Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

Ensemble learning algorithms show good forecasting performances for financial distress in many studies. Despite considering the feature selection and feature importance procedures, most overlook imbalanced data handling. This study proposes the Easyensemble method based on undersampling and combines it with ensemble learning models to predict financial distress. The results show that Easyensemble sampling presents better forecasting performance than SMOTE sampling. We subsequently conduct Permutation Importance (PIMP), Recursive Feature Elimination (RFE), and partial dependence plots, and the experimental results show that the feature selection procedure can effectively reduce the number of indicators without affecting the prediction accuracy, improve the prediction efficiency as well as save processing time. In addition, the indicators from profitability, cash flow, solvency, and structural ratios are essential in predicting financial distress.

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.

Precise forecasting of spatiotemporal CO₂ plume evolution in deep saline aquifers is imperative for ensuring the long-term integrity and efficacy of carbon capture and storage. However, a persistent dichotomy exists in current modeling paradigms: high-fidelity numerical simulations suffer from computational intractability, while prevailing data-driven surrogates often lack embedded physical constraints, failing to capture long-term temporal dependencies and fine-scale geological heterogeneity. To bridge this gap, this study proposes a novel Physics-Synergized Deep Learning Framework designed for CO₂ plume temporal forecasting and spatial super-resolution modeling. First, we introduce a hybrid Bidirectional-Long-Short-Term-Memory-Transformer architecture that harmonizes local sequential inductive biases with global attention mechanisms to capture multi-scale pressure dynamics. A Physics-Guided Transfer Learning strategy is then employed to enforce hydrodynamic interdependencies, transferring the learned manifold of pressure fields to constrain complex saturation transport. Second, to address grid-scale mismatches, we develop a Physics-Informed Graph Attention Network (PI-GAT) for super-resolution modeling. This module leverages topological graph structures and governing laws to act as a learned super-resolution operator, reconstructing high-fidelity plume migration details from coarse-grid inputs. Comprehensive validation on the Johansen formation demonstrates the framework’s superior performance. The proposed method achieves a tenfold increase in computational efficiency relative to conventional finite element simulations for both temporal prediction and spatial reconstruction, effectively decoupling prediction latency from grid complexity. This efficiency gain is realized without compromising physical rigor: the model maintains high accuracy in coupled reservoir dynamics (Pressure RMSE < 15,000 Pa; Saturation R ² > 0.96) and ensures thermodynamic consistency during spatial downscaling ( R ² > 0.90). Furthermore, the framework exhibits robust extrapolation capabilities under significant distributional shifts, including extended time horizons and varying injection scenarios ( R ² > 0.90). This work establishes a resilient, physics-aware computational paradigm for high-fidelity real-time monitoring and risk assessment in large-scale geological sequestration.