An oil and gas reservoir intelligent exploration method and system based on PETROGPT and GFM heterogeneous intelligent agent bidirectional cross verification

Abstract

The application discloses an oil and gas reservoir intelligent exploration method and system based on bidirectional cross verification of PETROGPT and GFM heterogeneous intelligent agents, relates to the technical field of oil and gas reservoir exploration, and comprises the following steps: S1. Heterogeneous data hierarchical access and preprocessing: collecting unstructured knowledge data such as regional geology reports, drilling and logging texts, and geological interpretation data, and constructing a standardized regional geology knowledge graph K_geo through natural language entity extraction and relationship analysis; collecting structured geophysical exploration data such as prestack and poststack seismic data, well logging curves and core physical property experimental data, completing denoising, time-depth correction, registration and normalization, and generating a unified tensor data set D_seis. The application adopts a double-agent architecture with completely heterogeneous PETROGPT and GFM mechanisms, avoids the problems of homologous errors and bias superposition caused by the isomorphic model from the root, realizes cross-dimension error complementary offset, and greatly improves the model generalization ability and exploration stability in complex geological scenes.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir exploration technology, specifically to an intelligent oil and gas reservoir exploration method and system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents. Background Technology

[0002] Intelligent exploration of oil and gas reservoirs relies on the fusion of artificial intelligence, geophysical inversion, and geological big data technologies to predict underground reservoir physical parameters, identify oil and gas enrichment areas, and further analyze favorable areas. Compared to traditional manual interpretation and single geophysical exploration methods, it boasts advantages such as high efficiency, strong quantification, and adaptability to industrial-scale batch exploration, and has become the mainstream development direction for refined exploration of complex oil and gas reservoirs. High-precision, interpretable, and highly stable intelligent exploration results are crucial for ensuring accurate reservoir characterization, reducing exploration risks, and improving well placement success rates.

[0003] Current intelligent exploration technologies for oil and gas reservoirs generally suffer from technological fragmentation and a lack of verification mechanisms, resulting in overall exploration accuracy and reliability that are insufficient to meet the production demands of complex blocks. Traditional geophysical numerical inversion techniques rely entirely on structured data such as seismic and well logging to drive calculations, heavily depending on data signal-to-noise ratio and formation homogeneity. They fail to incorporate prior geological knowledge such as regional geological evolution, sedimentary characteristics, and tectonic sealing as constraints. Under complex formation conditions with low signal-to-noise ratios, such as fracture development, strong reservoir heterogeneity, and weak seismic signals, they are highly susceptible to waveform distortion and random noise interference, generating numerous false reservoir anomalies. This leads to multiple interpretations of reservoirs, difficulty in distinguishing between true and false reservoirs, and poor reliability of exploration results. Furthermore, general-purpose geological models only possess geological text recognition, information interpretation, and knowledge retrieval capabilities, enabling qualitative geological analysis only. They cannot output industrial-grade quantitative physical properties such as porosity, permeability, and oil saturation, lacking the ability to independently complete quantitative reservoir exploration solutions and thus failing to support industrial-scale reservoir prediction operations.

[0004] Existing intelligent multi-agent exploration architecture designs have significant limitations. Most mainstream exploration systems in the industry are built using homogeneous algorithm clusters, with highly consistent operational mechanisms, learning dimensions, and error sources among the agents. This results in problems such as shared model errors and uniform output biases, making it impossible to achieve cross-dimensional error complementarity and cross-correction, and exhibiting extremely poor fault tolerance. Conventional intelligent exploration workflows only employ an open-loop operation mode of "one-way guidance of geophysical inversion by geological knowledge." This mode relies solely on prior geological knowledge to positively constrain the geophysical solution process, lacking a closed-loop mechanism for back-verifying geological understanding with quantitative inversion results. It cannot utilize anomalies in quantitative geophysical data to back-verify and correct geological cognitive biases, easily leading to problems such as missed detection of hidden reservoirs and misidentification of false reservoirs. Ultimately, this results in poor interpretability and insufficient stability of exploration results, making it difficult to adapt to the high-precision exploration and production requirements of complex fault blocks and highly heterogeneous oil and gas reservoirs.

[0005] Therefore, this application proposes an intelligent exploration method and system for oil and gas reservoirs based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent oil and gas reservoir exploration method and system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents, in order to solve the problems of existing oil and gas reservoir exploration such as the separation of geology and geophysical exploration, unidirectional constraints, false reservoir misjudgment, low exploration accuracy, and poor interpretability of results.

[0007] To achieve the above objectives, this invention provides the following technical solution: an intelligent oil and gas reservoir exploration method based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents, wherein PETROGPT is a large-scale geological cognitive model and GFM is a geophysical quantitative inversion intelligent agent, comprising the following steps: S1. Heterogeneous Data Layered Access and Preprocessing: Collect unstructured knowledge data such as regional geological reports, drilling logging texts, and geological interpretation data, and construct a standardized regional geological knowledge graph K_geo through natural language entity extraction and relationship sorting; collect structured geophysical data such as pre-stack and post-stack seismic data, well logging curves, and core physical property test data, and complete denoising, time-depth correction, registration and normalization to generate a unified tensor dataset D_seis; S2. Independent Parallel Operation of Two Heterogeneous Agents: Two agents with completely heterogeneous functions and mechanisms are invoked for synchronous reasoning operations. The PETROGPT geological cognitive agent takes K_geo as input and completes the reasoning of regional hydrocarbon accumulation patterns, sedimentary characteristics, and structural styles based on the Transformer architecture. It outputs a set of qualitative conclusions C_geo, such as favorable exploration facies zones and structural risk areas, and outputs the corresponding cognitive confidence P_cog. The GFM geophysical quantitative inversion agent takes the standardized dataset D_seis as input and performs parameter solving through geophysical full waveform inversion and deep learning inversion network. It outputs the three-dimensional reservoir parameter body R_quant, which includes porosity, permeability, and oil saturation, and outputs the numerical calculation confidence P_calc. S3. Two-way cross-loop verification and pseudo-reservoir error correction: positive cognitive constraint verification: the geological facies zone constraints and structural boundary conditions output by PETROGPT are mapped to the spatial weight matrix W_geo and regular constraint terms of the GFM inversion objective function, optimizing the inversion solution space and avoiding geological inconsistencies; Reverse quantitative source tracing verification: Extract contradictory anomaly areas with high physical properties and low seismic amplitude from the GFM inversion results, automatically generate geological verification propositions and push them to PETROGPT. PETROGPT combines the regional reservoir formation law knowledge base to verify the rationality of the anomalies, generate a binary mask M_mask for anomaly areas that do not conform to geological laws, automatically remove pseudo reservoir areas, and at the same time generate a forward consistency coefficient α and a reverse consistency coefficient β. S4. Multi-factor confidence fusion and iterative output: Construct a multi-dimensional adaptive weighted fusion model, calculate the global comprehensive exploration confidence score Score_final, set a unified exploration evaluation threshold, and output the final reservoir 3D model, reservoir property distribution, favorable area division, and standardized exploration results report when the comprehensive confidence score is greater than or equal to the threshold; when the comprehensive confidence score is lower than the threshold, adaptively adjust the constraint coefficient λ, restart the dual-agent iterative verification until the results meet the accuracy requirements.

[0008] Furthermore, in step S3, the positive cognitive constraint is added to the inversion objective function by transforming geological facies zones and tectonic boundaries into a spatial weight matrix W_geo and a regularization term: J(m) = ||d_obs f(m)||²+λ·W_geo·R(m) Where: m is the underground medium model parameter, f(m) is the seismic forward modeling operator, R(m) is the regularization term, and λ is the adaptive constraint coefficient.

[0009] Furthermore, in step S3, the reverse quantitative verification generates verification propositions by extracting contradictory and abnormal regions, and PETROGPT determines their rationality and generates a binary mask M_mask to eliminate false reservoirs.

[0010] Furthermore, the comprehensive confidence level is calculated in step S4 as follows: Score_final=w1 P_cog+w2 P_calc+w3 (α+β) / 2 Where w1, w2, and w3 are preset weight coefficients, and w1+w2+w3=1; if Score_final≥threshold, the result is output; otherwise, λ is adjusted and the iteration is restarted.

[0011] This invention also provides an intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents, including: a hierarchical heterogeneous data preprocessing module, a PETROGPT geological cognitive reasoning module, a GFM geophysical quantitative inversion module, a bidirectional cross-validation closed-loop verification engine module, a multi-factor confidence fusion decision module, and a three-dimensional visualization and result output module. Each module is connected in sequence to communicate and work together to complete the intelligent exploration of oil and gas reservoirs.

[0012] Furthermore, the hierarchical heterogeneous data preprocessing module is used for the classification, collection, cleaning, and standardized modeling of unstructured geological knowledge and structured geophysical data.

[0013] Furthermore, the PETROGPT geological cognitive reasoning module is used to complete the mining of regional geological patterns, the judgment of hydrocarbon accumulation, and the identification of qualitative risks.

[0014] Furthermore, the GFM geophysical quantitative inversion module is used for high-precision inversion and solution of reservoir parameters driven by seismic and well logging data.

[0015] Furthermore, the bidirectional cross-loop verification engine module is used to perform dual-path verification and pseudo-reservoir removal, which involves positive constraints of geological cognition and reverse tracing of quantitative anomalies.

[0016] Furthermore, the multi-factor confidence fusion decision module is used for weighted calculation of multiple confidence indicators, result determination, and automatic iterative control.

[0017] Furthermore, the 3D visualization and output module is used for reservoir model display, verification result visualization, and automatic generation of exploration reports.

[0018] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a two-way cross-constraint and closed-loop error correction mechanism between PETROGPT geological cognition and GFM geophysical quantitative inversion to achieve mutual verification between geological prior knowledge and geophysical data. This can effectively identify and eliminate false reservoir anomalies without geological basis, significantly suppress false reservoir misjudgment, and greatly improve the accuracy and reliability of reservoir identification in complex oil and gas blocks. 2. This invention optimizes the inversion objective function by transforming geological laws into regularized constraints, effectively improving the ill-conditioned solution problem of traditional inversion, enhancing the ability to identify thin interbedded and weak signal reservoirs, and significantly improving the resolution and prediction accuracy of thin reservoir exploration; 3. This invention enables each reservoir identification result to be supported by geological knowledge and verified by two heterogeneous intelligent agents through collaborative reasoning and two-way verification and tracing. This breaks the black box mode of traditional AI exploration and realizes that the exploration results are interpretable, traceable and verifiable throughout the entire process, which is highly adaptable to oilfield industrial production and field application. 4. This invention adopts a dual-agent architecture with completely heterogeneous PETROGPT and GFM mechanisms, which fundamentally avoids the problem of overlapping homogeneous errors and biases caused by isomorphic models, and achieves cross-dimensional complementary cancellation of errors, significantly improving the system's generalization ability and exploration stability under complex geological conditions, strong heterogeneity, and low signal-to-noise ratio. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the intelligent oil and gas reservoir exploration method of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0021] This invention provides, for example Figure 1 The method shown is an intelligent oil and gas reservoir exploration method based on bidirectional cross-validation of heterogeneous intelligent agents PETROGPT and GFM, where PETROGPT is a large-scale geological cognitive model and GFM is a geophysical quantitative inversion intelligent agent. The method includes the following steps: S1. Heterogeneous Data Layered Access and Preprocessing: Collect unstructured knowledge data such as regional geological reports, drilling logging texts, and geological interpretation data, and construct a standardized regional geological knowledge graph K_geo through natural language entity extraction and relationship sorting; collect structured geophysical data such as pre-stack and post-stack seismic data, well logging curves, and core physical property test data, and complete denoising, time-depth correction, registration and normalization to generate a unified tensor dataset D_seis.

[0022] This embodiment addresses the issues of unstructured geological knowledge being unable to be quantified and computationally efficient, and the significant noise interference in structured geophysical data, through a hierarchical differentiated preprocessing approach. It achieves bidirectional regularization and adaptation between "geological prior knowledge" and "geophysical measured data," providing a high-quality and highly reliable data foundation for subsequent qualitative reasoning and quantitative inversion by intelligent agents. This reduces exploration errors from the data source and avoids reservoir prediction distortion caused by defects in the original data.

[0023] S2. Independent Parallel Operation of Two Heterogeneous Agents: Two agents with completely heterogeneous functions and mechanisms are invoked for synchronous reasoning operations. The PETROGPT geological cognitive agent takes K_geo as input and completes the reasoning of regional hydrocarbon accumulation patterns, sedimentary characteristics, and structural styles based on the Transformer architecture. It outputs a set of qualitative conclusions C_geo, such as favorable exploration facies zones and structural risk areas, and outputs the corresponding cognitive confidence P_cog. The GFM geophysical quantitative inversion agent takes the standardized dataset D_seis as input and performs parameter solving through geophysical full waveform inversion and deep learning inversion networks. It outputs the three-dimensional reservoir parameter body R_quant, including porosity, permeability, and oil saturation, and outputs the numerical calculation confidence P_calc.

[0024] This embodiment adopts a heterogeneous dual-agent parallel architecture, which differs from the traditional single-model and homogeneous model prediction methods. It completes reservoir assessment from two completely independent dimensions: qualitative cognition of geological knowledge and quantitative solution of geophysical data. This avoids the problem of overlapping errors from the same source in homogeneous models from the root, and achieves cross-dimensional error complementarity. At the same time, the synchronous operation of the two agents greatly improves exploration efficiency and provides two sets of independent, comparable, and mutually verifiable core exploration data for subsequent two-way cross-validation.

[0025] S3. Two-way cross-loop verification and pseudo-reservoir error correction: positive cognitive constraint verification: the geological facies zone constraints and structural boundary conditions output by PETROGPT are mapped to the spatial weight matrix W_geo and regular constraint terms of the GFM inversion objective function, optimizing the inversion solution space and avoiding geological inconsistencies; Reverse quantitative source tracing verification: Extract contradictory anomaly areas with high physical properties and low seismic amplitude from the GFM inversion results, automatically generate geological verification propositions and push them to PETROGPT. PETROGPT combines the regional reservoir formation law knowledge base to verify the rationality of the anomalies, generate a binary mask M_mask for anomaly areas that do not conform to geological laws, automatically remove pseudo reservoir areas, and at the same time generate a forward consistency coefficient α and a reverse consistency coefficient β.

[0026] This embodiment addresses the shortcomings of traditional single geophysical inversion, such as lack of geological constraints, multiple solutions, and susceptibility to false structural high points and false reservoirs, by optimizing geophysical inversion through forward geological constraints and verifying the geological rationality through reverse quantitative anomaly verification. It effectively suppresses false anomalies caused by seismic noise and fault distortion, and achieves intelligent identification and automatic removal of false reservoirs. At the same time, through dual-coefficient quantification matching accuracy, the exploration results have quantifiable and traceable verification basis, which greatly improves the reliability of complex reservoir prediction.

[0027] S4. Multi-factor confidence fusion and iterative output: Construct a multi-dimensional adaptive weighted fusion model, calculate the global comprehensive exploration confidence score Score_final, set a unified exploration evaluation threshold, and output the final reservoir 3D model, reservoir property distribution, favorable area division, and standardized exploration results report when the comprehensive confidence score is greater than or equal to the threshold; when the comprehensive confidence score is lower than the threshold, adaptively adjust the constraint coefficient λ, restart the dual-agent iterative verification until the results meet the accuracy requirements.

[0028] This embodiment breaks through the limitations of traditional single-index evaluation. It achieves comprehensive quantitative evaluation of exploration results through multi-factor fusion. Combined with an adaptive iteration mechanism, it solves the problem of insufficient prediction accuracy in complex blocks. Through multiple closed-loop iterations, it continuously optimizes the inversion results, ensuring the exploration adaptability of blocks with high and low signal-to-noise ratios and different levels of complexity, and realizing standardized, high-precision, and automated output of exploration results.

[0029] In step S3, the positive cognitive constraint is achieved by transforming geological facies zones and tectonic boundaries into a spatial weight matrix W_geo and incorporating a regularization term into the inversion objective function: J(m) = ||d_obs f(m)||²+λ·W_geo·R(m) Where: m is the underground medium model parameter, f(m) is the seismic forward modeling operator, R(m) is the regularization term, and λ is the adaptive constraint coefficient.

[0030] In step S3, reverse quantitative verification generates verification propositions by extracting contradictory and abnormal regions. PETROGPT then determines the reasonableness of these propositions and generates a binary mask M_mask to eliminate false reservoirs.

[0031] The overall confidence level is calculated in step S4 as follows: Score_final=w1 P_cog+w2 P_calc+w3 (α+β) / 2 Where w1, w2, and w3 are preset weight coefficients, and w1+w2+w3=1; if Score_final≥threshold, the result is output; otherwise, λ is adjusted and the iteration is restarted.

[0032] This embodiment thoroughly solves the industry problems of traditional geophysical inversion, such as lack of geological constraints, ill-conditioned solutions, and multiple solutions, through a closed-loop formula system of forward formula constraints, reverse masking error correction, and multi-factor confidence iteration. The forward optimization formula achieves deep integration of geological knowledge and geophysical inversion, constrains the inversion solution space, and improves the geological rationality of reservoir inversion. The reverse proposition verification + masking elimination mechanism achieves intelligent and accurate removal of false reservoirs, eliminating exploration misjudgments caused by structural false high points and seismic noise. The multi-factor confidence fusion and adaptive iteration mechanism overcomes the one-sidedness of single evaluation indicators, making the accuracy of results quantifiable, controllable, and iteratively optimized. This significantly improves the stability, accuracy, and reliability of results in the exploration of complex fault blocks, low signal-to-noise ratio, and highly heterogeneous oil and gas reservoirs. At the same time, the entire process is automated iterative calculation without the need for manual parameter adjustment, significantly improving the level of intelligence in exploration operations and the value of industrial applications.

[0033] This invention also provides an intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents, including: a hierarchical heterogeneous data preprocessing module, a PETROGPT geological cognitive reasoning module, a GFM geophysical quantitative inversion module, a bidirectional cross-validation closed-loop verification engine module, a multi-factor confidence fusion decision module, and a three-dimensional visualization and result output module. Each module is connected in sequence to communicate and work together to complete the intelligent exploration of oil and gas reservoirs.

[0034] The hierarchical heterogeneous data preprocessing module is used for the classification, collection, cleaning, and standardized modeling of unstructured geological knowledge and structured geophysical data; the PETROGPT geological cognitive reasoning module is used to complete the mining of regional geological patterns, the judgment of reservoir formation cognition, and the identification of qualitative risks; the GFM geophysical quantitative inversion module is used for high-precision inversion and solution of reservoir parameters driven by seismic and well logging data; the bidirectional cross-loop verification engine module is used to perform dual-path verification and pseudo-reservoir removal for geological cognitive positive constraints and quantitative anomaly reverse tracing; the multi-factor confidence fusion decision module is used for multi-confidence index weighted calculation, result judgment, and automatic iterative control; and the 3D visualization and result output module is used for reservoir model display, verification result visualization, and automatic generation of exploration reports.

[0035] This embodiment achieves fully automated operation of data processing, intelligent reasoning, cross-validation, accuracy evaluation, and result output through modular and integrated system architecture design. Each module has a clear division of labor and works in synergy, solving the problems of traditional exploration processes being scattered, involving a lot of manual intervention, being highly subjective, and inefficient. It realizes the intelligent, standardized, and industrialized application of the entire oil and gas reservoir exploration process, significantly improving the accuracy and operational efficiency of complex oil and gas reservoir exploration.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent exploration of oil and gas reservoirs based on bidirectional cross-validation of heterogeneous intelligent agents PETROGPT and GFM, wherein PETROGPT is a large-scale geological cognitive model and GFM is a geophysical quantitative inversion intelligent agent, characterized in that... Includes the following steps: S1. Heterogeneous Data Layered Access and Preprocessing: Collect unstructured knowledge data such as regional geological reports, drilling logging texts, and geological interpretation data, and construct a standardized regional geological knowledge graph K_geo through natural language entity extraction and relationship sorting; collect structured geophysical data such as pre-stack and post-stack seismic data, well logging curves, and core physical property test data, and complete denoising, time-depth correction, registration and normalization to generate a unified tensor dataset D_seis; S2. Independent Parallel Operation of Two Heterogeneous Agents: Two agents with completely heterogeneous functions and mechanisms are invoked for synchronous reasoning operations. The PETROGPT geological cognitive agent takes K_geo as input and completes the reasoning of regional hydrocarbon accumulation patterns, sedimentary characteristics, and structural styles based on the Transformer architecture. It outputs a set of qualitative conclusions C_geo, such as favorable exploration facies zones and structural risk areas, and outputs the corresponding cognitive confidence P_cog. The GFM geophysical quantitative inversion agent takes the standardized dataset D_seis as input and performs parameter solving through geophysical full waveform inversion and deep learning inversion network. It outputs the three-dimensional reservoir parameter body R_quant, which includes porosity, permeability, and oil saturation, and outputs the numerical calculation confidence P_calc. S3. Two-way cross-loop verification and pseudo-reservoir error correction: positive cognitive constraint verification: the geological facies zone constraints and structural boundary conditions output by PETROGPT are mapped to the spatial weight matrix W_geo and regular constraint terms of the GFM inversion objective function, optimizing the inversion solution space and avoiding geological inconsistencies; Reverse quantitative source tracing verification: Extract contradictory anomaly areas with high physical properties and low seismic amplitude from the GFM inversion results, automatically generate geological verification propositions and push them to PETROGPT. PETROGPT combines the regional reservoir formation law knowledge base to verify the rationality of the anomalies, generate a binary mask M_mask for anomaly areas that do not conform to geological laws, automatically remove pseudo reservoir areas, and at the same time generate a forward consistency coefficient α and a reverse consistency coefficient β. S4. Multi-factor confidence fusion and iterative output: Construct a multi-dimensional adaptive weighted fusion model, calculate the global comprehensive exploration confidence score Score_final, set a unified exploration evaluation threshold, and when the comprehensive confidence score is greater than or equal to the threshold, output the final reservoir 3D model, reservoir physical property distribution, favorable area division and standardized exploration results report. When the overall confidence level is lower than the threshold, the constraint coefficient λ is adaptively adjusted, and the dual-agent iterative verification is restarted until the results meet the accuracy requirements.

2. The intelligent oil and gas reservoir exploration method based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 1, characterized in that: In step S3, the positive cognitive constraint is added to the inversion objective function by transforming geological facies zones and tectonic boundaries into a spatial weight matrix W_geo and a regularization term: J(m)=||d_obs f(m)||²+λ·W_geo·R(m) Where: m is the underground medium model parameter, f(m) is the seismic forward modeling operator, R(m) is the regularization term, and λ is the adaptive constraint coefficient.

3. The intelligent oil and gas reservoir exploration method based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 1, characterized in that: In step S3, the reverse quantitative verification generates verification propositions by extracting contradictory and abnormal regions, and PETROGPT determines their rationality and generates a binary mask M_mask to eliminate false reservoirs.

4. The intelligent oil and gas reservoir exploration method based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 3, characterized in that: The comprehensive confidence level calculation in step S4 is as follows: Score_final=w1 P_cog+w2 P_calc+w3 (a+b) / 2 Where w1, w2, and w3 are preset weight coefficients, and w1+w2+w3=1; if Score_final≥threshold, the result is output; otherwise, λ is adjusted and the iteration is restarted.

5. An intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents, characterized in that, include: The system comprises a hierarchical heterogeneous data preprocessing module, a PETROGPT geological cognitive reasoning module, a GFM geophysical quantitative inversion module, a two-way cross-loop verification engine module, a multi-factor confidence fusion decision module, and a three-dimensional visualization and output module. These modules are sequentially connected and work together to complete intelligent exploration of oil and gas reservoirs. The hierarchical heterogeneous data preprocessing module is used for the classification, collection, cleaning, and standardized modeling of unstructured geological knowledge and structured geophysical data.

6. The intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 5, characterized in that: The PETROGPT geological cognitive reasoning module is used to complete the mining of regional geological patterns, the cognitive judgment of hydrocarbon accumulation, and the identification of qualitative risks.

7. The intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 5, characterized in that: The GFM geophysical quantitative inversion module is used for high-precision inversion and solution of reservoir parameters driven by seismic and well logging data.

8. The intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 5, characterized in that: The bidirectional cross-loop verification engine module is used to perform dual-path verification and pseudo-reservoir removal, including positive constraints for geological cognition and reverse tracing of quantitative anomalies.

9. The intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 5, characterized in that: The multi-factor confidence fusion decision module is used for weighted calculation of multiple confidence indicators, result determination, and automatic iterative control.

10. The intelligent oil and gas reservoir exploration system based on bidirectional cross-validation of PETROGPT and GFM heterogeneous intelligent agents according to claim 5, characterized in that: The 3D visualization and output module is used for reservoir model display, verification result visualization, and automatic generation of exploration reports.

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