STATISTICAL DATA-DRIVEN MODELS FOR FORECASTING PRODUCTION PERFORMANCE WITH UNCERTAINTY ANALYSIS

📄 Project Abstract

Data-driven analytical models are important tools employed in the petroleum industry to forecast production rates and reserves of petroleum assets. Studies have shown that existing rate forecasting models project future performance trends by averaging the observed production history data with little or no preferential consideration of the influence of the trends of most recent historical production data. It is also understood that existing empirical data-driven rate forecast models do not account for the uncertainties involved in such future predictions. In this work, new statistical data-driven models for production performance forecasting are developed. These models forecast future production performance trends using statistical-exponential smoothing of the historically-observed data, attributing more weights to the most recent historically-observed performance trends. Both linearexponential and double-exponential smoothing algorithms were considered in the study. The uncertainty ana...