The extraction of shale oil and gas is confronted with numerous complex geomechanical issues. As a core factor determining extraction efficiency and safety, the mechanical properties of shale urgently require in-depth research and exploration. Against this backdrop, machine learning, with its powerful capabilities in data processing and pattern recognition, has opened up new avenues for research on shale mechanical properties. This paper focuses on the application of machine learning in the study of shale mechanical properties, systematically elaborating on the current status, challenges, and prospects in this field. Firstly, it details the application achievements of current machine learning algorithms in the prediction of shale mechanical parameters and the recognition of failure modes, demonstrating their significant advantages over traditional research methods in processing complex data and mining potential patterns. Subsequently, it summarizes the multiple challenges faced in the application of machine learning to the study of shale mechanical properties. Shale sample data exhibits high-dimensional and small-sample characteristics, which easily lead to overfitting in models. Meanwhile, the internal operating mechanisms of most machine learning models are difficult to interpret, restricting their popularization and application. In addition, the geological conditions of shale are complex and variable, with significant differences in mineral composition and pore structure of shale in different regions. The existing models show obviously insufficient universality when applied across regions and geological conditions. Finally, the future is prospected based on the development trends of cutting-edge technologies. Machine learning has broad prospects in the field of shale mechanical properties research. By integrating multi-source data such as geological, geophysical, and logging data, it can provide more abundant information for models and reduce the negative impact caused by data dimensionality. Optimizing algorithm architectures and combining technologies such as transfer learning and ensemble learning can improve the generalization ability of models. Constructing physics-constrained machine learning models can not only enhance the interpretability of models but also improve their adaptability under complex geological conditions. These strategies are expected to break through existing bottlenecks, promote the in-depth application of machine learning in the study of shale mechanical properties, and provide solid theoretical and technical support for the efficient development of shale oil and gas resources.

Faults serve as crucial pathways and sites for hydrocarbon migration and accumulation, making their identification a key task in the interpretation of seismic data. However, the diversity of fault types, extensive distribution, and complex characteristics pose significant challenges to fault identification. To address this issue, this paper proposes a fault identification method using a 3D TransUnet model. Constructed based on 3D CNN and transformer modules, this model adopts an end-to-end structural design of the 3D Unet architecture. By learning the spatial relationships of three-dimensional faults in synthetic seismic data, it directly predicts fault information in actual seismic data. The method has been successfully applied to seismic work areas in the F3 block of the Dutch North Sea and the Halahatang area of the Tarim Basin, achieving excellent results. The research findings demonstrate that the 3D TransUnet model combines the high local accuracy of CNN and the global attention mechanism of Transformer, enabling inference and prediction of faults in complex regions based on global fault information. Compared with the 3D Unet model and other traditional fault identification methods, the 3D TransUnet model achieved a recall rate of 0.87 and a precision rate of 0.83 on the validation set, significantly outperforming other approaches. In practical applications within three-dimensional seismic work areas, the 3D TransUnet model accurately identifies fault information across different regions. For faults with subtle features, the incorporation of the Transformer module equips the model with a global attention mechanism, allowing it to infer the presence of faults by analyzing distribution trends across the entire work area. By applying the trained fault identification model to different practical seismic work areas (the F3 block and the Halahatang area in this study), the universality of the method is demonstrated, indicating that the trained fault identification model can be effectively utilized across seismic data from various regions. This study finds that the method can effectively identify microfracture information within formations. In oil and gas fields where microfractures serve as reservoirs, since microfractures primarily develop along major faults, well locations are typically deployed near these large faults. However, during the middle and late stages of oil production in such fields, well placement decisions rely more heavily on the development degree of microfractures. Therefore, this fault identification method provides valuable guidance for well placement in oil and gas fields where microfractures act as reservoirs.

Formation drillability assessment is crucial for drilling operations, as it directly influences operational efficiency and cost-effectiveness. Traditional three-dimensional (3D) assessment methods often face challenges due to the unstable integration of multi-source and cross-scale data, resulting in limited spatial generalization and suboptimal prediction performance. To address these limitations, this paper proposes a multi-source data fusion method based on a gated recurrent unit (GRU) network to enhance intelligent formation drillability assessment and improve drilling efficiency in a study area in eastern China. The method consists of two phases: well data training and 3D application. In the first phase, pseudo-depth domain seismic records synthesized from seismic average wavelets and well logging data serve as the foundation. Sensitive attributes related to formation drillability are further extracted as network inputs. These sensitive attributes include a velocity model incorporating geological information and a seismic frequency-fraction attribute that captures multi-scale stratigraphic structure. A corrected drillability index (D_c) is used as a label for model training, ensuring that the network learns to establish an accurate mapping relationship between input attributes and drillability indicators. This training method leverages the temporal and sequential learning capabilities of the GRU network to effectively model complex relationships in the data. In the second phase, the pretrained network was extended to 3D applications, constructing a 3D input dataset by extracting the corresponding attributes. This dataset was then fed into a pretrained GRU model to predict formation drillability in the study area. Analysis of five representative wells in the study area validated the effectiveness of D_c in characterizing rock drillability in the study area. Furthermore, experiments using the Marmousi numerical model demonstrated that the method outperformed traditional intelligent prediction methods, such as those relying solely on raw seismic data or a combination of raw seismic and well logging data. Practical application in the study area further confirmed the method’s ability to effectively capture variations in formation drillability. By providing reliable predictions, the method becomes a powerful tool for optimizing drilling operations and enhancing drilling engineering decision-making.

With the continuous advancement of oil and gas exploration technologies, unconventional hydrocarbon reservoirs have emerged as a pivotal domain for global energy resource augmentation and production enhancement. However, the inherent characteristics of low permeability, dense rock matrix, and complex heterogeneity in these reservoirs pose substantial challenges to conventional logging interpretation methodologies, particularly in constructing theoretical models, deriving empirical formulas, and inverting reservoir parameters, thereby hindering accurate reservoir identification and efficient development. The recent breakthroughs in artificial intelligence (AI) technologies have provided innovative solutions for logging interpretation in unconventional reservoirs. Through systematic analysis of cutting-edge research achievements worldwide, this paper first elucidates the core geological characteristics and evaluation challenges of unconventional reservoirs. Subsequently, it comprehensively summarizes the implementation modalities and operational efficacy of AI techniques, including machine learning and deep learning algorithms, in critical logging interpretation processes such as lithology identification, porosity prediction, permeability estimation, and hydrocarbon-bearing potential assessment. The study particularly highlights the transformative capabilities of convolutional neural networks in processing multi-scale logging data, recurrent neural networks in handling time-series measurements, and ensemble learning approaches in enhancing prediction accuracy under high-dimensional parameter spaces. The research demonstrates that AI-driven approaches achieve remarkable performance improvements compared to conventional methods, with reported accuracy enhancements of 25%~40% in lithofacies classification and 15%~30% reduction in mean absolute error for porosity estimation across various case studies. Furthermore, advanced deep learning architectures have shown exceptional capability in capturing nonlinear relationships between logging responses and reservoir properties, effectively addressing the “low signal-to-noise ratio” dilemma common in unconventional reservoir evaluation. A critical evaluation is conducted from multiple dimensions, including data quality requirements, algorithmic adaptability, computational efficiency, and model interpretability. The analysis reveals that while data-driven models excel in pattern recognition, their physical consistency and generalization capability require further improvement, particularly when dealing with spatially heterogeneous formations and limited training datasets. To address these challenges, the paper proposes three strategic development directions: (1) Hybrid modeling frameworks integrating physical constraints with data-driven approaches. (2) Transfer learning schemes for small-sample learning scenarios. (3) Multi-modal data fusion architectures incorporating logging, core, and seismic information. Moreover, the study emphasizes the necessity of establishing standardized workflows for feature engineering, model validation, and uncertainty quantification in AI-based logging interpretation systems. Special attention is given to emerging technologies such as graph neural networks for 3D reservoir characterization and physics-informed neural networks for incorporating petrophysical laws into machine learning architectures. This comprehensive review not only synthesizes the current state-of-the-art in intelligent logging interpretation but also provides a strategic roadmap for future research endeavors. The findings offer valuable theoretical references and methodological guidance for optimizing AI-based interpretation techniques in unconventional reservoir evaluation, ultimately contributing to more reliable reservoir characterization and enhanced hydrocarbon recovery in complex geological settings.

This study addresses the challenges of poor real-time performance and low accuracy in drilling condition identification by introducing an innovative intelligent recognition method. The proposed approach integrates a one-dimensional convolutional neural network (1dCNN) for local feature extraction, a bidirectional gated recurrent unit (BiGRU) to capture sequential dependencies, and a multi-head attention mechanism to emphasize critical information. This fusion enables efficient discrimination among 13 drilling conditions, including rotary drilling, slide drilling, whipstocking, and reverse whipstocking. In the model design phase, comprehensive ablation studies were conducted to evaluate the contributions of each module—1dCNN, BiGRU, self-attention, and multi-head attention—as well as their serial and parallel configurations. The performance was further optimized using the Optuna framework for automatic hyperparameter tuning. Experimental results demonstrated that the model achieved an accuracy of 96.22% on time-domain data from a single well. Additionally, in both intra- and inter-block transfer tests, the overall accuracy ranged from 94% to 97%, with each drilling condition exceeding an 80% recognition rate. Real-time testing on field data also showed a high degree of consistency with actual operational conditions. Overall, the proposed method provides a robust technical framework for real-time monitoring and optimization of drilling operations.

Shale oil wells commonly experience rapid production decline throughout their productive life, requiring frequent well interventions to maintain stable production. However, accurately identifying “abnormal decline” poses significant challenges in field management. On one hand, shale oil production is highly sensitive to reservoir conditions and operational fluctuations, which makes prediction difficult. On the other hand, although well interventions are frequently implemented, the corresponding records are often incomplete or missing, which hinders effective modeling and analysis. To address these challenges, this paper proposes an early warning method for abnormal production decline by integrating Positive-Unlabeled (PU) Learning with classical decline curve models. First, an LSTM-based PU Learning model is constructed to identify potential intervention points during the productive life. The model is trained on limited labeled intervention data and a large volume of unlabeled data.The production curve of each well is then segmented into multiple “natural decline intervals.” Next, a double exponential decline model is employed to fit the production data within each interval, from which key decline parameters are extracted. The historical distribution of these decline parameters serves as the baseline for comparison. Based on this, an anomaly detection mechanism is developed using percentile thresholds of decline parameters to issue early warnings for segments exhibiting abnormally rapid decline. An empirical study was conducted using historical production data from over 600 wells in a typical shale oil block in China from 2021 to 2024. The results demonstrate that the proposed PU-LSTM model effectively identifies intervention points, even with incomplete data labeling. The decline model exhibits high fitting accuracy and robustness, and the overall warning system shows strong practical applicability and potential for broad engineering application in well performance monitoring and optimizing intervention timing.

Deepwater gas field development faces complex challenges such as ultra-deep water, strong multi-field coupling, and high operational risks, including hull stability risks, difficulties in reservoir characterization, limited accessibility of monitoring data, and the complexity of integrated production management. Traditional approaches often rely on fragmented single-module simulations and manual decision-making, resulting in delayed model updates, isolated information, and the inability to achieve end-to-end collaborative optimization across reservoirs, pipeline networks, and platforms. Digital-twin technology overcomes these limitations by breaking down data silos, enhancing model coupling, and reducing decision latency. It enables real-time interaction, closed-loop control, and full-chain integrated management, thereby eliminating information barriers among reservoirs, wellbores, pipelines, and platforms and providing coordinated, efficient production control and safety assurance for deepwater gas fields. Focusing on the “Deep Sea No. 1” production platform, this study explores the construction of a production digital-twin system that spans the entire business chain of reservoir-wellbore-pipeline network-platform-operation-finance. First, the research progress of digital twins in oil and gas production is systematically reviewed, including typical modeling methods, technical frameworks, and engineering practices. Secondly, a modular hybrid modeling approach integrating physical mechanism models and data-driven models is proposed, establishing a complete modeling workflow comprising system decomposition, model construction, data integration, optimization solving, and feedback control. Third, based on the actual application scenario of the “Deep Sea No. 1” platform, digital-twin modules are developed for mooring and hull management, flow assurance management, intelligent reservoir management, and 3D visualization, enabling early warning, predictive maintenance, decision support, immersive visualization, and full-chain closed-loop control. Field application results demonstrate that the system significantly improves the automation and intelligence of the platform, reducing production allocation calculation time from 4-5 days to less than 1 hour with prediction accuracy exceeding 90%. Finally, in response to current issues such as limited model transferability and heavy manual intervention, this paper suggests establishing a linkage framework of large and small models, strengthening integration with subsea control systems, and building a full-lifecycle digital-twin system. The research results provide a feasible technical pathway and engineering reference for the intelligent and efficient development of deepwater gas fields.

Inter-well connectivity has become one of the important criteria for guiding the development of water drive reservoirs. Traditional methods for predicting inter-well connectivity, such as tracer analysis, well testing analysis, numerical simulation, etc, have problems such as computational difficulties, cumbersome processes, and high costs. However, deep learning based methods suffer from issues such as data sensitivity and poor adaptability. To address the aforementioned challenges and problems, this paper proposes a prediction method for predicting inter well connectivity by fusing numerical models with graph neural networks. On the one hand, this method fully considers the physical parameters related to the injection and production well network in the production process, which derives a numerical model of inter-well connectivity considering multiple factors, and solves the problem of single form in previous numerical models. On the other hand, the similarity between well network structure and graph structure was reasonably utilized, a graph neural network model based on long and short-term memory neural network was designed. Then, a fusion method with deep learning network model was proposed, which was fused with numerical model. This solves the problem of traditional artificial intelligence methods ignoring physical parameters, and introduces self attention mechanism to optimize the model under the long and short-term memory neural network framework. The mechanism model and actual reservoir production data were used to predict inter-well connectivity and fluid production on the established model, and new development plans were formulated based on the predicted results. The model’s performance was verified through several sets of experiments. The results show that when the model established in this paper is used to predict inter-well connectivity, and the liquid production is calculated based on the connectivity prediction results, the prediction accuracy is as high as 98%, indicating that the model’s prediction results are reliable. Using the fusion model to predict inter-well connectivity of different sub-layers in the reservoir, it is found that the model’s prediction accuracy reaches over 95% in well patterns of different scales, showing strong applicability. Finally, the production plan was adjusted based on the connectivity prediction results: liquid reduction was implemented for both wells with high connectivity and those with low connectivity. Comparison shows that the predicted recovery degree after 10 years of the adjusted development plan is 6.8% higher than that of the original plan. This method balances physical interpretability and computational efficiency, providing reliable technical support for judging the development effect of water-flooded reservoirs and designing secondary development plans.

In the process of oilfield development, accurate production performance prediction can provide crucial support for adjusting production measures and optimizing development strategies. The complex spatial structure of underground well networks and the dynamic stochastic time-varying characteristics hinder the effective learning of spatiotemporal relationships between injection and production wells in existing prediction methods. Furthermore, current approaches fail to account for the cross-time-step spatiotemporal response relationships among multiple parameters, resulting in limitations in extracting temporal features and conducting correlation analysis of multi-well production performance sequences. These constraints ultimately restrict the improvement of production prediction accuracy. This study proposes a spatiotemporal graph neural network-based multi-well production forecasting method, incorporating a Tree-structured Parzen Estimator (TPE)-driven model parameter optimization strategy. The approach effectively aggregates multivariate information from neighboring nodes, enhancing reservoir production prediction accuracy and robustness. The model is validated using production data from an offshore waterflood reservoir. Results demonstrate that the optimized model achieves high accuracy, with improved production trend and confidence interval predictions. Comparative experiments confirm the model’s effectiveness in leveraging multi-dynamic information, significantly improving prediction accuracy. Specifically, the mean squared error is reduced by 23.67%~56.96%, and the quantile loss function decreases by 18.31%~59.58% compared to existing methods. The proposed framework provides reliable support for waterflood reservoir production forecasting and decision-making.

The production dynamics of horizontal wells in bottom-water sandstone reservoirs are influenced by multiple factors, including reservoir properties and production regimes, posing significant challenges for prediction. Traditional empirical formulas and numerical simulation methods have numerous limitations. In recent years, with the advancement of intelligent oilfield processes, machine learning approaches have been increasingly applied to oilfield production processes. Compared to traditional methods, machine learning offers comprehensiveness and efficiency but lacks physical interpretability and robustness, thereby restricting its credibility and applicability in practical engineering scenarios. To address this issue, firstly, taking a block in the Bohai Oilfield as an example, this paper establishes a seepage mechanism model for horizontal wells in bottom-water sandstone reservoirs considering time-varying permeability. Secondly, improvements are made to the traditional Long Short-Term Memory (LSTM) neural network structure to construct a neural network framework that supports multi-layer inputs of both static and dynamic data, enhancing the model’s adaptability to various geological conditions, with adjustable weights for static and dynamic parameters. Lastly, a dataset generated from the seepage mechanism model is used, and through dataset fusion, a hybrid prediction model combining data-driven and seepage physics mechanisms for horizontal well production dynamics is established. This data-physics fusion mechanism enhances the interpretability and robustness of the model. Test results indicate that the multi-layer input neural network framework for static and dynamic parameters achieves slightly higher prediction accuracy compared to traditional LSTM networks, while the hybrid model integrating data-driven and seepage mechanism shows a significantly improved prediction accuracy over the multi-layer input neural network framework. Field application also verifies the high efficiency and practicality of this hybrid model in predicting the production dynamics of horizontal wells, providing strong support for optimizing oilfield development plans and establishing production regimes.

CO₂ flooding is a key technology for achieving the dual objectives of efficient reservoir development and carbon sequestration. However, the occurrence of CO₂ channeling during the flooding process has emerged as a critical engineering challenge, severely restricting improvements in oil recovery and compromising the safety and efficiency of carbon storage. The Accurate prediction of CO₂ channeling onset, coupled with the timely deployment of effective mitigation strategies, is indispensable for safeguarding the economic benefits of CO₂ flooding, ensuring the long-term integrity of carbon storage, maximizing incremental oil recovery, and minimizing the operational and environmental risks associated with premature gas channeling. Despite its importance, existing prediction methods face several practical challenges: (1) conventional quantitative characterization models rely heavily on specific reservoir parameters, limiting their applicability to diverse reservoir conditions; (2) in the early stages prior to channeling onset, the gas-oil ratio is close to zero, with the available data typically high-dimensional, small-sample, and sparse-features that hinder empirical formula-based methods from effectively capturing complex nonlinear patterns; (3) high-permeability zones and fractures, which form preferential flow channels, are randomly distributed within the reservoir, exhibit permeability far exceeding that of the matrix, and are difficult to characterize accurately, thereby exacerbating the complexity and uncertainty of CO₂ channeling prediction. To address these challenges, this work proposes an intelligent prediction framework that integrates multi-source domain deep transfer learning with a Stacking ensemble learning strategy. A multi-source heterogeneous data training approach is employed to enable adaptive knowledge transfer across reservoirs. Four base learners-Support Vector Regression, Decision Tree, Random Forest, and K-Nearest Neighbors—are incorporated into the Stacking ensemble model, leveraging their complementary prediction strengths to improve fitting performance and enhance the accuracy of complex pattern recognition in the high-dimensional, small-sample datasets associated with CO₂ channeling onset prediction. Field application results demonstrate the method’s outstanding predictive performance, achieving a coefficient of determination (R²) of 0.96, mean squared error (MSE) of 12.61, and mean absolute percentage error (MAPE) as low as 6.04%. This work not only provides a more precise and intelligent technical pathway for CO₂ channeling prediction but also exhibits strong generalizability and cross-reservoir adaptability. It offers a new solution for optimizing CO₂ flooding across different reservoir types, with significant engineering value for enhancing oil recovery, reducing development risks, and achieving large-scale carbon sequestration goals. Furthermore, the proposed approach offers valuable methodological insights and transferable principles for the prediction and mitigation of other fluid channeling timing, including gas channeling and water channeling.

Predicting the rate of penetration (ROP) plays a significant role in optimizing drilling parameters, improving drilling efficiency, and reducing costs. Although intelligent algorithms have achieved promising results in ROP prediction, existing methods generally ignore the impact of drill bit wear on ROP. To address this technical bottleneck, this study proposes a ROP prediction model incorporating drill bit wear, which establishes a dual neural network architecture for predicting drill bit wear coefficients and ideal ROP. This architecture enables modeling of the complex nonlinear relationships among drilling parameters, wear states, and ROP. Aiming at the scarcity of real-time drill bit wear labels, a pretraining mechanism is proposed to obtain wear coefficients through a two-step training process. Comparative experiments based on measured data from Blocks A and B in Bohai oilfield show that: (1) The prediction accuracy of the porposed model for ROP is improved by 100% and 27% in Blocks A and B, respectively, compared with traditional machine learning methods, and by 14% and 7.6% compared with BP neural network models, significantly surpassing the performance of traditional data-driven models. (2) The proposed model demonstrates improvements in prediction performance in the shallow strata with complex lithology (Block A) than in the deep strata with stable lithology (Block B). (3) The proposed pretraining mechanism enables the model to predict drill bit wear coefficients without real-time wear labels and simultaneously improves the prediction accuracy of mechanical ROP by 24% and 10% in the two blocks, respectively. The coupled model and pretraining mechanism developed in this study not only provide a more accurate method for mechanical ROP prediction but also offer an effective means for real-time monitoring of drill bit wear states, providing practical guidance for drilling operations.

The strong heterogeneity of volcanic gas reservoirs and the multiple factors affecting the open flow potential of gas wells make it difficult for traditional methods of predicting the open flow potential of gas wells to balance computational efficiency and accuracy. In response to the above issues, this study introduces the data-driven Extreme Gradient Boosting algorithm (XGBoost) and proposes an algorithm that integrates the gas permeation mechanism and the data-driven approach to construct a single-well open-flow potential model for volcanic gas reservoirs based on the physically guided XGBoost algorithm (PG-XGBoost). This study is based on actual data from 50 gas wells in the Dixi block of the Kelameili gas field. Through the dual screening of the Mean Decrease Impurity (MDI) algorithm and Spearman correlation coefficient analysis, a comprehensive quantitative analysis is conducted on seven factors affecting the open flow potential of gas wells, including reservoir lithology, permeability, porosity, formation pressure, reservoir thickness, degree of fracture development, and fracturing treatment. The key factor for the open flow potential of gas wells is selected. Based on this, the XGBoost algorithm is used to construct a prediction model for the open flow potential of gas wells, and the binomial gas well productivity equation is used as the characterization formula for the gas seepage mechanism. Combining with the loss function of the XGBoost algorithm, a physics-guided XGBoost algorithm is constructed. Furthermore, the actual data of gas wells in the Dixi block are applied for blind well testing to evaluate the accuracy of the PG-XGBoost algorithm in predicting the open flow rate of gas wells. The results indicate that permeability, formation pressure, fracturing, reservoir lithology, and the degree of fracture development are the key factors for the open flow rate of a single well in the volcanic gas reservoir of the Dixi block in the Kelameili gas field. The PG-XGBoost algorithm was tested on 5 gas wells in this block, and the prediction accuracy of the open flow rate is 88.0%, which is 15.2% higher than that of the data-driven XGBoost algorithm. Therefore, using the binomial productivity equation as a physical constraint for the data-driven algorithm can effectively characterize non-Darcy gas flow and improve the prediction accuracy of the XGBoost algorithm for the gas well open flow rate. The method in this study can accurately predict the open flow potential of a single well in volcanic gas reservoirs, providing a technical path for predicting the open flow potential of complex gas reservoirs such as volcanic gas reservoirs.

Accurate and reliable production forecasting is a critical component for the efficient development of oil and gas fields and supports informed scientific decision-making. Although machine learning methods have achieved significant progress in this domain, existing models are typically trained from scratch using limited historical production data, making it difficult to effectively capture the complex nonlinear dynamics, long-term temporal dependencies, and high-dimensional interactions among variables inherent in production time series. This often leads to insufficient generalization capacity and limited predictive robustness. To address these challenges, this study proposes a novel gas well production forecasting method based on large language models (LLMs). The approach builds upon a pre-trained GPT-2 architecture and incorporates several key adaptations to enable effective time-series prediction. First, the input data—including daily gas production rate, tubing pressure, casing pressure, and production time—are subjected to instance normalization to facilitate knowledge transfer. Second, a trainable embedding layer is designed to map numerical time-series data into the semantic embedding space of the LLM, thereby achieving cross-modal alignment between continuous signals and the discrete representation format required by the model. Third, a parameter-efficient transfer learning strategy combining freezing and fine-tuning is implemented: the core self-attention and feed-forward network layers of the LLM are frozen to preserve general-purpose knowledge acquired during pre-training, while the positional encoding and layer normalization modules are selectively fine-tuned to enhance the model’s ability to characterize temporal patterns specific to production dynamics. The resulting model, termed GPT4TS, is systematically evaluated on real-world production data from a marine carbonate gas reservoir in the Sichuan Basin. Experimental results show that for wells with long production histories, GPT4TS significantly outperforms the conventional LSTM model. Under univariate input, the mean absolute percentage error (MAPE) is reduced by 18.573% on average; under multivariate input, the MAPE reduction reaches 35.610%, demonstrating its superior capability in modeling complex trends and leveraging multi-variable synergies. However, for newly commissioned wells with short production histories, insufficient data hinders effective fine-tuning, leading to lower prediction accuracy compared to LSTM. This study not only validates the potential of large language models in petroleum production forecasting but also highlights their strong dependence on historical data length, providing both theoretical insights and practical guidance for model selection in real-world engineering applications.

In the process of petroleum exploration and development, long-term accumulated documents contain a large amount of engineering knowledge and practical experience, and these materials are of great significance for the scientific development of oilfields and production decision-making. However, such information is mostly preserved in multimodal and unstructured forms such as textual descriptions, data tables, and illustrative figures, lacking a unified structured representation, which leads to low efficiency in retrieval and utilization, and makes the knowledge difficult to be systematically applied. Traditional information retrieval methods have limitations in dealing with complex cross-paragraph and multimodal corpora, and relying only on large-scale language models for question answering is prone to hallucinations and context fragmentation, which cannot meet the requirements of professional fields for accuracy and interpretability. To solve this problem, this paper, based on Microsoft’s open-source graph retrieval-augmented generation framework, constructs a graph retrieval-augmented intelligent question answering system for reservoir geology. Aiming at the linguistic complexity, hierarchical diversity, and structural heterogeneity of oilfield documents, three optimization methods were applied: a logical structure-based segmentation method was used to identify heading hierarchies and numbering rules to achieve reasonable division of semantic units, thereby avoiding semantic fragmentation in entity and relation extraction; a prompt optimization mechanism combined with the terminology system of reservoir geology was applied to improve the accuracy and completeness of entity and relation recognition and extraction, and to reduce errors and omissions; and a multimodal output mechanism was employed to realize the linkage of textual answers with relevant figures and tables through embedding matching, so that the results not only have linguistic coherence but also obtain visual evidence support, enhancing the interpretability and credibility of the answers. In the experimental part, a comprehensive report of about ninety thousand characters from a typical offshore oilfield was used as the data source to construct a knowledge graph and carry out system evaluation. Compared with unoptimized methods and the original framework, the results show that the optimized system has achieved significant improvements in factuality, answer relevance, context precision, and context recall. The improvement in factuality and answer relevance indicates that the system can more accurately generate answers that conform to facts and question intent, while the improvement in context indicators shows that it has greater advantages in cross-paragraph integration and multimodal association. The research results show that this system exhibits higher accuracy and reliability in knowledge extraction, organization, and application, has good engineering adaptability and scalability, not only provides a feasible solution for the structured management and intelligent utilization of complex oilfield knowledge, but also offers references and practical experience for the application of large language models in petroleum engineering and other highly specialized fields.

The rapid advancement of generative artificial intelligence (AI), exemplified by models like the DeepSeek series, has substantially lowered the application barrier for large language model (LLM) technology. This progress injects new intelligent capabilities into the field of oil and gas exploration and development, a domain highly dependent on expertise and data-intensive analysis. However, the practical capabilities and implementation pathways of large language models in vertical industry scenarios remain unclear. This study comprehensively and systematically evaluates the multidimensional application capabilities of the DeepSeek model series within this specific domain. A comprehensive six-dimensional quantitative assessment framework was designed to evaluate core competencies, including foundational domain knowledge, complex reasoning, computational proficiency, multimodal processing, performance on open-ended and innovative problems, and professional task execution capabilities. The testing results indicate that LLMs demonstrate exceptional performance in terms of breadth of foundational knowledge coverage and in handling open-ended, innovative questions, revealing strong domain-specific comprehension and application and significant potential for interdisciplinary knowledge integration. However, several critical limitations were identified. The models exhibit hallucination risks when processing specific instances and data, display a lack of sufficient granularity in logical reasoning within complex problem-solving scenarios, and show deficiencies in the accuracy and efficiency of intricate numerical computations. Furthermore, distinct capability boundaries were observed, particularly in multimodal processing - especially the generation and interpretation of professional diagrams and images -, in the operation of specialized software, and responsiveness to real-time engineering demands. To address these identified limitations, this paper proposes an integrated four-dimensional technical pathway to facilitate intelligent transformation. This cohesive strategy comprises: 1) a dynamic knowledge fusion mechanism based on Retrieval-Augmented Generation (RAG) to mitigate knowledge obsolescence and data hallucinations; 2) a knowledge-graph-driven reasoning engine designed to enhance logical reasoning precision for complex problems; 3) a specialized software collaboration architecture that extends the model’s operational boundaries via API gateways integrating domain-specific tools; and 4) an Agent-empowered engineering system for the automated decomposition and execution of complex tasks. The research further delves into key technical challenges, such as the construction of vertical domain knowledge graphs, software ecosystem interoperability, and real-time decision-making by AI agents, proposing targeted directions for technological breakthroughs. In conclusion, the deep integration of LLMs into the oil and gas sector necessitates tight coupling with domain knowledge engineering, specialized software ecosystems, and edge computing technologies. The transition from point solutions to systemic intelligence should be gradual, starting with focused scenario development, overcoming core technical bottlenecks, and ultimately realizing the synergistic application of “Data-Knowledge-Tools.”