The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention for its potential to address these challenges. This review explores the current state of AGI applications in the oil and gas sector, focusing on key areas such as data analysis, optimized decision and knowledge management, etc. AGIs, leveraging vast datasets and advanced retrieval-augmented generation (RAG) capabilities, have demonstrated remarkable success in automating data-driven decision-making processes, enhancing predictive analytics, and optimizing operational workflows. In exploration, AGIs assist in interpreting seismic data and geophysical surveys, providing insights into subsurface reservoirs with higher accuracy. During production, AGIs enable real-time analysis of operational data, predicting equipment failures, optimizing drilling parameters, and increasing production efficiency. Despite the promising applications, several challenges remain, including data quality, model interpretability, and the need for high-performance computing resources. This paper also discusses the future prospects of AGI in oil and gas reservoir development, highlighting the potential for multi-modal AI systems, which combine textual, numerical, and visual data to further enhance decision-making processes. In conclusion, AGIs have the potential to revolutionize oil and gas reservoir development by driving automation, enhancing operational efficiency, and improving safety. However, overcoming existing technical and organizational challenges will be essential for realizing the full potential of AI in this sector.

Relative permeability is a critical parameter governing multiphase fluid flow through porous media, significantly impacting recovery efficiency and CO₂ sequestration potential in geological reservoirs. Accurately evaluating relative permeability in heterogeneous reservoirs remains challenging due to spatially variable porosity and permeability distributions. This study presents a novel intelligent prediction approach for evaluating water-CO₂ relative permeability in heterogeneous porous media by integrating fluid properties, heterogeneity characteristics, and relative permeability measurements from uniform porous media. We established a comprehensive training dataset through systematic micromodel experiments that captured various heterogeneity patterns and fluid conditions. Using this dataset, we developed an Artificial Neural Network (ANN) model that achieved exceptional accuracy with a Mean Squared Error below 0.0025. The model was then applied to predict relative permeability in heterogeneous reservoirs using site-specific relative permeability data obtained from core experiments as input parameters. To validate our approach, we incorporated the predicted relative permeability values into Computer Modelling Group (CMG) reservoir simulations of CO₂ sequestration in saline aquifers. The simulation results demonstrated strong agreement with published literature, confirming the model’s predictive capability. This work provides a practical, efficient, and reliable methodology for predicting relative permeability in heterogeneous reservoirs, addressing a significant challenge in reservoir characterization and flow modeling.

China’s liquefied natural gas (LNG) ex-factory prices are affected by market-oriented reforms, supply structure changes, and diverse heterogeneous factors, presenting strong nonlinearity, nonstationarity, multiscale coupling, and high-frequency disturbances. Traditional models fail to capture such complicated dynamics, bringing difficulties to industrial capacity allocation, procurement, and inventory management. This paper proposes a CEEMDAN–LSTM–MiniMax hybrid framework combining signal decomposition, ensemble learning, and large language model semantic reasoning to realize closed-loop synergy of numerical forecasting and semantic decision-making. CEEMDAN decomposes raw price series, and LSTM models each component with validation set-based combinatorial optimization for ensemble prediction. The embedded MiniMax LLM extracts information from policies, market sentiment, and supply–demand texts to calibrate forecasts and support human–machine collaborative pricing, constructing a “numerical drive–semantic refinement–decision output” loop. Empirical results show MAE = 29.424, MAPE = 0.744%, RMSE = 38.557, and R² = 0.991. The model outperforms single LSTM and conventional CEEMDAN ensemble models in accuracy and adaptability to extreme price volatility.