Research Article
Application of Machine Learning for Production Forcasting in Niger Delta Oil Field (Ozoro Field)
Issue:
Volume 10, Issue 1, June 2026
Pages:
1-16
Received:
9 January 2026
Accepted:
3 February 2026
Published:
24 February 2026
DOI:
10.11648/j.pse.20261001.11
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Abstract: Daily oil production forecasts are a key part of how reservoirs are managed and how production plans are made in the upstream oil and gas industry. In practice, however, getting accurate daily forecasts is not always easy. This is very common in the Niger Delta region where wells are usually affected by shutdowns, flow interruptions and changing operational conditions. Because of these reasons, oil production data from these wells are usually irregular and traditional forecasting methods usually struggle to capture these changes. This study looks at whether machine learning models can do a better job of predicting daily oil production rates. Historical oil production data from four wells in a Niger Delta oilfield was used for the study. Two ensemble models which are random forest and gradient boosting were selected and tested. Before building the models, the data was checked carefully and cleaned. Some new variables were also created to help the models understand how production changes over time. Hyperparameter optimization was performed using RandomisedSearchCV with 5-fold cross-validation to choose the best model settings and to avoid the risk of overfitting. Their performance was assessed using Coefficient of Determination (R²), Root Measure Squared Error (RMSE), and Mean Absolute Error (MAE). From the results, Gradient Boosting performed better in most cases. Its R² values were generally between 0.8767 and 0.9887, while the Random Forest model produced values in the range of about 0.7803 to 0.9756. The best predictions were obtained for wells that showed relatively stable production behaviour. For wells with frequent shutdowns and more unstable production, both models recorded higher errors with random forest having the highest error across all wells. This shows that prediction becomes more difficult when production conditions change often. Even though, the overall results suggest that ensemble machine learning models, particularly Gradient Boosting, can provide useful and reasonably accurate daily oil production forecasts for Niger Delta fields. These models can therefore support better planning and operational decision-making in Nigeria’s upstream oil and gas sector.
Abstract: Daily oil production forecasts are a key part of how reservoirs are managed and how production plans are made in the upstream oil and gas industry. In practice, however, getting accurate daily forecasts is not always easy. This is very common in the Niger Delta region where wells are usually affected by shutdowns, flow interruptions and changing op...
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Shamsuddeen Abubakar Ashurah
