AI Approach Advances Predictive Precision in CO2 Minimum Miscibility Pressure
Authors: Carpenter Chris 
Published: January 2025

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 219101, “Advancing Predictive Precision in CO2 Minimum Miscibility Pressure: An Interpretable AI Approach for CO2 EOR and CCUS Applications,” by Fahimeh Hadavimoghaddam, Ufa State Petroleum Technological University and Northeast Petroleum University; Peyman Pourafshary, SPE, Nazarbayev University, and Alexei Rozhenko, Plekhanov Russian University of Economics, et al. The paper has not been peer reviewed.

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The study described in the complete paper develops an explainable data-driven method using five methods—recurrent neural networks, XGBoost, the group method of data handling (GMDH), CatBoost, and genetic programming (GP)—to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure (MMP). Moreover, two robust correlations are developed that can be used for a wide range of parameters. The vast range of features in the data set makes it suitable to study the effects of different parameters on MMP in conditions representative of CO2 enhanced oil recovery and carbon capture, use, and storage.

Introduction
Numerous studies shed light on the development of empirical models used for MMP forecasting. Nevertheless, the accuracy levels of most correlations are inadequate because of the methods used to construct the equations used in the model’s development. Data-driven techniques, however, make use of diverse data to track and determine connections between factors. By integrating diverse input and output procedures for training with the data supplied, these models have the capability to develop highly nonlinear relationships accurately. Most of these learning strategies are straightforward mathematical processes that do not require the application of complicated problem-solving techniques.
The MMP of crude oil/CO2 structures has been explored with various data-driven models during the past decade. Even though several studies have examined the MMP of impure and pure CO2 streams, this area of research remains uncharted.
The principal objective of this research was to combine different techniques and predict MMP with greater precision than existing models. The authors initially assembled an extensive data set consisting of five features derived from various disciplines, each representing a distinct parameter. Following that, the data-driven model using the deep neural network (DNN) algorithm was implemented. Furthermore, GP, gene expression programming (GEP), and GMDH were used to establish an explicit correlation. The model developed in this research possesses a wider array of features and parameters, thereby expanding its applicability to encompass a greater variety of reservoirs. The newly developed algorithms can predict accurately the target according to a wide range of input parameters. Additionally, to enhance the applicability of the algorithm, GP, GEP, and GMDH were used to develop mathematical expressions based on the collected data set.

Methodology
This work is based on the classic machine-learning (ML) project-development scheme. The schematic of the research is presented in Fig. 1. The research includes four main stages—data input, preprocessing, model construction, and evaluation.