Abstract
Abstract Numerical reservoir simulation is a valuable, computationally expensive, tool for the design, evaluation and operation of hydrocarbon fields. Such reservoir models are complex, time consuming to build, yet uncertain; being based on limited information. The use of alternative models based on the field's production history data to reliably quantify inter-well connectivity and support well control decisions is preferred. This paper extends their application to a field with Intelligent production Wells (IWs) equipped with downhole monitoring and flow control. A field with IWs is a natural candidate for closed-loop, reservoir model update and field optimisation. The Capacitance-Resistance Model (CRM), one type of data-driven models, has proved to be effective for making well production optimisation decisions in a single-layer, mature, water flooded reservoirs. This study extends the application of CRMs to a multiple-layer, heterogeneous reservoir being developed by IWs. The optimisation strategy was further extended to include both injection and production control at the level of an IW zone. The resulting, CRM-based, proactive optimisation strategy using zonal production/injection history data proved to be capable of delaying water breakthrough and increasing recovery. This paper describes and compares the reliability of three CRMs in synthetic, single- and multi-layer, reservoir models. The optimum CRM complexity is a function of the reservoir's geological complexity and the complexity of the chosen optimisation strategy. The CRM calculated inter-well, layer connectivity results can be used to suggest changes to the operation of one or more IW zone's injection or production rate. The CRM can be updated once sufficient further injection and production data has accumulated. A comprehensive CRM -based, closed-loop workflow of data gathering, history matching and optimisation has thus been described. The workflow, programmed as a spreadsheet, is a complementary tool to numerical reservoir simulation, providing insights into both reservoir characterization and production control in intelligent and conventional field developments.
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