Oil and gas resource prospective area evaluation method, electronic device, and storage medium

Abstract

The application provides an oil and gas resource prospective area evaluation method, an electronic device and a storage medium. The method comprises the following steps: obtaining exploration data of a region to be evaluated; determining an oil and gas geological condition score set and an oil and gas resource value set according to the exploration data; determining a target weight group according to scene characteristics, wherein the target weight group comprises weights corresponding to each score in the oil and gas geological condition score set and the oil and gas resource value set; determining a reliability coefficient and a potential coefficient corresponding to the region to be evaluated according to the oil and gas geological condition score set, the oil and gas resource value set and the target weight group; and determining a prospective area evaluation result of the region to be evaluated according to the reliability coefficient, the potential coefficient and a current coefficient evaluation chart. The application quantifies exploration data into geological condition related scores and oil and gas resource value related scores through multiple dimensions, dynamically matches weights and accurately classifies, reduces subjective intervention and adapts to the characteristics of sparse early exploration data.

Description

Technical Field

[0001] This application relates to the field of resource exploration technology, and more specifically, to a method for evaluating potential oil and gas resource areas, electronic equipment, and storage medium. Background Technology

[0002] The evaluation of onshore oil and gas resource prospective areas is the first step in oil and gas resource surveys. It aims to quickly identify large-scale target areas with potential for reservoir formation and resource value in sedimentary basins with low exploration levels and scarce data, so as to provide direction for subsequent work deployment.

[0003] Existing methods for evaluating potential scenic areas include qualitative analogy based on expert experience, multi-factor condition analysis based on hydrocarbon accumulation geological conditions, and simple product algorithms of source rock conditions × preservation conditions × matching conditions.

[0004] However, existing evaluation methods for prospective exploration areas have the following problems: First, the evaluation dimensions are fragmented, focusing only on hydrocarbon accumulation geological conditions while ignoring value attributes. This leads to the misselection of targets with high geological adaptability but low resource value, or the omission of global targets with outstanding resource endowment but high geological risks. Second, existing quantitative methods are mostly geared towards mid-to-late stage exploration zone evaluation, with high parameter requirements, making it difficult to adapt to the problem of sparse and uncertain data in the prospective exploration area stage. Summary of the Invention

[0005] The purpose of this application is to provide a method, electronic device, and storage medium for evaluating potential oil and gas resource areas, addressing the shortcomings of the prior art, in order to solve the problems of fragmented evaluation dimensions and difficulty in adapting to the sparse and uncertain data at the potential resource stage in the prior art.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0007] Firstly, this application provides a method for evaluating potential oil and gas resource areas, the method comprising:

[0008] Acquire exploration data for the area to be evaluated. The exploration data includes: source rock data, reservoir data, caprock data, regional type, and scene characteristics. The regional type includes: type breakthrough subtype, regional driving subtype, and compliance subtype. The scene characteristics include: integrity label of collected data, geological understanding maturity label, and work status label.

[0009] Based on the exploration data, a set of oil and gas geological condition scores and a set of oil and gas resource values ​​are determined. The set of oil and gas geological condition scores includes: hydrocarbon generation condition score, reservoir condition score, and caprock condition score. The set of oil and gas resource values ​​includes: resource scale score, resource abundance score, and value score.

[0010] Based on the scenario characteristics, a target weight reorganization is determined, which includes the oil and gas geological condition score set and the weights corresponding to each score in the oil and gas resource value set.

[0011] Based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weighting, determine the reliability coefficient and potential coefficient corresponding to the area to be evaluated;

[0012] Based on the reliability coefficient, the potential coefficient, and the current coefficient evaluation chart, the evaluation result of the prospective scenic area of ​​the area to be evaluated is determined. The current coefficient evaluation chart includes: priority area, key tracking area, and observation area.

[0013] Optionally, determining the oil and gas geological condition score set and the oil and gas resource value set based on the exploration data includes:

[0014] Based on the preset hydrocarbon generation condition quantification model and the source rock data, the hydrocarbon generation condition score in the oil and gas geological condition score set is determined.

[0015] Based on the preset reservoir condition quantification model and the reservoir data, the reservoir condition score in the oil and gas geological condition score set is determined.

[0016] Based on the preset caprock condition quantification model and the caprock data, the caprock condition score in the oil and gas geological condition score set is determined.

[0017] Based on the preset value evaluation parameter mapping table and the exploration data, the resource scale score, resource abundance score and value score in the oil and gas resource value set are determined respectively.

[0018] Optionally, the determination of the resource size score, resource abundance score, and value score in the oil and gas resource value set based on the preset value evaluation parameter mapping table and the exploration data includes:

[0019] Calculate the oil and gas resources based on the exploration data;

[0020] The resource scale fraction is determined based on the amount of oil and gas resources.

[0021] Based on the oil and gas resource quantity and the caprock area in the caprock data, the resource abundance is determined, and based on the resource abundance, the resource abundance score is determined.

[0022] The value score is determined based on the region type.

[0023] Optionally, determining the target weight reorganization based on scene characteristics includes:

[0024] If the integrity label of the collected data in the scenario features indicates that the data is incomplete, and the geological knowledge maturity label indicates that the knowledge is immature, and the work status label indicates that the work is currently in the early stage of the investigation, then the target weight reorganization is determined to be the first weight reorganization;

[0025] If the integrity label of the collected data in the scene features indicates that the data is incomplete, and the geological cognitive maturity label indicates cognitive maturity, then the target weighting reorganization is determined to be the second weighting reorganization.

[0026] If the integrity label of the collected data in the scenario features indicates that the data is complete, and the working status label indicates that the current working period is the breakthrough verification period, then the target weight reassembly is determined to be a third weight reassembly.

[0027] In the first weighted reorganization, the weight corresponding to the hydrocarbon generation condition score is greater than the weight corresponding to the hydrocarbon generation condition score in the second weighted reorganization; the weight corresponding to the hydrocarbon generation condition score in the second weighted reorganization is greater than the weight corresponding to the hydrocarbon generation condition score in the third weighted reorganization; the weight corresponding to the resource scale score in the first weighted reorganization is greater than the weight corresponding to the resource scale score in the second weighted reorganization; and the weight corresponding to the resource scale score in the second weighted reorganization is greater than the weight corresponding to the resource scale score in the third weighted reorganization.

[0028] Optionally, determining the reliability coefficient and potential coefficient corresponding to the area to be evaluated based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weighting reorganization includes:

[0029] Based on formula The reliability coefficient is calculated, where R is the reliability coefficient, α is a first preset coefficient, and β is a second preset coefficient. It refers to the weight corresponding to the hydrocarbon generation condition score in the target weight reorganization. These are the weights corresponding to the reservoir condition scores in the target weighting reorganization. These are the weights corresponding to the capping condition scores in the target weight reorganization. It is the conditional fraction for hydrocarbon generation. It is the reservoir condition score. It is the capping condition score, where the sum of the first preset coefficient and the second preset coefficient is 1;

[0030] Based on formula The potential coefficient is calculated, where P is the potential coefficient. It is the third preset coefficient. It is the fourth preset coefficient. It is the weight corresponding to the resource scale score in the target weight restructuring. These are the weights corresponding to the resource abundance scores in the target weight reorganization. It refers to the weight corresponding to the value score in the target weight restructuring. It is a resource scale score. It is a resource abundance score. It is a value score, and the sum of the third preset coefficient and the fourth preset coefficient is 1.

[0031] Optionally, the current coefficient evaluation chart is generated through the following process:

[0032] Determine whether there are any new samples. If so, generate a distance curve based on the new samples and multiple historical samples. The new samples and each historical sample include: reliability coefficient, potential coefficient and drilling confirmation data, respectively.

[0033] The distance corresponding to the curvature inflection point of the distance curve is selected as the neighborhood radius. Based on the neighborhood radius and the minimum number of samples, the newly added samples and multiple historical samples are clustered to obtain an initial evaluation chart. The minimum number of samples is determined based on the number of historical samples.

[0034] Based on the drilling confirmation data of the newly added samples and the drilling confirmation data of multiple historical samples, the convex hull boundary of the initial evaluation chart is expanded or contracted to obtain the current coefficient evaluation chart.

[0035] Optionally, the step of clustering the new samples and multiple historical samples based on the neighborhood radius and the minimum number of samples to obtain an initial evaluation chart includes:

[0036] The reliability coefficients and potential coefficients in the newly added samples, as well as the reliability coefficients and potential coefficients in each historical sample, are integrated into a two-dimensional point set;

[0037] Cluster the two-dimensional point set using the neighborhood radius as a distance threshold and the minimum number of samples as a density threshold to determine multiple core points and multiple noise points;

[0038] A connected region consisting of multiple core points is treated as a cluster, resulting in multiple clusters. Noise points outside the connected regions are then removed.

[0039] Calculate the mean value of each cluster, and determine the region type of each cluster based on the first preset map threshold, the second preset map threshold, and the mean value of each cluster;

[0040] An initial evaluation chart is generated based on the region type of each cluster.

[0041] Optionally, determining the prospective scenic area evaluation result of the area to be evaluated based on the reliability coefficient, the potential coefficient, and the current coefficient evaluation chart includes:

[0042] Determine the target regions where the reliability coefficient and the potential coefficient are located in the current coefficient evaluation chart;

[0043] The type of the target area is used as the evaluation result of the prospective area of ​​the area to be evaluated.

[0044] Secondly, this application provides an oil and gas resource prospect assessment device, the device comprising:

[0045] The acquisition module is used to acquire exploration data of the area to be evaluated. The exploration data includes: source rock data, reservoir data, caprock data, regional type and scene characteristics. The regional type includes: type breakthrough subtype, regional driving subtype and compliance subtype. The scene characteristics include: integrity label of collected data, geological understanding maturity label and working status label.

[0046] The first determining module is used to determine the oil and gas geological condition score set and the oil and gas resource value set based on the exploration data. The oil and gas geological condition score set includes: hydrocarbon generation condition score, reservoir condition score and caprock condition score. The oil and gas resource value set includes: resource scale score, resource abundance score and value score.

[0047] The second determining module is used to determine the target weight reorganization based on the scene characteristics. The target weight reorganization includes the oil and gas geological condition score set and the weights corresponding to each score in the oil and gas resource value set.

[0048] The third determining module is used to determine the reliability coefficient and potential coefficient of the area to be evaluated based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weighting set.

[0049] The evaluation module is used to determine the evaluation result of the prospective area of ​​the area to be evaluated based on the reliability coefficient, the potential coefficient, and the current coefficient evaluation chart. The current coefficient evaluation chart includes: priority area, key tracking area, and observation area.

[0050] Thirdly, this application provides an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the oil and gas resource prospective area evaluation method described above.

[0051] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described oil and gas resource prospective area evaluation method.

[0052] The beneficial effects of this application are as follows: It obtains exploration data for the area to be evaluated; based on the exploration data, it determines the set of oil and gas geological condition scores and the set of oil and gas resource values; based on scene characteristics, it determines the target weight reorganization; based on the set of oil and gas geological condition scores, the set of oil and gas resource values, and the target weight reorganization, it determines the reliability coefficient and potential coefficient corresponding to the area to be evaluated; based on the reliability coefficient, potential coefficient, and current coefficient evaluation map, it determines the evaluation result of the prospective area of ​​the area to be evaluated. This application quantifies exploration data into geological condition-related scores and oil and gas resource value-related scores from multiple dimensions, solving the problem of abstractness and difficulty in guiding exploration deployment in traditional evaluation methods. Furthermore, based on scene characteristics matching weight reorganization, it strengthens the weight of core basic indicators in the prospective area stage where data is sparse and understanding is immature, weakens the influence of highly uncertain indicators, and adapts to the evaluation needs of the early stages of exploration. In addition, this application achieves automated classification of areas by dividing them according to exploration priority, reducing subjective human intervention and improving the objectivity and accuracy of evaluation results. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a method for evaluating potential oil and gas resource areas provided in an embodiment of this application;

[0055] Figure 2 This is a schematic diagram of a current coefficient evaluation chart provided in an embodiment of this application;

[0056] Figure 3 This is a schematic diagram of a process for determining a set of oil and gas geological conditions and a set of oil and gas resource values, provided in an embodiment of this application.

[0057] Figure 4 This is a schematic diagram of a process for determining resource size score, resource abundance score, and value score, provided in an embodiment of this application.

[0058] Figure 5 This is a schematic diagram of a process for determining a target weight group provided in an embodiment of this application;

[0059] Figure 6 This is a schematic diagram of a process for generating a current coefficient evaluation chart, provided in an embodiment of this application;

[0060] Figure 7 This is a schematic diagram of a process for obtaining an initial evaluation plate provided in an embodiment of this application;

[0061] Figure 8 This is a schematic diagram of the structure of an oil and gas resource prospect evaluation device provided in an embodiment of this application;

[0062] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0064] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0065] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0066] Existing methods for evaluating prospective oil and gas resource areas suffer from fragmented evaluation dimensions and difficulty in adapting to the constraints of sparse and highly uncertain data at different stages. Therefore, this application proposes a new method for evaluating prospective oil and gas resource areas. This method acquires exploration data, including source rock data, reservoir data, caprock data, regional type data, and scene characteristics. Based on this data, it determines a set of oil and gas geological condition scores (containing hydrocarbon generation, reservoir, and caprock scores) and a set of oil and gas resource value scores (containing resource scale, abundance, and value scores). It then matches target weights covering each score based on scene characteristics. Combining the score set and weights, it calculates reliability and potential coefficients. Finally, it determines the evaluation result based on the coefficients and a current coefficient evaluation map containing priority, key tracking, and observation areas. This method achieves quantitative evaluation across both geological and value dimensions, dynamically matches weights, and accurately classifies prospective areas through coefficients and dynamic maps, reducing subjective intervention and improving the objectivity and operability of the evaluation.

[0067] Figure 1 This is a flowchart illustrating a method for evaluating potential oil and gas resource areas provided in an embodiment of this application. The following is based on... Figure 1 This method will be described in detail.

[0068] S101. Obtain exploration data for the area to be evaluated. The exploration data includes: source rock data, reservoir data, caprock data, regional type and scene characteristics. Regional type includes: type breakthrough subtype, regional driving subtype and compliance subtype. Scene characteristics include: integrity label of collected data, geological understanding maturity label and work status label.

[0069] Exploration data refers to multi-source heterogeneous basic data used for evaluating potential oil and gas resources within the terrestrial sedimentary basin area to be evaluated. Source rock data characterizes the development characteristics of source rocks and may include data such as organic carbon content, vitrinite reflectance, organic matter type, effective thickness, and area. Reservoir data characterizes reservoir properties and distribution characteristics and may include data such as stratigraphy, lithology, porosity, permeability, and effective thickness. Caprock data characterizes the caprock's sealing capacity and distribution characteristics and may include data such as lithology, stratigraphy, thickness, continuity, and degree of fracture damage.

[0070] Regional types are used to quantitatively evaluate the overall significance of oil and gas resources, thereby determining resource value. Among them, the type breakthrough subtype indicates whether the area to be evaluated is a new area, a new stratigraphic system, or a new type of oil and gas reservoir; the regional driving subtype can indicate the driving effect of oil and gas discovery in the area to be evaluated on exploration in the surrounding areas; and the compliance subtype can indicate whether the area to be evaluated complies with energy security policies and whether it is located in a prohibited or restricted development zone.

[0071] Scene characteristics are used to characterize the progress of oil and gas exploration work, data accumulation, and geological understanding in the area to be evaluated. Specifically, the "Completeness of Collected Data" tag characterizes the completeness of collected data in the area, specifically the completeness of seismic, drilling, and sample analysis data. This indicator can be determined based on the number and spatial coverage density of the actual types of exploration data collected. The completeness tag can also include the specific content of the collected data. The "Geological Understanding Maturity" tag characterizes the level of research and understanding of the core geological conditions for oil and gas accumulation in the area to be evaluated. This tag can also include specific research and understanding results. This indicator can be determined based on the number of confirmed oil and gas discoveries in the area, the reliability of analogies with neighboring areas, and the measured proportion of key geological parameters. The "Work Status" tag characterizes the current stage of oil and gas exploration work in the area to be evaluated, such as the initial investigation phase or the breakthrough verification phase.

[0072] Optionally, after the raw data is collected, it can be standardized and transformed into a dataset in a unified format as exploration data.

[0073] S102. Based on the exploration data, determine the oil and gas geological condition score set and the oil and gas resource value set. The oil and gas geological condition score set includes: hydrocarbon generation condition score, reservoir condition score and caprock condition score. The oil and gas resource value set includes: resource scale score, resource abundance score and value score.

[0074] The oil and gas geological condition score set can be generated by a preset quantitative model, and the score can be in the range of [0,10]. In the oil and gas geological condition score set, the hydrocarbon generation condition score is used to quantitatively characterize the strength of hydrocarbon generation capacity of the source rocks in the area to be evaluated, the reservoir condition score is used to characterize the strength of hydrocarbon storage capacity of the reservoirs in the area to be evaluated, and the caprock condition score is used to quantitatively characterize the strength of caprock sealing capacity of the area to be evaluated.

[0075] The oil and gas resource value set can be a standardized score set generated based on exploration data through preset assignment standards. It is used to characterize the economic and global value of oil and gas resources, with scores ranging from [0, 10]. Among them, the resource scale score is used to quantitatively characterize the total potential oil and gas resources in the area to be evaluated; the resource abundance score is used to quantitatively characterize the amount of potential oil and gas resources per unit area in the area to be evaluated, reflecting the degree of resource concentration; and the value score is used to quantitatively characterize the global significance of the oil and gas resources in the area to be evaluated.

[0076] Specifically, the exploration data is mapped into scores within a preset range by using a pre-defined quantification model and assignment standard.

[0077] As one optional implementation, each score item can correspond to a quantification model. The exploration data corresponding to each score item is input into the quantification model to obtain the corresponding score. As another optional implementation, two score mapping tables can be maintained, corresponding to the set of oil and gas geological condition scores and the set of oil and gas resource values, respectively. Then, based on the exploration data of the area to be evaluated, each item in the mapping table is traversed to determine the initial interval corresponding to each score. Finally, based on the interpolation method, the specific score is determined according to the numerical values ​​in the specific exploration data.

[0078] The scoring standards for the oil and gas geological condition score set and the oil and gas resource value set need to be unified to ensure a consistent data basis for coefficient calculation.

[0079] As an optional implementation, if the data corresponding to the score is missing in the exploration data, the score can be assigned a preset value and the area to be evaluated can be marked.

[0080] S103. Based on the scenario characteristics, determine the target weight reorganization, which includes the set of oil and gas geological condition scores and the weights corresponding to each score in the set of oil and gas resource value.

[0081] The target weighting includes the weights corresponding to the hydrocarbon generation condition score, reservoir condition score, caprock condition score, resource scale score, resource abundance score, and value score.

[0082] As an optional implementation method, a target weight reorganization for all quantified scores can be obtained by matching from multiple preset weight schemes based on the scene characteristics of the area to be evaluated.

[0083] As an alternative implementation, a feedback validity index can be retrieved from the historical database of exploration data with the same scenario characteristics as the area to be evaluated. This feedback validity index can be calculated based on the actual oil and gas discovery rate of historically predicted priority areas. Then, based on the scenario characteristics and the feedback validity index, a target weighted reassembly is generated based on a preset initial weighted reassembly. Specifically, the lower the geological cognitive maturity label and the worse the completeness label of the collected data, the higher the weight corresponding to the hydrocarbon generation condition score, while the weights corresponding to the reservoir condition score and caprock condition score decrease accordingly. The working status label indicates that the area to be evaluated is currently in the breakthrough verification period, thus the higher the weight corresponding to the resource scale score, while the weights corresponding to the resource abundance score and value score decrease accordingly. If the feedback validity index is lower than the preset validity threshold, the weights corresponding to each score in the oil and gas resource value set converge towards the average value to enhance robustness. Finally, the adjusted six weights are normalized to obtain the target weighted reassembly.

[0084] S104. Based on the set of oil and gas geological conditions, the set of oil and gas resource values, and the target weighting, determine the reliability coefficient and potential coefficient of the area to be evaluated.

[0085] The reliability coefficient reflects the reliability of the geological conditions for hydrocarbon accumulation in the area to be evaluated, and the coefficient can be in the range of [0, 10]. The potential coefficient reflects the potential value of hydrocarbon resources in the area to be evaluated, and the coefficient can be in the range of [0, 10].

[0086] Optionally, the reliability coefficient and potential coefficient can be calculated based on the linear weighting formula and the geometric mean formula. The linear weighting formula reflects the weight differences of each indicator, while the geometric mean formula reflects the weakest link parameter, avoiding evaluation bias caused by simply using product or weighted summation. The geometric mean can be the product of the scores and the quotient of the number of scores.

[0087] Specifically, the reliability coefficient can be obtained by inputting each score in the oil and gas geological condition score set and its corresponding weight into a linear weighting formula, and by inputting each score in the oil and gas geological condition score set into a geometric mean formula. The sum of the results calculated by the two formulas is the reliability coefficient.

[0088] Input each score in the oil and gas resource value set and its corresponding weight into a linear weighting formula, and input each score in the oil and gas resource value set into a geometric mean formula. The sum of the results of the two formulas is the potential coefficient.

[0089] S105. Based on the reliability coefficient, potential coefficient, and current coefficient evaluation chart, determine the evaluation results of the prospective scenic area of ​​the area to be evaluated. The current coefficient evaluation chart includes: priority area, key tracking area, and observation area.

[0090] in, Figure 2 This is a schematic diagram of a current coefficient evaluation chart provided in an embodiment of this application, such as... Figure 2 As shown, the current coefficient evaluation map can be a two-dimensional dynamic evaluation map constructed with the reliability coefficient as the horizontal axis and the potential coefficient as the vertical axis. The boundary of the current coefficient evaluation map is dynamically updated as exploration samples accumulate. The priority area is the region with both high reliability and potential coefficients in the current coefficient evaluation map; this represents the optimal direction for oil and gas exploration and requires priority in exploration efforts. The key monitoring area is the region with medium reliability and potential coefficients in the current coefficient evaluation map; this represents a potential direction for oil and gas exploration and requires continuous monitoring and supplementation of exploration data. The observation area is the region with low reliability and potential coefficients in the current coefficient evaluation map; this region has poor oil and gas exploration potential and requires temporary suspension of exploration efforts.

[0091] The evaluation result of the prospective area can be the final exploration priority determination result of the area to be evaluated, which can be determined by the area type in the map based on the coordinates formed by its reliability coefficient and potential coefficient.

[0092] Specifically, the coordinates formed by the calculated reliability coefficient and potential coefficient of the area to be evaluated are projected onto the current coefficient evaluation chart to determine the target area where the coordinates are located. Then, a prospective area evaluation result for the area to be evaluated is generated based on the type of the target area. The type of the target area can be a priority area, a key tracking area, or an observation area. As an optional implementation method, exploration suggestions can be proposed based on the type of the target area, and the exploration suggestions and the type of the target area can be used as the prospective area evaluation result for the area to be evaluated.

[0093] As an optional implementation method, the evaluation method for oil and gas resource prospective areas also includes the following steps: The corresponding reliability coefficient and potential coefficient can be calculated multiple times according to the exploration stage, and a resource change trajectory line can be generated in the trajectory map. By analyzing the movement direction, speed, and crossing of classification boundaries of the trajectory line, the dynamic evolution trend of the area's potential can be determined.

[0094] In this embodiment, exploration data of the area to be evaluated is acquired. Based on the exploration data, a set of oil and gas geological condition scores and a set of oil and gas resource values ​​are determined. Target weighting is determined based on scene characteristics. Based on the set of oil and gas geological condition scores, the set of oil and gas resource values, and the target weighting, the reliability coefficient and potential coefficient corresponding to the area to be evaluated are determined. Based on the reliability coefficient, potential coefficient, and the current coefficient evaluation map, the evaluation result of the prospective area of ​​the area to be evaluated is determined. This embodiment quantifies exploration data into geological condition-related scores and oil and gas resource value-related scores from multiple dimensions, solving the problem of abstractness and difficulty in guiding exploration deployment in traditional evaluation methods. Furthermore, based on scene characteristic matching weighting, the weight of core basic indicators is strengthened for prospective areas with sparse data and immature understanding, while the influence of highly uncertain indicators is weakened, adapting to the evaluation needs of early exploration stages. In addition, this embodiment achieves automated classification of areas by dividing them according to exploration priority, reducing subjective human intervention and improving the objectivity and accuracy of the evaluation results.

[0095] Figure 3 This is a schematic diagram illustrating a process for determining a fractional set of oil and gas geological conditions and a value set of oil and gas resources, provided in an embodiment of this application. Next, refer to... Figure 3 In step S102 above, determining the fractional set of oil and gas geological conditions and the set of oil and gas resource values ​​based on exploration data includes:

[0096] S301. Based on the preset hydrocarbon generation condition quantification model and source rock data, determine the hydrocarbon generation condition score in the oil and gas geological condition score set.

[0097] The hydrocarbon generation condition quantification model incorporates threshold determination and weight superposition rules for hydrocarbon source rock parameters, which can transform basic data of hydrocarbon source rock strata into standardized hydrocarbon generation condition scores in the [0,10] interval.

[0098] Optionally, the parameter preset judgment rules in the hydrocarbon generation condition quantification model can be, for example, that the organic carbon content is greater than 1.5% and the vitrinite reflectance is in the range of 0.7-1.2% as a favorable range for hydrocarbon generation, and the weight superposition rules can be, for example, that the effective thickness of the source rock layer is greater than 50 meters and the thickness weight is superimposed.

[0099] As an optional implementation method, the source rock data that can be collected in the prospective area stage can be directly input into the preset model. The model performs threshold matching and weight superposition on the input basic data according to the built-in rules, and automatically calculates and outputs the hydrocarbon generation condition score in the range of [0,10].

[0100] As an alternative implementation, the exploration data is preprocessed before being input into the hydrocarbon generation condition quantification model. Specifically, the organic carbon content (in weight percentage) is determined by analyzing the organic carbon content of field outcrop samples, drill cores, or cuttings samples from the source rock data. The organic matter type is determined by identifying kerogen microstructures and using the Rock-Eval pyrolysis hydrogen index, classifying organic matter into Type I, Type II, and Type III. Maturity is then determined based on vitrinite reflectance. The obtained organic carbon content, organic matter type, maturity, effective thickness, and area are then input into the hydrocarbon generation condition quantification model to ultimately obtain the hydrocarbon generation condition score.

[0101] S302. Based on the preset reservoir condition quantification model and reservoir data, determine the reservoir condition score in the oil and gas geological condition score set.

[0102] The reservoir condition quantification model incorporates core reservoir parameter classification and thickness coefficient superposition rules, which can convert reservoir data into reservoir condition scores within the range of [0,10]. Parameter classification rules include, for example, a porosity of 10%-15% indicates a medium-grade reservoir, and thickness coefficient superposition rules include, for example, superimposing a positive coefficient on an effective thickness greater than 20 meters.

[0103] As an optional implementation method, the reservoir data available in the prospective area stage can be directly input into the model. The model performs hierarchical matching and thickness coefficient superposition on the basic data according to the built-in rules, and automatically calculates and outputs the reservoir condition score in the [0,10] interval.

[0104] S303. Based on the preset caprock condition quantification model and caprock data, determine the caprock condition score in the oil and gas geological condition score set.

[0105] The caprock condition quantification model incorporates rules for converting qualitative to quantitative values ​​of core caprock parameters, transforming basic caprock data into standardized caprock condition scores within the range of [0,10]. Specifically, the model includes rules for converting qualitative indicators into quantitative scores; for example, continuous mudstone thickness greater than 80 meters without fractures or damage is considered excellent, corresponding to a high score range.

[0106] As an optional implementation method, the caprock data available in the prospective area stage can be directly input into the model. The model performs qualitative judgment and quantitative scoring on the caprock data according to built-in rules, and automatically outputs the caprock condition score in the range [0,10]. The caprock data may include lithology, thickness, continuity, and degree of fracture and damage.

[0107] As an alternative implementation, the initial intervals for hydrocarbon generation condition fractions, reservoir condition fractions, and caprock condition fractions can also be directly determined based on a pre-maintained fraction mapping table, as shown in Table 1 below:

[0108] Table 1

[0109]

[0110] Then, based on the interval interpolation algorithm, the specific hydrocarbon generation condition score, reservoir condition score, and caprock condition score are determined.

[0111] S304. Based on the preset value evaluation parameter mapping table and exploration data, determine the resource scale score, resource abundance score and value score in the oil and gas resource value set, respectively.

[0112] The value evaluation parameter mapping table includes built-in interval division and score matching rules for resource scale, abundance, and value score. Based on the estimation accuracy of resource indicators in the prospective area stage, the resource scale, abundance, and value score are divided into multiple score intervals, and each interval is matched with a corresponding indicator judgment standard. For example, the resource scale of 30 million to 90 million tons of oil equivalent corresponds to the interval [5,7).

[0113] For example, the value evaluation parameter mapping table is shown in Table 2 below:

[0114] Table 2

[0115]

[0116] Specifically, the exploration data is pre-calculated to determine the amount and scale of oil and gas resources. Then, based on the amount and scale of oil and gas resources and the regional type, the resource scale score, resource abundance score, and value score are determined respectively.

[0117] In this embodiment, based on the preset hydrocarbon generation condition quantification model, reservoir condition quantification model, caprock condition quantification model, and value evaluation parameter mapping table, the oil and gas geological condition score set and oil and gas resource value set are automatically calculated according to the built-in rules, reducing human subjective intervention and adapting to the accuracy requirements of resource quantity estimation. No high-precision, multi-dimensional fine test parameters are required. In addition, the unified dimensions based on each model and mapping table increase the comparability of different regions.

[0118] Figure 4 This is a schematic diagram illustrating a process for determining resource size score, resource abundance score, and value score, provided in an embodiment of this application. The following refers to... Figure 4 The specific implementation steps of step S304 above are explained as follows:

[0119] S401. Calculate the amount of oil and gas resources based on the exploration data.

[0120] Among them, the oil and gas resources are the total potential oil and gas reserves in the area to be evaluated, which is an estimated value in the prospective area stage.

[0121] Next, we will introduce two methods for predicting oil and gas resources.

[0122] As an optional implementation method, a known oil and gas reservoir with similar geological conditions to the area to be evaluated is selected as an analogy zone. The resource quantity and area data of the analogy zone are extracted, and the oil and gas resource quantity is calculated based on the following formula (1) by combining the area of ​​the area to be evaluated and the preset geological condition correction coefficient:

[0123] (1)

[0124] The correction coefficient can be a preset value.

[0125] As another optional implementation method, the amount of oil and gas resources is calculated using the following formula (2):

[0126] (2)

[0127] The effective carbon conversion coefficient and the transport coefficient are both preset values.

[0128] S402. Determine the resource scale score based on the amount of oil and gas resources.

[0129] Specifically, the oil and gas resources are mapped to Table 2 above to obtain the initial range of resource scale scores, and the specific scores are calculated using linear interpolation.

[0130] S403. Determine the resource abundance based on the amount of oil and gas resources and the caprock area in the caprock data, and determine the resource abundance score based on the resource abundance.

[0131] Specifically, the quotient of oil and gas resources and caprock area is used as the resource abundance. Then, the resource abundance is mapped to Table 2 above to obtain the initial interval of resource abundance scores, and a linear interpolation method is used to calculate the specific scores.

[0132] S404. Determine the value score based on the area type.

[0133] Specifically, for the three sub-dimensions of the region type, standardized sub-item scoring rules are preset, and then the sub-item scoring results of the three sub-dimensions are superimposed and summed to obtain a preliminary score. Then, the superimposed preliminary score is mapped to the [0,10] interval to obtain the final value score.

[0134] Among them, the breakthrough sub-type is scored by new area, new stratum, and new type, with each item corresponding to a fixed score. The total score of this sub-dimension is calculated by summing the scores. The regional driving sub-type is scored based on the driving effect of oil and gas discovery on exploration in the surrounding area. The wider the driving range and the greater the potential, the higher the score. The compliance sub-type is scored based on whether it complies with energy security policies and whether it is located in a prohibited or restricted development zone. If it complies with the policy, it will receive bonus points; if it is located in a prohibited or restricted zone, it will receive deduction points.

[0135] In this embodiment, the value set of oil and gas resources is determined based on exploration data, thereby quantifying the resource value and achieving a comprehensive evaluation of the resource value.

[0136] Next, refer to Figure 5 The process of determining the target weight reorganization based on scene characteristics in step S103 above will be described. Figure 5 This is a schematic diagram of a process for determining a target weight group provided in an embodiment of this application.

[0137] S501. If the integrity label of the collected data in the scene features indicates that the data is incomplete, and the geological knowledge maturity label indicates that the knowledge is immature, and the work status label indicates that the work is currently in the early stage of the investigation, then the target weight reorganization is determined to be the first weight reorganization.

[0138] Specifically, the data completeness label is "incomplete," for example, only a small amount of basic data, or the data is inaccurate. The geological knowledge maturity label is "immature," for example, only a preliminary understanding of the reservoir-caprock formation conditions, and high uncertainty about the reservoir or caprock. The work status label is "early stage of investigation," for example, no systematic exploration work, only basic surveys are being conducted.

[0139] S502. If the integrity label of the collected data in the scene features indicates that the data is incomplete, and the geological cognitive maturity label indicates cognitive maturity, then the target weight reorganization is determined to be the second weight reorganization.

[0140] Among them, the integrity label of the collected data is incomplete, such as the lack of high-precision data such as dense drilling and 3D seismic data, while the geological knowledge maturity label is mature, such as the formation conditions and resource potential of source, reservoir and caprock have been systematically understood.

[0141] S503. If the integrity label of the collected data in the scene features indicates that the data is complete, and the working status label indicates that the current working period is the breakthrough verification period, then the target weight reorganization is determined to be the third weight reorganization.

[0142] Among them, the integrity label of the collected data is "complete", such as "high-precision, multi-dimensional exploration data is complete", and the working status label is "breakthrough verification period", such as "oil and gas drilling or discovery", "entering the fine evaluation stage".

[0143] In the first weighted reshuffle, the weight corresponding to the hydrocarbon generation condition score is greater than that in the second weighted reshuffle, the weight corresponding to the hydrocarbon generation condition score in the second weighted reshuffle is greater than that in the third weighted reshuffle, the weight corresponding to the resource scale score in the first weighted reshuffle is greater than that in the second weighted reshuffle, and the weight corresponding to the resource scale score in the second weighted reshuffle is greater than that in the third weighted reshuffle.

[0144] Specifically, the weight of the hydrocarbon generation condition score decreased slowly from a high proportion in the early stage of the investigation as exploration deepened and source rocks were gradually confirmed. The weight of resource scale also decreased gradually as exploration deepened and resource evaluation progressed from rough estimates to refined evaluation values.

[0145] For example, in the first weighted reorganization, the weight corresponding to the hydrocarbon generation condition score is 0.7, the weight corresponding to the reservoir condition score is 0.2, the weight corresponding to the caprock condition score is 0.1, the weight corresponding to the resource scale score is 0.6, the weight corresponding to the resource abundance score is 0.2, and the weight corresponding to the value score is 0.2.

[0146] In the second weighting, the weight corresponding to the hydrocarbon generation condition score is 0.5, the weight corresponding to the reservoir condition score is 0.3, the weight corresponding to the caprock condition score is 0.2, the weight corresponding to the resource scale score is 0.5, the weight corresponding to the resource abundance score is 0.3, and the weight corresponding to the value score is 0.2.

[0147] In the third weighting, the weight corresponding to the hydrocarbon generation condition score is 0.3, the weight corresponding to the reservoir condition score is 0.4, the weight corresponding to the caprock condition score is 0.3, the weight corresponding to the resource scale score is 0.4, the weight corresponding to the resource abundance score is 0.3, and the weight corresponding to the value score is 0.3.

[0148] In this embodiment, the target weight reorganization is determined based on the specific circumstances of the integrity label, geological knowledge maturity label, and work status label of the collected data in the scene features. This makes the target weight reorganization adapt to the exploration characteristics of the area to be evaluated, making the evaluation results more robust and reliable in the data sparse stage.

[0149] The following section introduces the specific method for determining the reliability coefficient and potential coefficient of the area to be evaluated based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weight reorganization in step S104 above.

[0150] The reliability coefficient is calculated based on formula (3):

[0151] (3)

[0152] Where R is the reliability coefficient, α is the first preset coefficient, and β is the second preset coefficient. It refers to the weight corresponding to the hydrocarbon generation condition score in the target weight reorganization. These are the weights corresponding to the reservoir condition scores in the target weighting reorganization. These are the weights corresponding to the capping condition scores in the target weight reorganization. It is the conditional fraction for hydrocarbon generation. It is the reservoir condition score. It is the capping condition score, and the sum of the first preset coefficient and the second preset coefficient is 1.

[0153] The potential coefficient is calculated based on formula (4):

[0154] (4)

[0155] Where P is the potential coefficient. It is the third preset coefficient. It is the fourth preset coefficient. It is the weight corresponding to the resource scale score in the target weight restructuring. These are the weights corresponding to the resource abundance scores in the target weight reorganization. It is the weight corresponding to the value score in the target weight restructuring. It is a resource scale score. It is a resource abundance score. It is the value score, and the sum of the third and fourth preset coefficients is 1.

[0156] Next, refer to Figure 6 The process of generating the current coefficient evaluation chart is described. Figure 6 This is a schematic diagram of a process for generating a current coefficient evaluation chart, provided in an embodiment of this application.

[0157] S601. Determine whether there are any new samples. If so, generate a distance curve based on the new samples and multiple historical samples. The new samples and each historical sample include: reliability coefficient, potential coefficient and drilling confirmation data.

[0158] As an optional implementation, when the number of historical samples exceeds a preset threshold, a current coefficient evaluation chart can be dynamically generated. Each time a new sample is added, a process is triggered to determine whether to generate a current coefficient evaluation chart. If the number of historical samples does not exceed the preset threshold, the previous current coefficient evaluation chart is used as the current coefficient evaluation chart.

[0159] The newly added samples and historical samples refer to areas where evaluation has been completed and drilling confirmation data has been obtained. Drilling confirmation data refers to the actual exploration results obtained after drilling operations in the sample area, such as oil and gas discoveries, industrial oil flows, and no oil and gas shows.

[0160] Specifically, new samples and all historical samples in the database are extracted. The reliability coefficient and potential coefficient of all integrated samples are projected onto a plane as two-dimensional coordinates. The distance from each sample to its k-th nearest neighbor is calculated, and a distance curve is generated based on all the calculation results.

[0161] S602. Select the distance corresponding to the curvature inflection point of the distance curve as the neighborhood radius. Based on the neighborhood radius and the minimum number of samples, cluster the new samples and multiple historical samples to obtain the initial evaluation chart. The minimum number of samples is determined based on the number of historical samples.

[0162] Specifically, the neighborhood radius is adaptively determined by the inflection point of the distance curve curvature. Combined with a dynamic minimum sample size generated from historical sample counts, density clustering is performed on the two-dimensional sample points to construct an initial evaluation chart. Specifically, the curvature of the distance curve is calculated, and the distance corresponding to the inflection point is selected as the neighborhood radius for clustering. The area to the left of this inflection point represents a dense sample region with small distances and a gentle curve, while the area to the right represents a sparse sample region with large distances and a steep curve. Therefore, using this radius accurately delineates the dense and discrete regions of the samples.

[0163] Optionally, the minimum sample size can be set proportionally to the total number of historical samples. The more historical samples there are, the higher the minimum sample size should be set, and vice versa.

[0164] Specifically, clustering new samples and multiple historical samples based on neighborhood radius and minimum sample size yields multiple core points, multiple boundary points, and multiple noise points. Connectivity analysis is then performed on these core points, boundary points, and noise points to remove irrelevant noise points, resulting in an initial evaluation chart.

[0165] S603. Based on the drilling confirmation data of the newly added samples and the drilling confirmation data of multiple historical samples, the convex hull boundary of the initial evaluation chart is expanded or contracted to obtain the current coefficient evaluation chart.

[0166] The convex hull boundary refers to the smallest convex polygon boundary that encloses the core point of each cluster in the initial evaluation chart.

[0167] Specifically, drilling confirmation data from all new and historical samples are extracted and categorized into oil and gas discoveries, industrial oil flows, no oil and gas indications, and non-oil and gas-bearing areas. These categories are then precisely marked on the corresponding two-dimensional coordinate positions of the initial evaluation chart. For each cluster's convex hull boundary in the initial chart, the drilling confirmation data types within a certain range around the boundary are statistically analyzed. If the area around the boundary is dominated by positive data such as oil and gas discoveries or industrial oil flows, the convex hull boundary is expanded to appropriately increase the cluster's area. If the area around the boundary is dominated by negative data such as no oil and gas indications or non-oil and gas-bearing areas, the convex hull boundary is contracted to appropriately decrease the cluster's area. If the positive and negative data around the boundary are balanced, the original convex hull boundary remains unchanged. Following these rules, the convex hull boundaries of all clusters are adjusted accordingly. After correction, each cluster is mapped to priority areas, key tracking areas, and observation areas based on the mean of the two-dimensional coordinates, ultimately forming the current coefficient evaluation chart, which is simultaneously updated to the evaluation system for subsequent classification and determination of prospective areas.

[0168] In this embodiment, the current coefficient evaluation chart is dynamically generated, so that the chart can be continuously iterated as the exploration work progresses, reflecting the latest exploration status.

[0169] Furthermore, referring to Figure 7 The process of obtaining the initial evaluation chart in step S602 above will be described. Specifically, Figure 7 This is a schematic diagram of a process for obtaining an initial evaluation plate provided in an embodiment of this application.

[0170] S701. Integrate the reliability coefficients and potential coefficients in the newly added samples, as well as the reliability coefficients and potential coefficients in each historical sample, into a two-dimensional point set.

[0171] S702. Cluster the two-dimensional point set using the neighborhood radius as the distance threshold and the minimum number of samples as the density threshold to determine multiple core points and multiple noise points.

[0172] The core points are two-dimensional points that satisfy the condition that the number of sample points within a distance threshold range is greater than or equal to a preset density threshold. Noise points are discrete points in the sample set and may be anomalous samples.

[0173] Specifically, each two-dimensional coordinate point in the two-dimensional point set is traversed and analyzed one by one. For the current two-dimensional point, a circle is drawn with the current two-dimensional point as the center and a distance threshold as the radius. The number of other sample points contained within the circle is counted. If the count is greater than or equal to the density threshold, the point is determined to be a core point. If the count is less than the density threshold, it can be further determined whether the point is a boundary point in the neighborhood of a core point. If not, it is determined to be a noise point. This process continues until all points have been traversed.

[0174] S703. Take the connected region formed by multiple core points as a cluster, obtain multiple clusters, and remove noise points outside the connected region.

[0175] Specifically, spatial connectivity analysis is performed on all marked core points. If a core point's neighborhood contains other core points, the two points are connected, and the core points involved in the associated connected regions are considered as a cluster. Each cluster contains several interconnected core points. Clusters are independent of each other and do not overlap spatially, ultimately resulting in multiple clusters that match the number of connected regions.

[0176] All noise points that are identified as noise points and are not located in the connected regions of each cluster are removed from the clustering results. These points will not be included in the subsequent cluster mean calculation and region type determination.

[0177] S704. Calculate the mean of each cluster, and determine the region type of each cluster based on the first preset map threshold, the second preset map threshold, and the mean of each cluster.

[0178] Specifically, for each generated cluster, the average reliability coefficient and the average potential coefficient of all core points within it are calculated to obtain the mean coordinates of that cluster. Then, pre-set first and second preset map thresholds are retrieved. These thresholds are set based on oil and gas exploration professional logic; for example, areas with both high average reliability and potential coefficients are priority areas, areas with both medium averages are key tracking areas, and areas with both low averages are observation areas. Next, the mean coordinates of each cluster are compared with the first and second preset map thresholds to determine the appropriate region type for each cluster according to preset rules. Specifically, if the mean is greater than or equal to the first preset map threshold, the cluster is matched as a priority area; if the cluster's mean is greater than or equal to the second preset map threshold but less than the first preset threshold, it is matched as a key tracking area; and if the cluster's mean is less than the second preset threshold, it is matched as an observation area. Finally, the corresponding region type is assigned to each cluster.

[0179] S705. Generate an initial evaluation chart based on the region type of each cluster.

[0180] Specifically, a two-dimensional Cartesian coordinate system is constructed with the reliability coefficient as the horizontal axis and the potential coefficient as the vertical axis. Then, for each cluster of marked region types, the convex hull boundary formed by its core point set is drawn; that is, the smallest convex polygon enclosing all core points of the cluster. This convex hull boundary is the partition boundary of the cluster in the coordinate system. Within the convex hull boundary of each cluster, its corresponding region type is labeled. As an optional implementation, different visual identifiers can be used to distinguish different region types, improving the intuitiveness of the diagram.

[0181] In this embodiment, by constructing a coordinate system, drawing boundaries, and labeling types, the clustering results are transformed into a two-dimensional evaluation map that conforms to the current geological conditions, making the zoning results of the prospective area intuitive and clear.

[0182] The specific steps in step S105 above to determine the evaluation results of the potential scenic area of ​​the region to be evaluated based on the reliability coefficient, potential coefficient, and current coefficient evaluation chart are as follows:

[0183] Optionally, the target area where the reliability coefficient and potential coefficient are located in the current coefficient evaluation chart can be determined.

[0184] Specifically, the latest current coefficient evaluation chart is retrieved, and the reliability coefficient and potential coefficient of the area to be evaluated are projected onto the two-dimensional coordinate system of the current coefficient evaluation chart. The target area where the point is located is determined by the spatial point-surface matching algorithm.

[0185] Optionally, the type of the target area can be used as the evaluation result of the prospective area of ​​the area to be evaluated.

[0186] In this embodiment, the evaluation logic is simple and efficient by using the target region type as the evaluation result of the region to be evaluated.

[0187] Based on the same inventive concept, this application also provides an oil and gas resource prospective area evaluation device corresponding to the oil and gas resource prospective area evaluation method. Since the principle of the device in this application is similar to the oil and gas resource prospective area evaluation method described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0188] Reference Figure 8 The diagram shown is a structural schematic of an oil and gas resource prospect evaluation device provided in an embodiment of this application. The device includes: an acquisition module 801, a first determination module 802, a second determination module 803, a third determination module 804, and an evaluation module 805, wherein:

[0189] The acquisition module 801 is used to acquire exploration data of the area to be evaluated. The exploration data includes: source rock data, reservoir data, caprock data, regional type and scene characteristics. The regional type includes: type breakthrough subtype, regional driving subtype and compliance subtype. The scene characteristics include: integrity label of collected data, geological understanding maturity label and working status label.

[0190] The first determining module 802 is used to determine the oil and gas geological condition score set and the oil and gas resource value set based on the exploration data. The oil and gas geological condition score set includes: hydrocarbon generation condition score, reservoir condition score and caprock condition score. The oil and gas resource value set includes: resource scale score, resource abundance score and value score.

[0191] The second determining module 803 is used to determine the target weight reorganization based on the scene characteristics, wherein the target weight reorganization includes the oil and gas geological condition score set and the weights corresponding to each score in the oil and gas resource value set.

[0192] The third determining module 804 is used to determine the reliability coefficient and potential coefficient of the area to be evaluated based on the oil and gas geological condition score set, the oil and gas resource value set and the target weighting set.

[0193] The evaluation module 805 is used to determine the evaluation result of the prospective area of ​​the area to be evaluated based on the reliability coefficient, the potential coefficient and the current coefficient evaluation chart. The current coefficient evaluation chart includes: priority area, key tracking area and observation area.

[0194] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0195] This application also provides an electronic device, such as... Figure 9 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application, including a processor 901, a memory 902, and a bus. The memory 902 stores machine-readable instructions executable by the processor 901. When the computer device is running, the processor 901 and the memory 902 communicate via the bus. The processor 901 executes the machine-readable instructions to perform the processing of the above-mentioned oil and gas resource prospective area evaluation method.

[0196] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described oil and gas resource prospect evaluation method.

[0197] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0198] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0199] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating potential oil and gas resource areas, characterized in that, The method includes: Acquire exploration data for the area to be evaluated. The exploration data includes: source rock data, reservoir data, caprock data, regional type, and scene characteristics. The regional type includes: type breakthrough subtype, regional driving subtype, and compliance subtype. The scene characteristics include: integrity label of collected data, geological understanding maturity label, and work status label. Based on the exploration data, a set of oil and gas geological condition scores and a set of oil and gas resource values ​​are determined. The set of oil and gas geological condition scores includes: hydrocarbon generation condition score, reservoir condition score, and caprock condition score. The set of oil and gas resource values ​​includes: resource scale score, resource abundance score, and value score. Based on the scenario characteristics, a target weight reorganization is determined, which includes the oil and gas geological condition score set and the weights corresponding to each score in the oil and gas resource value set. Based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weighting, determine the reliability coefficient and potential coefficient corresponding to the area to be evaluated; Based on the reliability coefficient, the potential coefficient, and the current coefficient evaluation chart, the evaluation result of the prospective scenic area of ​​the area to be evaluated is determined. The current coefficient evaluation chart includes: priority area, key tracking area, and observation area. The step of determining the target weight reorganization based on the scenario characteristics includes: If the integrity label of the collected data in the scenario features indicates that the data is incomplete, and the geological knowledge maturity label indicates that the knowledge is immature, and the work status label indicates that the work is currently in the early stage of the investigation, then the target weight reorganization is determined to be the first weight reorganization; If the integrity label of the collected data in the scene features indicates that the data is incomplete, and the geological cognitive maturity label indicates cognitive maturity, then the target weighting reorganization is determined to be the second weighting reorganization. If the integrity label of the collected data in the scenario features indicates that the data is complete, and the working status label indicates that the current working period is the breakthrough verification period, then the target weight reassembly is determined to be a third weight reassembly. In the first weighted reorganization, the weight corresponding to the hydrocarbon generation condition score is greater than the weight corresponding to the hydrocarbon generation condition score in the second weighted reorganization, the weight corresponding to the hydrocarbon generation condition score in the second weighted reorganization is greater than the weight corresponding to the hydrocarbon generation condition score in the third weighted reorganization, the weight corresponding to the resource scale score in the first weighted reorganization is greater than the weight corresponding to the resource scale score in the second weighted reorganization, and the weight corresponding to the resource scale score in the second weighted reorganization is greater than the weight corresponding to the resource scale score in the third weighted reorganization. The process of determining the reliability coefficient and potential coefficient of the region to be evaluated based on the oil and gas geological condition score set, the oil and gas resource value set, and the target weighting reorganization includes: Based on formula The reliability coefficient is calculated, where R is the reliability coefficient, α is a first preset coefficient, and β is a second preset coefficient. It refers to the weight corresponding to the hydrocarbon generation condition score in the target weight reorganization. These are the weights corresponding to the reservoir condition scores in the target weighting reorganization. These are the weights corresponding to the capping condition scores in the target weight reorganization. It is the conditional fraction for hydrocarbon generation. It is the reservoir condition score. It is the capping condition score, where the sum of the first preset coefficient and the second preset coefficient is 1; Based on formula The potential coefficient is calculated, where P is the potential coefficient. It is the third preset coefficient. It is the fourth preset coefficient. It is the weight corresponding to the resource scale score in the target weight restructuring. These are the weights corresponding to the resource abundance scores in the target weight reorganization. It is the weight corresponding to the value score in the target weight restructuring. It is a resource scale score. It is a resource abundance score. It is a value score, and the sum of the third preset coefficient and the fourth preset coefficient is 1.

2. The method for evaluating potential oil and gas resource areas according to claim 1, characterized in that, The process of determining the oil and gas geological condition score set and the oil and gas resource value set based on the exploration data includes: Based on the preset hydrocarbon generation condition quantification model and the source rock data, the hydrocarbon generation condition score in the oil and gas geological condition score set is determined. Based on the preset reservoir condition quantification model and the reservoir data, the reservoir condition score in the oil and gas geological condition score set is determined. Based on the preset caprock condition quantification model and the caprock data, the caprock condition score in the oil and gas geological condition score set is determined. Based on the preset value evaluation parameter mapping table and the exploration data, the resource scale score, resource abundance score and value score in the oil and gas resource value set are determined respectively.

3. The method for evaluating potential oil and gas resource areas according to claim 2, characterized in that, Based on the preset value evaluation parameter mapping table and the exploration data, the resource size score, resource abundance score, and value score in the oil and gas resource value set are determined, including: Calculate the oil and gas resources based on the exploration data; The resource scale fraction is determined based on the amount of oil and gas resources. Based on the oil and gas resource quantity and the caprock area in the caprock data, the resource abundance is determined, and based on the resource abundance, the resource abundance score is determined. The value score is determined based on the region type.

4. The method for evaluating potential oil and gas resource areas according to claim 1, characterized in that, The current coefficient evaluation chart is generated through the following process: Determine whether there are any new samples. If so, generate a distance curve based on the new samples and multiple historical samples. The new samples and each historical sample include: reliability coefficient, potential coefficient and drilling confirmation data, respectively. The distance corresponding to the curvature inflection point of the distance curve is selected as the neighborhood radius. Based on the neighborhood radius and the minimum number of samples, the newly added samples and multiple historical samples are clustered to obtain an initial evaluation chart. The minimum number of samples is determined based on the number of historical samples. Based on the drilling confirmation data of the newly added samples and the drilling confirmation data of multiple historical samples, the convex hull boundary of the initial evaluation chart is expanded or contracted to obtain the current coefficient evaluation chart.

5. The method for evaluating potential oil and gas resource areas according to claim 4, characterized in that, The initial evaluation chart is obtained by clustering the new samples and multiple historical samples based on the neighborhood radius and the minimum number of samples, including: The reliability coefficients and potential coefficients in the newly added samples, as well as the reliability coefficients and potential coefficients in each historical sample, are integrated into a two-dimensional point set; Cluster the two-dimensional point set using the neighborhood radius as a distance threshold and the minimum number of samples as a density threshold to determine multiple core points and multiple noise points; A connected region consisting of multiple core points is treated as a cluster, resulting in multiple clusters. Noise points outside the connected regions are then removed. Calculate the mean value of each cluster, and determine the region type of each cluster based on the first preset map threshold, the second preset map threshold, and the mean value of each cluster; An initial evaluation chart is generated based on the region type of each cluster.

6. The method for evaluating potential oil and gas resource areas according to claim 1, characterized in that, The step of determining the prospective scenic area evaluation result of the area to be evaluated based on the reliability coefficient, the potential coefficient, and the current coefficient evaluation chart includes: Determine the target regions where the reliability coefficient and the potential coefficient are located in the current coefficient evaluation chart; The type of the target area is used as the evaluation result of the prospective area of ​​the area to be evaluated.

7. An electronic device, characterized in that, include: The electronic device includes a processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when the electronic device is in operation, execute the machine-readable instructions to perform the steps of the oil and gas resource prospect evaluation method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the oil and gas resource prospect evaluation method as described in any one of claims 1 to 6.

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