Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their high complexity and low interpretability can hinder their practical application. This study proposes using interpretable ML solutions, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to provide explicit explanations of the ML prediction model. The study uses data from the Eastern Sulige tight gas field in the Ordos Basin, China, containing various geological and engineering factors. The results show that the gradient boosting decision tree model exhibits superior predictive performance compared to other ML models. The global interpretation using SHAP provides insights into the overall impact of these factors, while the local interpretation using SHAP and LIME offers individualized explanations of well productivity predictions. These results can facilitate improvements in well operations and field development planning, providing a better understanding of the underlying physical processes and supporting more informed and effective decision-making. Ultimately, this study demonstrates the potential of interpretable ML solutions to address the challenges of forecasting well productivity in tight gas reservoirs and enable more efficient and sustainable gas production.

Shale gas has revolutionized the global energy supply, underscoring the importance of robust production forecasting for the effective management of well operations and gas field development. Nonetheless, the intricate and nonlinear relationship between gas production dynamics and physical constraints like shale formation properties and engineering parameters poses significant challenges. This investigation introduces a hybrid neural network model, GRU-MLP, to proficiently predict shale gas production. The GRU-MLP architecture can capture sequential dependencies within production data as well as the intricate nonlinear correlations between production and the governing constraints. The proposed model was evaluated employing production data extracted from two adjacent horizontal wells situated within the Marcellus Shale. The comparative analysis highlights the superior performance of the GRU-MLP model over the LSTM and GRU models in both short-term and long-term forecasting. Specifically, the GRU model’s mean absolute percentage error of 4.7% and root mean squared error of 120.03 are notably 66% and 80% larger than the GRU-MLP model’s performance in short-term forecasting. The accuracy and reliability of the GRU-MLP model make it a promising tool for shale gas production forecasting. By providing dependable production forecasts, the GRU-MLP model serves to enhance decision-making and optimize well operations.

Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry.

Accurate numerical modeling of multiphase flow in subsurface oil and gas reservoirs is critical for optimizing hydrocarbon recovery. However, traditional physics-based algorithms face substantial computational hurdles due to the need for fine grid resolution and the inherent geological heterogeneity. To overcome these challenges, data-driven surrogate models solving the flow governing partial differential equations (PDEs) offer a promising alternative to enhance the efficiency of hydrocarbon production operations. In this study, we employ the Fourier Neural Operator (FNO) to extract spectral information from the reservoir properties, thereby facilitating the solution of coupled porous flow PDEs. Our focus is on two-phase flow dynamics, specifically exploring how water injection enhances reservoir pressure and displaces oil. This scenario involves solving a set of nonlinearly coupled PDEs with highly heterogeneous coefficients. Numerical results demonstrate that the developed FNO accurately predicts the reservoir pressure distributions. We further observe that the FNO's zero-shot super-resolution capability is sensitive to abrupt local changes in the reservoir pressure near injection and production wells. To enhance its accuracy, we propose a multi-fidelity FNO model that exhibits better adaptability across various grid configurations. After moderate training on graphics processing units (GPUs), the FNO achieves a speedup of three orders of magnitude compared to traditional numerical PDE solvers. Our experiments confirm the FNO's potential to replace repetitive physics-based simulations, significantly advancing computational efficiency in the uncertainty quantification of reservoir performance.

CO₂ geological sequestration in shale is a promising method to mitigate global warming caused by greenhouse gas emissions as well as to enhance the gas recovery to some degree, which effectively addresses the problems related to energy demand and climate change. With the data from the New Albany Shale in the Illinois Basin in the United States, the CMG-GEM simulator is applied to establish a numerical model to evaluate the feasibility of CO₂ sequestration in shale gas reservoirs with potential enhanced gas recovery (EGR). To represent the matrix, natural fractures, and hydraulic fractures in shale gas reservoirs, a multicontinua porous medium model will be developed. Darcys and Forchheimers models and desorption-adsorption models with a mixing rule will be incorporated into the multicontinua numerical model to depict the three-stage flow mechanism, including convective gas flow mainly in fractures, dispersive gas transport in macropores, and CH₄-CO₂ competitive sorption phenomenon in micropores. With the established shale reservoir model, different CO₂ injection schemes (continuous injection vs. pulse injection) for CO₂ sequestration in shale gas reservoirs are investigated. Meanwhile, a sensitivity analysis of the reservoir permeability between the hydraulic fractures of production and injection wells is conducted to quantify its influence on reservoir performance. The permeability multipliers are 10, 100, and 1,000 for the sensitivity study. The results indicate that CO₂ can be effectively sequestered in shale reservoirs. But the EGR of both injection schemes does not perform well as expected. In the field application, it is necessary to take the efficiency of supplemental energy utilization, the CO₂ sequestration ratio, and the effect of injected CO₂ on the purity of produced methane into consideration to design an optimal execution plan. The case with a permeability multiplier of 1,000 meets the demand for both CO₂ sequestration and EGR, which indicates that a moderate secondary stimulation zone needs to be formed between the primary hydraulic fractures of injection and production wells to facilitate the efficient energy transfer between interwell as well as to prevent CO₂ from channeling. To meet the demand for CO₂ sequestration in shale gas reservoirs with EGR, advanced and effective fracking is essential.