Tight oil fields are affected by factors such as geology, technology, and development, so it is difficult to directly obtain an accurate recovery rate. The accurate prediction of the recovery rate is very important for measuring reservoir development effects and dynamic analysis. Traditional tight oil recovery predictions are obtained by conventional formula calculations and curve fitting, which are less applicable and very different from actual conditions. Machine learning can make accurate predictions based on a large amount of data, so it is used to predict the recovery rate of tight oil reservoirs. The recovery rate of 200 wells in M tight oil reservoirs ranges widely between 8.8 and 27.6%, with more than 14 factors affecting the recovery rate, and the overall declining rule is not clear. Therefore, this research combines the production data of horizontal wells with random forest, support vector regression (SVR), and other methods, establishing recovery prediction models to gain more accurate recovery predictions. First, the Pearson correlation coefficient and the random forest (RF) machine learning method are used to measure and calculate the degree of nonlinear influence of factors on oil well recovery. Second, SVR and optimization of support vector regression by particle swarm (PSO-SVR) recovery prediction models are developed and tested, with 75% of the data being used to train SVR and PSO-SVR recovery prediction models and 25% to verify the model. Third, the accuracy of the results of these two SVR oil recovery prediction models is compared, suggesting that when the data are scarce, the optimized model is more accurate than the unoptimized one by 10.85%. Thus, this model can assure a relatively more accurate prediction of oil recovery. Machine learning recovery prediction, being more accurate and applicable, enables the data of factors such as construction and production systems to be optimized in the future, enhancing the oil recovery rate.

CO₂ enhanced oil recovery (EOR) has proven its capability to explore unconventional tight oil reservoirs and the potential for geological carbon storage. Meanwhile, the extremely low permeability pores increase the difficulty of CO₂ EOR and geological storage processing in the actual field. This paper initiates the ultrasonic-assisted approach to facilitate oil–gas miscibility development and finally contributes to excavating more tight oils. Firstly, the physical properties of crude oil with and without ultrasonic treatments were experimentally analyzed through gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR) and viscometer. Secondly, the oil–gas minimum miscibility pressures (MMPs) were measured from the slim-tube test and the miscibility developments with and without ultrasonic treatments were interpreted from the mixing-cell method. Thirdly, the nuclear-magnetic resonance (NMR) assisted coreflood tests were conducted to physically model the recovery process in porous media and directly obtain the recovery factor. Basically, the ultrasonic treatment (40 KHz and 200 W for 8 h) was found to substantially change the oil properties, with viscosity (at 60 °C) reduced from 4.1 to 2.8 mPa·s, contents of resin and asphaltene decreased from 27.94% and 6.03% to 14.2% and 3.79%, respectively. The FTIR spectrum showed that the unsaturated C-H bond, C-O bond and C≡C bond in macromolecules were broken from the ultrasonic, which caused the macromolecules (e.g., resin and asphaltenes) to be decomposed into smaller carbon-number molecules. Accordingly, the MMP was determined to be reduced from 15.8 to 14.9 MPa from the slim-tube test and the oil recovery factor increased by an additional 11.7%. This study reveals the mechanisms of ultrasonic-assisted CO₂ miscible EOR in producing tight oils.

With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy–correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection–production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5–59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.

With the increasing energy consumption and the exhaustion of conventional oil reservoir, CO₂ flooding is becoming one of the key technologies to improve the oil production of tight reservoirs. Not only oil production of CO₂ flooding but also its main influencing parameters and quantitative evaluation have attracted people’s attention in recent years. The experimental and numerical techniques are the main approach to evaluate the production and influencing parameters. However, it is expensive and costs a lot of time to use those approaches. To solve the above problems, in this paper, a comprehensive integrated hierarchy and correlation model is initially established to evaluate the production of CO₂ flooding and confirm the main influencing parameters. Specifically speaking, the Analytic Hierarchy Process (AHP) and Grey Relationship Analysis (GRA) are used to evaluate the main influencing parameters of oil production for the CO₂ flooding process in tight oil reservoirs, respectively. Subsequently, the Rank-Sum Ratio (RSR) is adopted to combine the Analytic Hierarchy Process with Grey Relationship Analysis to acquire the comprehensive weight and ensure the main influencing parameters. The results show that the comprehensive weight of permeability, variation coefficient, sand injection, and reservoir thickness is 0.1157, 0.0937, 0.0914, and 0.0895 and those parameters play a extremely important role in the production of CO₂ flooding. The model is applied to eight production wells and the rank–sum ratio is 2.3 to 6.6. The arrange regular of comprehensive results evaluated by the rank–sum ratio has a very consistent relationship with that of production. It is convenient and accurate to evaluate the production-related parameters by using the comprehensive integrated-hierarchy model, and this study provides a functional method to analyze the production performance.