Enhancing Gas Utilization and Revenue in Nigeria: A Machine Learning Framework for Production Forecasting

Abstract

Nigeria's natural gas sector loses an estimated USD 2.5 billion annually to suboptimal utilisation and persistent flaring, yet conventional forecasting tools remain inadequate for the sector's nonlinear volatility. This study develops and validates an integrated machine learning framework for forecasting monthly natural gas utilisation and revenue using 48 months of official NNPC production data (January 2021 – December 2024). Five models are benchmarked on a 12-month hold-out test set: SARIMA as the statistical baseline, Random Forest, XGBoost, a two-layer LSTM network, and a Hybrid Ensemble combining the three ML learners via inverse-RMSE weighting. All ML models exceeded the target of a 15% RMSE reduction over SARIMA, with the Hybrid Ensemble achieving the greatest improvement at approximately 48.8% and an R2 of 0.93. SHAP feature importance analysis identified lagged utilisation and rolling-mean features as the dominant predictors, followed by upstream production volumes and cyclical seasonal patterns. Retrained on the full dataset, the Hybrid Ensemble generates a 60-month forecast (2025–2029) across three scenarios: a Base Case projecting stable annual utilisation of approximately 2,320 BSCF/year and cumulative revenues of approximately USD 13.0 billion, and an Optimistic scenario projecting 2,795 BSCF/year and USD 31.4 billion under favourable LNG pricing and capacity expansion. Flaring reduction modelling indicates that achieving a 22% reduction in the current 7.7% flaring rate could recover approximately USD 0.93 billion in otherwise wasted gas value over five years, with co-benefits aligned to Nigeria's 2030 zero-flaring pledge and Nationally Determined Contributions. These findings offer a replicable, interpretable decision-support framework for operators, regulators, and investors seeking to align gas production forecasts with Nigeria's energy transition objectives.