Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

Abstract

Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP.

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

“…Then, the rest of the data (30%), which was unseen during the training stage, was used to evaluate the model performance. The use of 70 and 30% of the data for training and testing, respectively, was reported by several researchers; ,− therefore, we used these ratios in the current study. Moreover, several evaluation indices were used to evaluate the model’s reliability.…”

Supercritical Fluid Application in the Oil and Gas Industry: A Comprehensive Review

“…This is the pressure at which the complete miscibility of oil and gas (or the pressure at which the mechanism of mixing displacement begins to be realized). There are experimental methods [65][66][67] and correlation formulas [68,69], and scientific work is underway to develop a calculation algorithm, through the use of artificial intelligence [70], to calculate the MMP. The use of artificial intelligence to calculate the MMP has reduced the average absolute error of calculations to 6.6%, whereas when calculating with correlation formulas, calculation errors can be 15-20% [70].…”

Mini Review of Miscible Condition Evaluation and Experimental Methods of Gas Miscible Injection in Conventional and Fractured Reservoirs

“…MMP also can be considered as the minimum pressure at which the interfacial tension (IFT) between oil and injected gas tends to zero. , Therefore, it can be concluded that, in general, the pressure at which two phases are thoroughly mixed in each other is considered as MMP. There are various ways to estimate MMP, such as laboratory methods, using the equation of state (EOS), ,− empirical correlations, mathematical models, molecular dynamic (MD), , and artificial intelligence. − It is necessary to mention that artificial intelligence methods for MMP estimation are divided into three groups, i.e., neural network, deep learning, and machine learning. It is worth emphasizing that none of these mentioned estimation methods are used alone to assess the MMP; instead, in one case, two or more of these methods will be used simultaneously for MMP estimation.…”