Comprehensive Review of the Determination and Reduction of the Minimum Miscibility Pressure during CO₂ Flooding

Abstract

With the increasing oil demand, more attention has been paid to enhancing oil recovery in old oil fields. CO 2 flooding is popular due to its high oil displacement efficiency and ability to reduce greenhouse gas emissions. Laboratory experiments and on-site application cases have shown that the minimum miscibility pressure has a greater impact on CO 2 flooding than other factors. If the reservoir pressure is below the minimum miscible pressure, then there is CO 2 immiscible flooding. Both theoretical analysis and experimental results show that the recovery rate of CO 2 miscible flooding is 2−5 times higher than that of immiscible flooding. If the reservoir pressure is increased by water flooding before CO 2 injection, it is easily limited by the physical property parameters. Therefore, accurately determining and effectively reducing the minimum mixing pressure has become the focus of research. Currently, there are two types of methods for determining the minimum miscible pressure: experimental and theoretical methods. The experimental method is generally considered more accurate, including the slim tube test, rising bubble apparatus, and vanishing interfacial tension, etc. However, it is worth noting that the minimum miscibility pressure is dynamically changing, and there will be high economic costs if measured repeatedly through experimental methods during reservoir development. Therefore, it is recognized that the minimum mixing pressure can be determined at any time using theoretical calculation of initial data, which will reduce economic and time costs to a high degree. In this paper, the theoretical calculation method is divided into empirical correlation, state equation, and artificial intelligence algorithm. The techniques for reducing the minimum miscibility pressure can be classified into two categories: miscible solvents and surfactant methods. The miscible solvent method can be further divided into monocomponent and polycomponent methods. This paper compares the advantages and disadvantages of the existing techniques for measuring and reducing MMP and selects the best method.

Utilizing Artificial Intelligence Techniques for Modeling Minimum Miscibility Pressure in Carbon Capture and Utilization Processes: A Comprehensive Review and Applications

“…Examples of this category include the studies published by Dehaghani and Soleimani, Alatefi et al., Olofinnika et al, and Al-Khafaji et al Among the prior papers, the recent paper of Alatefi et al is the richest one as it summarizes many previous studies on the application of AI techniques for predicting the MMP of CO 2 – oil systems. A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi A part of a chapter in books devoted to the application of AI techniques in the petroleum industry or gas injection methods, such chapter four of the book published by Hemmati-Sarapardeh et al and the chapter written by Hadavimoghaddam et al …”

“…A special section in review papers related to CCSU or the application of AI techniques in the oil and gas industry: some recent review papers included a special section or certain paragraphs devoted to discussing the implementation of AI techniques for modeling the MMP of the systems CO 2 – oil. Examples of this category include the review papers published by Du et al, Ng et al, Song et al, and Mao and Jahanbani Ghahfarokhi …”