Four Methods to Estimate Minimum Miscibility Pressure of CO₂‐Oil Based on Machine Learning

Abstract

Summary of main observation and conclusion CO2 flooding accounts for a considerable proportion in gas flooding. Using CO2 as a gas displacement agent is benefit for enhanced oil recovery (EOR), and the alleviation of the greenhouse effect by the permanent storage of CO2 in the crust. Minimum miscibility pressure (MMP) of CO2‐oil is a key factor affecting EOR, which determines the yield and economic benefit of crude oil recovery. Therefore, it is of great importance to use fast, accurate and cheap prediction methods for MMP estimation. In the present study, to evaluate the reliability of four recently developed prediction models based on machine learning (i.e., neural network analysis (NNA), genetic function approximation (GFA), multiple linear regression (MLR), partial least squares (PLS)), 136 sets of data are selected for calculation via outlier analysis from 147 sets of data. Afterwards, we compared the four models with existing prediction models from the literature. The analysis of correlation coefficients and multiple error functions shows that the four models can solve the MMP prediction problem well, and the model using intelligent algorithm has a higher prediction accuracy than the simple linear model. Besides, intelligent methods based on similarity algorithm have little difference from each other. Finally, a sensitivity analysis was conducted.

Research on Minimum Miscible Pressure Between Crude Oil and Supercritical Carbon Dioxide System in Ultra-Low Permeability Reservoir by the Long-Slim-Tube Experiment Method

“…The generally accepted criterion for miscible flooding in slim tube displacement experiment is that the recovery ratio is greater than 90% when the injection volume is 1.2 times the pore volume, and the displacement efficiency does not increase significantly with the increase of displacement pressure (Choubineh et al, 2016;KarimanMoghaddam and SaeediDehaghani, 2017;Ma et al, 2018;Ghorbani et al, 2019;Li et al, 2019;Yu et al, 2020). In Table 4, when the injected CO 2 volume was 1.2 PV, the recovery ratio increased with increasing experimental pressure.…”

Review of Density Measurements and Predictions of CO2–Alkane Solutions for Enhancing Oil Recovery

“…Specifically, the saturated density of CO 2 –alkane is often predicted using the PR EOS when the solution reaches equilibrium. In addition to classical EOSs and empirical equations, with the development of computational technology, some state-of-the-art simulation methods, such as molecular dynamics (MD) − and artificial neural network (ANN), − are gradually used to investigate the properties of substances. Owing to the limited experimental database and complexity of these systems, there are no advanced simulation techniques to predict the density of CO 2 –alkane solutions.…”

Research for reducing the Minimum Miscible Pressure of crude oil and carbon dioxide by injecting citric acid isobutyl ester

“…The methods of predicting the MMP generally include laboratory experiment and theoretical calculation [50][51][52][53][54]. Laboratory experiment methods include long-slim-tube displacement experiment method, interfacial tension method, Rising Bubble Apparatus (RBA) method and vapor density method.…”