What question did this study set out to answer?

This study aims to evaluate the effectiveness of machine learning models in predicting daily oil production in the Niger Delta region, where traditional methods often fail.

March 2, 2026Open Access

Application of Machine Learning for Production Forcasting in Niger Delta Oil Field (Ozoro Field)

Key Points

This study aims to evaluate the effectiveness of machine learning models in predicting daily oil production in the Niger Delta region, where traditional methods often fail.
Analyzed historical oil production data from four wells in the Niger Delta oilfield
Implemented two ensemble models: random forest and gradient boosting
Conducted data cleaning and variable creation for model improvement
Performed hyperparameter optimization with RandomisedSearchCV and 5-fold cross-validation
Assessed model performance using R², RMSE, and MAE
Gradient Boosting outperformed Random Forest in most cases, with R² values ranging from 0.8767 to 0.9887
Random Forest produced R² values between 0.7803 and 0.9756
Stable production wells had better prediction accuracy, while unstable wells faced higher errors
Overall, ensemble models provide reasonably accurate forecasts for oil production, aiding operational decisions

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.

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