The stimulated reservoir volume fracturing development in tight oil reservoirs is characterized by multiscale flow of the reservoir matrix, fracture network, and hydraulic fracture. Therefore, the flow field structure is extremely complex. Multiscale flow characteristics have been revealed through the systematical experiments including the threshold pressure gradient and the stress sensitivity. Based on the theory of elliptical flow, a comprehensive and practical mathematical model of multiregion coupling flow is established to characterize the multiscale flow, and the pressure distribution equation is derived. The calculation method of moving boundary is established to simulate the dynamic supply boundary and the dynamic pressure distribution by using the steady-state sequential replacement method. The characteristics of multiscale flow, multistage development state, and stress sensitivity are considered, especially the different stress sensitivity characteristics in different regions. Finally, the pressure propagation in tight reservoirs is clarified and the influence of matrix permeability, stress sensitivity characteristics, and drawdown pressure on the distance at the dynamic supply boundary are revealed. The research results provide theoretical basis for the development effect evaluation.

Due to the small pore throat characteristics of tight oil reservoirs, their strong capillary pressure makes imbibition replacement an effective development method. Field data has indicated that only a little fracture fluid can flow back and that there is an enhancement in oil recovery with shut-in periods after volume fracturing. A large number of scholars have carried out core-scale experiments on imbibition characteristics, but there has been limited research on the quantitative characterization of the differential pressure and imbibition replacement during counter-current imbibition. At the same time, there was also controversy on the calculation method of the optimal soaking time. In this paper, a mathematical model of oil-water two-phase flow is first established. Then, a mathematical model representing differential pressure and imbibition replacement in tight reservoirs is derived with a diversion function. Based on the saturation equation, Corey relative-permeability curve, and J function, the model is simplified to a mathematical model of spontaneous imbibition in the shut-in periods after volume fracturing. Second, based on the finite difference method, a dynamic solution method for the flow field considering the dynamic capillary force was established, and the variation law of the pressure field and the water saturation field during the soaking time was revealed. The simulation results show that imbibition characteristics are the core of flow field reconstruction, and the differential pressure action can ensure the advancing distance of the fracturing fluid; both of them are not a linear superposition on tight oil development but complement each other and promote each other. Third, according to the growth rate of the imbibition replacement between fractures and the matrix during the soaking time, the calculation method of optimal soaking time was established. Taking the development parameters of the volume fracturing development case in the Ordos Basin into account, a reasonable soaking time was optimized. Finally, we analyzed the optimal soaking time under different conditions, and a chart of optimal soaking time for different initializations was plotted. Such a chart has profound reference significance for engineers, and they can make quick and accurate decisions regarding development and adjustment.

Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO₂ flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO₂ flooding for tight oil reservoir development, establishes a numerical model for CO₂ flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO₂ flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R² values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO₂ flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO₂ flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

After volumetric fracturing, the conventional recovery methods for tight oil reservoirs rely on natural energy depletion. However, the production rate rapidly declines due to their intricate characteristics, such as threshold pressure gradient (TPG), stress sensitivity, and multi-scale porous media. This study plotted permeability loss charts based on stress sensitivity experiments. A pressure distribution equation incorporating TPG and flow in different regions was developed, and production prediction methods for tight oil reservoirs were established. The results revealed the effects of TPG and multi-region flow on pressure distribution, demonstrating the need to control bottom hole pressure (BHP) in tight oil reservoirs. Accordingly, methods to improve tight oil reservoir development were proposed by controlling BHP. Reducing BHP, increasing the imbibition displacement, and improving oil-phase mobility proved conducive to enhanced oil recovery. The development model featuring BHP control for 1000 days with initially rapid and subsequently slower BHP declines demonstrated the highest recovery rate, surpassing depletion development by 7.404%. This research helps us to optimize tight oil reservoir development plans while offering significant practical guidance for developing similar oil reservoirs, which is of the reference value for the industry.

Understanding the reservoir feature changes is essential for optimizing oil exploitation throughout the development lifecycle. This paper proposed an analytical displacement unit method to character the features of water-flooded, low-permeability oil reservoirs. The method hinges the ratio of fluid flux to area and average water saturation, providing a fine description of reservoir dynamics. It has been implemented in a case study of a five-spot waterflooding scheme. The reservoir can be categorized into sixteen distinct unit types, each with specific attributes. This paper delves into the evolution of these displacement units and the key factors that influence their behavior. The findings provide insights into the degree of waterflooding and oil distribution following continuous waterflooding. Furthermore, the proposed method offers a valuable framework for analyzing the development of dominant water flow channels and exploiting the residual oil.

The implementation of CO₂ huff-n-puff in unconventional oil reservoirs represents a green development technology that integrates oil recovery and carbon storage, emphasizing both efficiency and environmental protection. A rational well selection method is crucial for the success of CO₂ huff-n-puff development. This paper initially identifies eight parameters that influence the effectiveness of CO₂ huff-n-puff development and conducts a systematic analysis of the impact of each factor on development effectiveness. A set of factors for well selection decisions is established with seven successful CO₂ huff-n-puff cases. Subsequently, the influencing factors are classified into positive, inverse, and moderate indicators. By using an exponential formulation, a method for calculating membership degrees is calculated to accurately represent the nonlinearity of each parameter’s influence on development, resulting in a dimensionless fuzzy matrix. Furthermore, with the oil exchange ratio serving as a pivotal parameter reflecting development effectiveness, recalibration of weighting factors is performed in conjunction with the dimensionless fuzzy matrix. The hierarchical order of weighting factors, from primary to secondary, is as follows: porosity, reservoir temperature, water saturation, formation pressure, reservoir thickness, crude oil density, crude oil viscosity, and permeability. The comprehensive decision factor and oil exchange ratio exhibit a positive correlation, affirming the reliability of the weighting factors. Finally, utilizing parameters of the Ordos Basin as a case study, the comprehensive decision factor is calculated, with a value of 0.617, and the oil exchange ratio is predicted as 0.354 t/t, which falls between the Chattanooga and Eagle Ford reservoirs. This approach, which incorporates exponential membership degrees and recalibrated weighting factors derived from actual cases, breaks the limitations of linear membership calculation methods and human factors in expert scoring methods utilized in existing decision-making methodologies. It furnishes oilfield decision-makers with a swifter and more precise well selection method.