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Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is ​​constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.

Based on the tight oil reservoir conditions of Lucaogou Formation in Jimusar Sag, Xinjiang, this paper conducts a full-scale characterization experiment of pore structure and designs the optimization numerical simulation of the development scheme based on the geological model combination with the fluid occurrence state. A comparative study on the development methods of tight reservoirs shows that the enhanced oil recovery effect of CO2 flooding is obviously better than that of CH4 flooding and water flooding. When the production bottom hole pressure is lower than the formation fluid saturation pressure, changing the production bottom hole pressure has little impact on the productivity of CO2 flooding in tight reservoirs. The recovery factor increases with the increase of injection rate, but when the injection rate is higher than 15,000 m³/d, the increase of oil recovery and the oil change rate decrease obviously; The complex fractures near the well can help to increase the swept volume of CO2 flooding, while the complex fractures far away from the well will cause channeling, which is not conducive to production. Combined with the occurrence state of the fluid, it is obtained that in the process of CO2 displacement, when the adsorption is considered, when the adsorption components are the same, with the increase of the adsorption capacity, the recovery factor decreases; When the adsorption capacity is constant, the higher the proportion of heavy components is, the lower the recovery factor is; With the increase of adsorption capacity, the permeability decreases more. The fluid occurrence state in tight oil reservoirs is very different from that in conventional reservoirs, and the adsorption phase accounts for a larger proportion, which seriously affects the flow capacity of the fluid during the development process. However, conventional numerical simulation rarely considers the influence of fluid occurrence state.

Shale oil has become a crucial unconventional resource, bolstering energy supply security, and it is important to accurately predict shale oil production dynamics. However, traditional prediction methods are faced with the challenges of large data fluctuations and numerous interference factors, which make accurate prediction difficult. This paper introduces a deep learning approach, specifically a physical constraint-based convolutional neural network combined with long short-term memory and attention mechanism (CNN-LSTM-AM), to predict shale oil production dynamics. Initially, the correlation analysis method is used to analyze the correlation strengths of features with the prediction target, and the data that are most strongly correlated with the daily oil production are screened out and preprocessed. Key features are then extracted; that is, the CNN layer's output is fed into the LSTM layer, the output of which informs a fully connected layer for time-series production predictions. The model integrates an AM to concentrate on vital data aspects. In the "five-linear flow" formula, the imbibition is hard-coded into the shale oil production prediction model. Physical constraints are introduced into the model. Data driven and empirical formulas are used to introduce a loss function as a constraint condition in the training process of a machine learning model to improve the interpretability and predictive ability of the model. During the tuning phase, Bayesian optimization is used to fine-tune the model's hyperparameters. Shapley additive explanation and accumulated local effects analyses are used to further assess and quantify the significance of the essential parameters, thus optimizing the prediction effectiveness. [ABSTRACT FROM AUTHOR]

The development of carbon capture, utilization, and storage technologies has notably advanced CO2-enhanced oil recovery (EOR) in shale oil reservoirs, which are characterized by abundant nanopores. These nanopores induce unique phase behaviors in hydrocarbons, challenging traditional phase equilibrium calculation methods. This paper presents a novel three-phase thermodynamic model (vapor–liquid-adsorbed three-phase equilibrium calculation) that addresses these challenges by considering the nanopore capillary pressure, critical parameter transitions, and material exchange between the adsorbed and bulk phases. Grounded in the multicomponent Langmuir–Freundlich adsorption equation and the Peng Robinson equation of state, this model is integrated into the MATLAB Reservoir Simulation Toolbox using an embedded discrete fracture model framework, enabling detailed study of CO2 and hydrocarbon phase behaviors within shale oil nanopores. The results reveal that there are significant nano-constrained effects on multicomponent fluid phase behavior, particularly in pores smaller than 20 nm, leading to notable changes in bubble and dew point pressures, as well as critical condensation pressures and temperatures. CO2 injection further complicates the system, enhancing interactions and expanding the coexistence region of the liquid and gas phases on the pressure–temperature diagram, especially across varying pore sizes. Optimization research on CO2 huff and puff technical parameters for shale oil reservoirs suggests the following optimal settings: a CO2 injection rate of 100 t/day, a shut-in time of 30 days, and six huff and puff cycles. The results of this study offer critical insights into CO2-EOR mechanisms in shale oil reservoirs and emphasize the importance of nanopore properties in EOR. [ABSTRACT FROM AUTHOR]

Gulong shale oil is found in a typical continental shale oil reservoir, which is different from marine shale oil reservoirs. The Gulong shale oil reservoir is a pure shale-type oil reservoir with abundantly developed nanoscale pores, making it extremely difficult to unlock fluids. Pressure drive does not easily achieve fluid unlock conditions; thus, it is necessary to utilize imbibition to unlock nanoscale pore fluids. In this study, experiments were conducted on oil displacement by high-speed centrifugal pressure and imbibition under different conditions, respectively, and simulations were used to evaluate the effects of pressure differential drive and imbibition efficiency on the utilization of crude oil following fracturing. Combined with the mixed wettability of the reservoir, the imbibition efficiency was analyzed, and the imbibition efficiency at different soaking stages was evaluated. When the fracturing pressure was higher than the matrix pore pressure, the imbibition efficiency was the most obvious, which was 27.9%. Spontaneous imbibition depending solely on capillary force had poor efficiency, at 16.8%. When the fracturing pressure was lower than the matrix pore pressure, the imbibition efficiency was the lowest, at only 1.3%. It is proposed that strengthening fracture pressure and promoting pressurized imbibition are the keys to improving shale oil development. [ABSTRACT FROM AUTHOR]