Dynamic Prediction of Reservoir Pressure in ESP Oil Wells

Abstract

This paper presents a well performance modeling approach to generate reservoir static bottom hole pressure (SBHP) data from on-stream oil well production data in wells completed with Electrical Submersible Pumps (ESP). The approach does not require shutting-in the well to obtain the SBHP as conventionally required prior to data collection. Hence, production losses are eliminated without compromising reservoir pressure surveillance. Recording downhole SBHP is an essential requirement for reservoir pressure surveillance as it provides significant information to evaluate reservoir inflow performance. SBHP data is used to generate the periodic isobaric maps, update the reservoir simulation models, and re-evaluate the applied injection strategy for possible adjustments when needed. Rigorous reservoir and wellbore performance analysis techniques were employed to generate accurate top-down reservoir pressure prediction models. The predictability of these models was statistically assessed on field representative samples of ESP completed wells with a wide-ranging operating parameters i.e. liquid rate, flowing wellhead pressure, water cut and reservoir pressure. In this modeling, ESP downhole sensors data were leveraged to reduce the uncertainty of the outflow performance models. A sample of 22 case studies from 4 different reservoirs was studied. These case studies were diligently selected to represent all ranges of operating parameters and reservoir pressure. Sensitivity analysis was performed on the well performance models to identify sources of uncertainty that can affect the model accuracy. Overall, this method has been proven to be widely accurate. The prediction performance of this method was found to be within 1.5% accuracy of actual pressure measurements.