interpretation result determination module 310 is used to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area based on the original logging data and original core data of a single well in the target area.

[0108] The classification scheme determination module 320 is used to determine the shale oil lithofacies classification scheme based on the well logging interpretation results of the shale oil geological engineering evaluation, and to determine the key parameters for shale oil sweet spot evaluation. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0109] The parameter value determination module 330 is used to determine the key parameter values ​​for shale oil sweet spot evaluation corresponding to different depth ranges in the target area based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth ranges in the target area and the key parameters for shale oil sweet spot evaluation.

[0110] The lithofacies score determination module 340 is used to determine the lithofacies type and the lithofacies score corresponding to the lithofacies type in different depth intervals of the target area based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation.

[0111] The sweet spot factor determination module 350 is used to determine the weights corresponding to the target sweet spot evaluation parameters based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, and to establish a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data and seismic data.

[0112] The evaluation result determination module 360 ​​is used to determine the comprehensive sweet spot evaluation result of shale oil geological engineering based on the comprehensive sweet spot factor of geological engineering and the target sweet spot evaluation parameter value.

[0113] The technical solution of this embodiment, by using original well logging data and original core data from a single well in the target area, determines the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area, which helps to more precisely characterize geological features. Based on the shale oil geological engineering evaluation logging interpretation results, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are identified. These key parameters include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness. Based on the shale oil geological engineering evaluation logging interpretation results and the relationship between the logging data curves at different depth intervals in the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals in the target area are determined, which can quantify geological features and provide specific indicators for sweet spot evaluation. Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores at different depth intervals in the target area are determined, which helps to more intuitively evaluate the production potential of different lithofacies types. Based on a three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established to comprehensively evaluate the production potential of the target exploration area. Based on the comprehensive geological engineering sweet spot factor and the target sweet spot evaluation parameter values, the comprehensive geological engineering sweet spot evaluation results for shale oil are determined. By conducting a comprehensive sweet spot evaluation of the target area, the complexity of shale oil reservoirs can be adapted, the correlation between various parameters can be improved, the detailed evaluation of shale oil sweet spots can be ensured, and the accuracy of selecting favorable target areas for shale oil exploration and development can be improved.

[0114] Optionally, the interpretation result determination module 310 is specifically used to: preprocess the original logging data and original core data of a single well in the target area to obtain preprocessed original logging data and original core data; perform depth repositioning on the preprocessed original logging data and original core data to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area.

[0115] Optionally, the classification scheme determination module 320 is specifically used for: organizing and analyzing mineral composition test data and organic matter abundance test data based on the well logging interpretation results of the shale oil geological engineering evaluation, determining the correlation analysis results between different mineral compositions and organic matter abundance; and determining the lithofacies types corresponding to different mineral content ranges and organic matter abundance ranges based on the correlation analysis results, thereby obtaining a shale oil lithofacies classification scheme.

[0116] Optionally, the lithofacies score determination module 340 is specifically used to: determine the lithofacies types corresponding to different depth ranges of the target area based on the shale oil lithofacies classification scheme, mineral content, and organic matter abundance; and determine the lithofacies scores corresponding to different shale lithofacies types in the target area based on the lithofacies types, oil-bearing properties, and mobility.

[0117] Optionally, the sweet spot factor determination module 350 is specifically used for: determining target sweet spot evaluation parameters related to the evaluation of sweet spot areas based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types, wherein the target sweet spot evaluation parameters include: porosity, compressibility, organic matter abundance, lithofacies thickness, and lithofacies fraction; determining the weights corresponding to the target sweet spot evaluation parameters in different depth ranges of the target area based on the target sweet spot evaluation parameters and actual drilling production data; and determining the comprehensive geological engineering sweet spot factor based on the weights corresponding to the target sweet spot evaluation parameters.

[0118] For example, the sweet spot factor determination module 350 is specifically used to: construct an original three-dimensional geological model based on lithofacies type, well logging data and seismic data; and map the key parameter values ​​for shale oil sweet spot evaluation and the lithofacies score to the original three-dimensional geological model to construct a three-dimensional model of shale oil sweet spot evaluation attributes.

[0119] For example, the evaluation result determination module 360 ​​is specifically used to: normalize the target sweet spot evaluation parameter value to obtain the normalized target sweet spot evaluation parameter value; and determine the shale oil geological engineering comprehensive sweet spot evaluation result corresponding to the target area based on the geological engineering comprehensive sweet spot factor and the normalized target sweet spot evaluation parameter value.

[0120] The shale oil geological engineering sweet spot evaluation device based on geological modeling provided in this embodiment of the invention can execute the shale oil geological engineering sweet spot evaluation method based on geological modeling provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0121] Figure 6A schematic diagram of an electronic device 12 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as desktop computers, workbenches, servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0122] like Figure 6 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0123] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0124] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0125] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 6 Not shown; usually referred to as a "hard drive"). Although Figure 6Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0126] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0127] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0128] Processing unit 16 executes various functional applications and data processing by running programs stored in system memory 28, such as implementing the steps of a shale oil geological engineering sweet spot evaluation method based on geological modeling provided in this embodiment, the method including:

[0129] Based on the original logging data and core data of single wells in the target area, determine the logging interpretation results of shale oil geological engineering evaluation corresponding to different depth intervals in the target area;

[0130] Based on the well logging interpretation results of the shale oil geological engineering evaluation, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are determined. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0131] Based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth intervals of the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals of the target area are determined.

[0132] Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores for different depth ranges in the target area are determined.

[0133] Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data, and seismic data.

[0134] Based on the comprehensive sweet spot factor and target sweet spot evaluation parameter values ​​of the geological engineering, the comprehensive sweet spot evaluation result of shale oil geological engineering is determined.

[0135] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the shale oil geological engineering sweet spot evaluation method based on geological modeling provided in any embodiment of the present invention.

[0136] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the steps of the shale oil geological engineering sweet spot evaluation method based on geological modeling provided in any embodiment of the present invention. The method includes:

[0137] Based on the original logging data and core data of single wells in the target area, determine the logging interpretation results of shale oil geological engineering evaluation corresponding to different depth intervals in the target area;

[0138] Based on the well logging interpretation results of the shale oil geological engineering evaluation, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are determined. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness.

[0139] Based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth intervals of the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals of the target area are determined.

[0140] Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores for different depth ranges in the target area are determined.

[0141] Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data, and seismic data.

[0142] Based on the comprehensive sweet spot factor and target sweet spot evaluation parameter values ​​of the geological engineering, the comprehensive sweet spot evaluation result of shale oil geological engineering is determined.

[0143] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0144] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0145] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0146] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0147] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0148] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for evaluating the sweet spot in shale oil geological engineering based on geological modeling, characterized in that, include: Based on the original logging data and core data of single wells in the target area, determine the logging interpretation results of shale oil geological engineering evaluation corresponding to different depth intervals in the target area; Based on the well logging interpretation results of the shale oil geological engineering evaluation, a shale oil lithofacies classification scheme is determined, and key parameters for shale oil sweet spot evaluation are determined. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness. Based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth intervals of the target area and the key parameters for shale oil sweet spot evaluation, the values ​​of the key parameters for shale oil sweet spot evaluation corresponding to different depth intervals of the target area are determined. Based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation, the lithofacies types and corresponding lithofacies scores for different depth ranges in the target area are determined. Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters are determined, and a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling is established. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data, and seismic data. Based on the comprehensive sweet spot factor and target sweet spot evaluation parameter values ​​of the geological engineering, the comprehensive sweet spot evaluation result of shale oil geological engineering is determined.

2. The method according to claim 1, characterized in that, The well logging interpretation results for shale oil geological engineering evaluation, based on the original well logging data and original core data of the target area, for different depth intervals in the target area, include: Preprocess the raw logging data and raw core data of the single well in the target area to obtain preprocessed raw logging data and raw core data; The preprocessed raw logging data and raw core data are repositioned to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area.

3. The method according to claim 1, characterized in that, The determination of the shale oil lithofacies classification scheme based on the well logging interpretation results of the shale oil geological engineering evaluation includes: Based on the well logging interpretation results of the shale oil geological engineering evaluation, the mineral composition test data and organic matter abundance test data were sorted and analyzed to determine the correlation analysis results between different mineral compositions and organic matter abundance. Based on the correlation analysis results, the lithofacies types corresponding to different mineral content ranges and organic matter abundance ranges are determined, and a shale oil lithofacies classification scheme is obtained.

4. The method according to claim 1, characterized in that, The determination of the lithofacies types and lithofacies scores corresponding to different depth intervals in the target area based on the shale oil lithofacies classification scheme and key parameter values ​​for shale oil sweet spot evaluation includes: Based on the shale oil lithofacies classification scheme, mineral content, and organic matter abundance, the lithofacies types corresponding to different depth ranges in the target area are determined; Based on the lithofacies type, oil content, and mobility, the lithofacies scores corresponding to different shale lithofacies types in the target area are determined.

5. The method according to claim 1, characterized in that, The three-dimensional model of shale oil sweet spot evaluation attributes based on different lithofacies types and actual drilling production data determines the weights corresponding to the target sweet spot evaluation parameters and establishes a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling, including: Based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types, target sweet spot evaluation parameters related to the evaluation of sweet spot areas are determined. The target sweet spot evaluation parameters include porosity, compressibility, organic matter abundance, lithofacies thickness, and lithofacies fraction. Based on the target sweet spot evaluation parameters and actual drilling production data, the weights corresponding to the target sweet spot evaluation parameters in different depth ranges of the target area are determined. Based on the weights corresponding to the target dessert evaluation parameters, the comprehensive dessert factor for geological engineering is determined.

6. The method according to claim 1, characterized in that, Based on the lithofacies type, the key parameter values ​​for shale oil sweetness evaluation, the lithofacies score, well logging data, and seismic data, a three-dimensional model of shale oil sweetness evaluation attributes is constructed, including: Based on lithofacies type, well logging data, and seismic data, an original three-dimensional geological model was constructed. The key parameter values ​​for shale oil sweetness evaluation and the lithofacies score are mapped to the original three-dimensional geological model to construct a three-dimensional model of shale oil sweetness evaluation attributes.

7. The method according to claim 1, characterized in that, The determination of the comprehensive shale oil geological engineering sweetness evaluation result based on the comprehensive sweetness factor and the target sweetness evaluation parameter value includes: The evaluation parameter values ​​of the target dessert are normalized to obtain the normalized evaluation parameter values ​​of the target dessert. Based on the comprehensive sweet spot factor of the geological engineering and the normalized target sweet spot evaluation parameter value, the comprehensive sweet spot evaluation result of the shale oil geological engineering corresponding to the target area is determined.

8. A shale oil geological engineering sweet spot evaluation device based on geological modeling, characterized in that, include: The interpretation result determination module is used to determine the shale oil geological engineering evaluation logging interpretation results corresponding to different depth intervals in the target area based on the original logging data and original core data of a single well in the target area. The classification scheme determination module is used to determine the shale oil lithofacies classification scheme based on the well logging interpretation results of the shale oil geological engineering evaluation, and to determine the key parameters for shale oil sweet spot evaluation. The key parameters for shale oil sweet spot evaluation include porosity, oil content, mobility, compressibility, organic matter abundance, mineral content, and lithofacies thickness. The parameter value determination module is used to determine the key parameter values ​​for shale oil sweet spot evaluation corresponding to different depth ranges in the target area based on the well logging interpretation results of the shale oil geological engineering evaluation and the relationship between the well logging data curves at different depth ranges in the target area and the key parameters for shale oil sweet spot evaluation. The lithofacies score determination module is used to determine the lithofacies type and the corresponding lithofacies score of the target area at different depth intervals based on the shale oil lithofacies classification scheme and the key parameter values ​​for shale oil sweet spot evaluation. The sweet spot factor determination module is used to determine the weights corresponding to the target sweet spot evaluation parameters based on the three-dimensional model of shale oil sweet spot evaluation attributes corresponding to different lithofacies types and actual drilling production data, and to establish a comprehensive geological engineering sweet spot factor based on shale oil lithofacies modeling. The three-dimensional model of shale oil sweet spot evaluation attributes is constructed based on the lithofacies type, the key parameter values ​​of shale oil sweet spot evaluation, the lithofacies score, well logging data and seismic data. The evaluation result determination module is used to determine the comprehensive sweet spot evaluation result of shale oil geological engineering based on the comprehensive sweet spot factor of geological engineering and the target sweet spot evaluation parameter value.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the shale oil geological engineering sweet spot evaluation method based on geological modeling as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for evaluating shale oil geological engineering sweet spots based on geological modeling, as described in any one of claims 1-7.

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