Search

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.

Shale oil reservoirs, as an unconventional hydrocarbon resource, have the potential to substitute conventional hydrocarbon resources and alleviate energy shortages, making their exploration and development critically significant. However, due to the low permeability and the development of nanopores in shale reservoirs, shale oil production is challenging and recovery efficiency is low. During the imbibition stage, fracturing fluid displaces the oil in the pores primarily under capillary forces, but the complex pore structure of shale reservoirs makes the imbibition mechanism unclear. This research studies the imbibition flow mechanism in nanopores based on the capillary force model and two-phase flow theory, coupled with numerical simulation methods. The results indicated that within a nanopore diameter range of 10–20 nm, increasing the pore diameter leads to a higher imbibition displacement volume. Increased pressure can enhance the imbibition displacement, but the effect diminishes gradually. Under the water-wet conditions, the imbibition displacement volume increases as the contact angle decreases. When the oil phase viscosity decreases from 10 mPa·s to 1 mPa·s, the imbibition displacement rate can increase by 72%. Moreover, merely increasing the water phase viscosity results in only a 5% increase in the imbibition displacement rate. The results provide new insights into the imbibition flow mechanism in nanopores within shale oil reservoirs and offer a theoretical foundation and technical support for efficient shale oil development. [ABSTRACT FROM AUTHOR]

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]