Probabilistic Production Data Analysis Applied to ESP Selection

Abstract Electric Submersible Pump technology dramatically evolved to encompass wider inflow ranges and conditions. Production and reservoir data uncertainty represent critical hurdles for ESP selection, often resulting in improper sizing of equipment. The improper sizing may result in the ESP operating out of range, or, in the worst case, constraining the well's production. A properly sized and fit-for-purpose ESP is important for reliable operations and to avoid costly intervention and deferred production which negatively impact project economics. Recently discovered reservoirs often present challenges with data availability and certainty. It becomes even more challenging if the reservoir is unconventional or if the data quality is questionable. This study encompasses an unconventional reservoir (recently discovered and a mature field), a deepwater subsea application and a field with limited and questionable data. There are various statistical options to manage uncertainty. This paper presents a methodology to apply the probabilistic production analysis to ESP design. The methodology comprises the use of nodal analysis, statistics and specialized ESP software. The resulting ESP options are simulated to model performance over time. Finally, all the options and conditions are scrutinized to select the configuration that best optimizes the ESP system from production and reliability perspectives.