Residual Oil Zone “Fairways” and Discovered Oil Resources: Expanding the Options for Carbon Negative Storage of CO2

Kuuskraa, V.A.; Petrusak, Robin; Wallace, Matthew

doi:10.1016/j.egypro.2017.03.1688

Cited by 15 publications

(1 citation statement)

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“…In particular, the Permian basin with the active ROZs employed the CO 2 injection in a number of field projects, but the actual evaluation of ultimate oil recovery is not yet investigated 14 . Also, the CO 2 storage capacity in ROZs capacity is still preliminary estimates with large uncertainties 15 . This is because of the limitation of characterization data and existing geological uncertainties in ROZs.…”

Section: Introductionmentioning

confidence: 99%

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

Thanh

Sugai

Sasaki

2020

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%