A decision support system for oil production prediction

This paper describes a decision support system (DSS) that aids users in predicting oil production of an infill well by presenting them with a possible range of cumulative production and length of production life of the infill well. The system shows worst and best case scenarios based on different production curves so that an expert can examine the post of predicted production rates for each existing well and decide which model gives the best fit. The production curve of each individual well was mathematically modeled so that production values beyond the historical data can be produced. Decline curve estimation and neural network approaches have been adopted. In our experiments, two groups of wells from the Oakly and Midale field in Saskatchewan, Canada were analyzed. Observations on the suitable duration that the historical data set should cover and a comparison among different curve estimation and neural network models are presented.