Application of Novel Predictive Analytics for Data Allocation of Commingled Production in Smart Fields

Abstract

This paper details out the application of a predictive analysis tool to ‘S’ Field's commingled production, aiming to enhance production allocation and reservoir understanding without the need of well intervention and a reduced frequency of zonal rate tests and data acquisition. Allocation of the production data to its respective reservoirs is performed via a novel Multi-Phase Allocation method (MPA), taking into account the water production trending evolution derived from relative permeability behavior of oil-water in each reservoir to compute flow rates for liquid phases over time. The precision of the derived rates is constrained by actual zonal rates tests through Inflow Control Valves (ICVs). This method will be cross referenced against ‘S’ Field's existing zonal rate calculation algorithm, utilizing input data from well tests results and real time pressure and temperature data. The MPA method demonstrates improvement in the allocation of production data as compared to the conventional KH-methodology as MPA takes into account the water cut trending between reservoirs. Leveraging on ICVs to obtain actual zonal rate measurements, this greatly reduces the range of uncertainty in the allocation process. MPA derived production split ratios closely match the split ratios derived from the ‘S’ Field's existing zonal rate calculation algorithm, which utilizes input data from well tests results and real time pressure and temperature data from down hole gauges. It is observed that the usage of actual measured zonal rate tests reduces the range of uncertainty of the MPA data. A combination of novel multi-phase deliverability models coupled with smart field technologies such as intelligent completions and real-time surveillance and analysis tools will increase the accuracy of the back allocation of multi-phase production data in commingled reservoirs.