DOI : https://doi.org/10.18517/ijaseit.13.6.19399

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

Diyah Rosiani (1), Muhamad Gibral Walay (2), Pradini Rahalintar (3), Arya Dwi Candra (4), Akhmad Sofyan (5), Yesaya Arison Haratua (6)
(1) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu 58315, Indonesia
(2) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu 58315, Indonesia
(3) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu 58315, Indonesia
(4) Department of Oil and Gas Production Engineering, Politeknik Energi dan Mineral Akamigas, Cepu 58315, Indonesia
(5) Department of Mineralogy, Geochemistry and Petrology, Faculty of Earth Science, University of Szeged, Szeged, 6722, Hungary
(6) PT Pertamina Hulu Rokan Zona 4, Jl. Jend. Sudirman No. 3, South Sumatra, 31122, Indonesia
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How to cite (IJASEIT) :
Rosiani, Diyah, et al. “Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 6, Dec. 2023, pp. 2338-44, doi:10.18517/ijaseit.13.6.19399.
Citation Format :
The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.

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