Inter-well connectivity detection in CO2 WAG projects using statistical recurrent unit models

Sen, Deepthi; Chen, Hongquan; Datta‐Gupta, Akhil

doi:10.1016/j.fuel.2021.122600

Cited by 13 publications

(1 citation statement)

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“…At the current stage, rapidly evolving intelligent algorithms, such as machine learning, have found significant applications in the field of petroleum exploration and development. Sen et al [21] employed a Specialized RNN Unit (SRU) model, which is a type of recurrent neural network (RNN), to optimize the parameters and predict the production in actual CO 2 -EOR projects. The injection rate, injection pressure, cumulative injection volume of the injection wells, and bottom hole flowing pressure of the production wells were used as inputs for the SRU model, while the fluid production of the production wells Energies 2023, 16, 6149 3 of 21 served as the output.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Approach to Optimize CO2-WAG Flooding in Low Permeability Oil Reservoirs

Gao,

Liu,

Qian

et al. 2023

Energies

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One of the main applications of carbon capture, utilization, and storage (CCUS) technology in the industry is carbon-dioxide-enhanced oil recovery (CO2-EOR). However, accurately and rapidly assessing their application potential remains a major challenge. In this study, a numerical model of the CO2-WAG technique was developed using the reservoir numerical simulation software CMG (Version 2021), which is widely used in the field of reservoir engineering. Then, 10,000 different reservoir models were randomly generated using the Monte Carlo method for numerical simulations, with each having different formation physical parameters, fluid parameters, initial conditions, and injection and production parameters. Among them, 70% were used as the training set and 30% as the test set. A comprehensive analysis was conducted using eight different machine learning regression methods to train and evaluate the dataset. After evaluation, the XGBoost algorithm emerged as the top-performing method and was selected as the optimal approach for the prediction and optimization. By integrating the production prediction model with a particle swarm optimizer (PSO), a workflow for optimizing the CO2-EOR parameters was developed. This process enables the rapid optimization of the CO2-EOR parameters and the prediction of the production for each period based on cumulative production under different geological conditions. The proposed XGBoost-PSO proxy model accurately, reliably, and efficiently predicts production, thereby making it an important tool for optimizing CO2-EOR design.

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Section: Introductionmentioning

confidence: 99%