Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

Abstract

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.

Performance Prediction and Heating Parameter Optimization of Organic-Rich Shale In Situ Conversion Based on Numerical Simulation and Artificial Intelligence Algorithms

“…The ranking results for the importance of the feature factors are shown in Figure 8 . By setting the threshold value to 0.075, 28 the main controlling factors of objective functions were screened out and summarized, as shown in Table 5 . The screened factors influencing the reservoir temperature include matrix permeability, formation depth, natural fracture permeability, Sc-CO 2 injection rate, initial kerogen concentration, Sc-CO 2 injection temperature, and thickness.…”

“…On the contrary, the artificial intelligence method helps to greatly reduce the simulation time and this method is widely used in hydrocarbon production prediction and development parameter optimization in the petroleum industry. − Chen et al applied the ANN to predict the diffusion coefficients of CO 2 in porous media . Al-Khafaji et al used machine learning methods to achieve rapid prediction for the minimum miscible pressure of CO 2 , showing superior accuracy and adaptability than traditional methods .…”

Quantitative Analysis of Coal Quality by a Portable Laser Induced Breakdown Spectroscopy and Three Chemometrics Methods

“…Compared with other algorithms, a particle swarm optimization algorithm retains the global search strategy for population, and its unique memory enables it to dynamically track the current search situation and adjust its search strategy. Since the velocity-displacement model adopted by the particle swarm optimization algorithm is simple to operate, it is used in this paper to optimize the parameters of the SVR model [30,31]. The initial parameters of the particle swarm optimization algorithm were set as c 1 = 1.5, c 2 = 1.7, particle population as 20 and iteration time as 100.…”

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

“…The applicability of ML in various domains of petroleum engineering has attracted extensive attention and interest. Vector autoregression (Zhang and Jia, 2021), support vector regression (Huang et al, 2021;Masoud et al, 2020), random forest (Bhattacharya et al, 2019;Xue et al, 2021) and artificial neural network (Liu et al, 2021b;Negash and Yaw, 2020;Zhou et al, 2021b) are used to predict oil and gas production. However, these traditional ML methods do not take into account the trend of production over time and the correlation between the data before and after.…”