Methodological Evaluation of Process-Control Systems for Yield Improvement in South African Petroleum Operations

The oil and gas sector in South Africa faces challenges in optimising production yields due to variability in geological formations and operational conditions. A time-series forecasting approach was employed, incorporating autoregressive integrated moving average (ARIMA) models to analyse and predict yield trends. Robust standard errors were used to quantify the uncertainty in these predictions. The ARIMA model demonstrated a correlation coefficient of R² = 0.85 for predicting yield improvements over a five-year period, indicating a significant reduction in forecast error. This study validates the use of ARIMA models as a reliable tool for predicting and improving petroleum yields in South African operations. The findings suggest that implementing these predictive models could lead to more efficient resource management and increased operational efficiency. process-control systems, yield improvement, time-series forecasting, autoregressive integrated moving average (ARIMA), South Africa