Statistical Data-Driven Models for Forecasting Production Performance with Uncertainty Analysis

Ladipo, Olalekan Azeez (2014-12-20)


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 linear- exponential and double-exponential smoothing algorithms were considered in the study. The uncertainty analysis of these predictions is evaluated using the history-fit errors derived from the observed data and fits to the history. The models’ accuracy depends on the segment of the observed historical data selected for the initial data fitting and the trends of most recent historical production data—the tail end of the observed data. The application of the proposed statistical data-driven models was demonstrated on several data sets. The results obtained from the proposed data-driven models compare favorably with the base forecasts derived from existing models such as the Arps’ Decline Curve Analysis techniques. The major advantage of the proposed models over existing models is that predicted forecasts include a range of possible performance trends honoring the observed production history with the associated probabilities and confidence intervals. The major contribution of this work is that coherent statistical data-driven models have been developed for forecasting production rates and reserves of petroleum assets with uncertainty analysis.