Abstract
Abstract Chemical Enhanced Oil Recovery (CEOR) in a multi-layered reservoir environment with moderate to strong natural water-drive is a complex process with associated risks and uncertainties. Reservoir simulation is one of the most important tools available to predict behavior under chemical flooding conditions and to study sensitivities aimed to a cost-effective CEOR process implementation. Key to the success of a reliable reservoir simulation is the application of a de-risking process and the acquisition of important calibration data, such as laboratory core-flood data and field-scale pilot experiments. A CEOR pilot is currently undertaken in the Caracara Sur (CCS) field (Llanos Basin, Colombia) which has an unfavorable mobility ratio and very low water salinity. However challenges exist, such as strong water drive (no water injection experience), high temperature and a complex geological nature (up to 15 reservoir layers that made up of multiple isolated distributary channels). A detailed reservoir simulation model was built to study full field implementation of CEOR in Caracara Sur. The model was calibrated with laboratory tests data, residual oil saturations from Single Well CEOR ASP Well Tracer Test Pilots, injectivity tests and breakthrough times from inter-well tracer tests. The paper discusses the approaches taken for full field modeling of Caracara Sur using commercial software and describes how the data collected from alkaline/surfactant/polymer (ASP) core flooding were up scaled and used in pilot sector modeling and for designing tracer back flow tests (TBFT) and single well tracer tests (SWTT) before and after ASP flooding. The TBFT and SWTT results in a pilot injection well, in the middle of three producers, confirmed the absence of aquifer drift, which was also predicted by pilot sector modeling. The paper explains how the obtained results from the tracer tests (residual oil saturations, breakthrough times) and well injectivity were used to calibrate the full field numerical model for reliable prediction of the effectiveness of the ASP flooding.
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