Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Hung Vo Thanh,Yuichi Sugai,Kyuro Sasaki

doi:10.1038/s41598-020-73931-2

Hung Vo Thanh, Yuichi Sugai + Show 1 more

Open Access

https://doi.org/10.1038/s41598-020-73931-2

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Journal: Scientific Reports	Publication Date: Oct 23, 2020
Citations: 74	License type: open-access

Affiliation: Kyushu University

Abstract

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

Highlights

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS)
This study was developed for ROZs, so the artificial neural network (ANN) model from this work just applied in the ROZs field
We can adjust the number of reservoir parameters for “training” ANN model depended on the available information

Summary

Introduction

Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). The predictive results were an excellent agreement between ANN predictions and field report data These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. Ampomah et al.[19] proposed the integrated workflow based on the uncertainty quantification method and the artificial neural network optimization approach to co-optimize the CO2 storage and EOR in the Farnsworth Unit oil field in Texas. Dai et al.[20] employed Monte Carlo (MC) simulations for the quantification uncertainty of CO2 sequestration potential within an active EOR project in the Morrow reservoir at the Farnsworth Unit, Texas. Hill et al.[21] stated that geologic CO2 storage coupling EOR provides the benefits to improve oil recovery, which offsets major capital costs of capture and storage facility

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