The oil recovery factor from shale oil remains low, about 5 to 7% of the oil in place. How to increase oil recovery from shale oil reservoirs is attracting more and more attention. CO₂ huff-and-puff was historically considered one of the best approaches to improve the oil rate. Most previous simulation studies have been based on dual porosity, but simulation results from dual-porosity models have not been as accurate as discrete fracture models in composition modeling. This study proposes a new model that integrates the embedded discrete fracture model and the dual-porosity, dual-permeability model (DPDP). The newly developed method could explicitly describe large-scale fractures as flow conduits by embedded discrete fracture modeling and could model the flow in small- and medium-length fractures by DPDP modeling. In this paper, we first introduce four different non-neighboring connections and the way to calculate the transmissibility among different media in the new model. Then, the paper compares the performance of the new method, discrete fracture modeling, DPDP modeling, and embedded discrete fracture modeling for production from oil reservoirs. Following, the paper carries out a series of simulations to analyze the effects of hydraulic fracture stages, hydraulic fracture permeabilities, and natural fracture permeabilities on the CO₂ huff-and-puff process based on the new method. In addition, the injection cycle and soaking time are investigated to optimize CO₂ huff-and-puff performance. This study is the first to integrate embedded discrete fracture modeling with DPDP modeling to simulate the CO₂ huff-and-puff process in a shale oil reservoir with natural fractures. This paper also provides detailed discussions and comparisons on the integrated strategy, embedded discrete fracture modeling, discrete fracture modeling, and dual-porosity, dual-permeability modeling in the context of fracture simulation with a compositional model. Most importantly, this study answers the question regarding how fractures affect CO₂ huff-and-puff and how to optimize the CO₂ huff-and-puff process in a reservoir with natural fractures.

To obtain a reliable production forecast, one has to establish a geological model with well logs and seismic data. The geological model usually has to be upscaled using certain upscaling techniques. Then, a dynamic reservoir model is constructed with another dataset, including completion data, production data, fluid properties, and relative permeability curves. At last, the dynamic model needs to be validated by a history matching process. This approach is data-intensive, time-consuming, and often not rigorously accomplished due to the lack of skillset and time. In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. Overall coefficients of determination () of the three Arps decline curve factors were 0.966, 0.990, and 0.945. The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. As a result of the ML-DCA regression model, a complete understanding can be established of the impact of reservoir properties on the DCA model. The proposed ML-DCA model not only provides a quick and robust method for petroleum engineers to estimate production performance for unconventional reservoirs from reservoir and completion properties without full-field geocellular modeling but also can be used to optimize the completion and operation parameters for wells of interest.

Gas injection has received increasing attention as one of the key technologies to enhance oil recovery. When gas is dissolved in crude oil, it will accelerate the flow of crude oil by reducing the density, viscosity, interfacial tension (IFT), and other properties of crude oil, so IFT is one of the main factors affecting the recovery of the gas drive. The interfacial properties of CH₄, one of the principal associated hydrocarbon gases, with crude oil remain unclear. In this study, molecular dynamics (MD) simulations were used to determine the IFTs of pure and impure CH₄ with n-decane as well as the IFTs of the ternary systems CH₄ + n-hexane + n-decane and CH₄ + n-decane + n-nonadecane. Additionally, investigating factors including pressure, temperature, gas composition, and crude oil composition reveals the mechanisms affecting the interfacial properties of CH₄ and crude oil. The results demonstrate that CO₂ significantly lowers the IFT of CH₄ + n-decane; the effect of crude oil components on IFT varies with the properties of the crude oil and, generally speaking, IFT is greater for crude oils containing heavy components than for those containing light components; the effect of temperature on the IFT of the CH₄ + n-decane system is more pronounced at low pressure and decreases with increasing pressure. This study contributes to understanding the behavior of CH₄ and oil systems in the formation and could be used to enhance the oil recovery technology.

As global demand for oil continues to rise, conventional reservoir extraction methods have proven insufficient to meet current production needs. To enhance oil recovery rates, researchers have investigated more economically viable extraction techniques. Among these, polymer flooding and CO2 injection have gained recognition as cost-effective methods. However, despite extensive research, the effectiveness of Polymer-Alternating-Gas (PAG) injection in field-scale applications remains ambiguous. This study employs the commercial reservoir simulation software CMG-GEM to construct a three-dimensional geological model of the Daqing X Block, based on detailed geological data. Using an orthogonal experimental design approach, the injection parameters for the PAG process are optimized through numerical simulation to determine the optimal injection scheme for the Daqing X Block. Furthermore, the study conducts a comprehensive comparison of the development performance and oil displacement efficiency of Water flooding, Polymer flooding, Water-Alternating-Gas (WAG), and PAG injection methods. The results indicate that PAG injection can significantly enhance the recovery factor and demonstrates favorable application potential in the Daqing X Block reservoir. This research provides critical theoretical insights and practical guidance for the implementation of Polymer-Alternating-Gas injection in oil field development.