#TITLE_ALTERNATIVE#

• Main Author: MULYO (NIM : 12214068), PRAKARSA
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/29961
• Institution: Institut Teknologi Bandung

The capacitance–resistance model (CRM) offers the promise of rapid evaluation of waterflood performance. This semi analytical modeling approach is a generalized nonlinear multivariate regression technique that is rooted in signal processing. Put simply, a rate variation at an injector introduces a signal, with the corresponding response felt at one or more producers. CRM uses production and injection rate data to calibrate the model against a specific reservoir. Thereafter, the CRM model is used for predictions. <br /> <br /> Initializing the CRM parameters correctly lowers the work load of history matching. CRM parameters values, (time delay constant) and (interconnectivity between injection and producing well) are obtained once the CRM is calibrated with historical production/injection data. This calibration was done multiple times with historical production/injection data from different reservoir and fluid properties. Multivariate linear regression and genetic algorithm are then used to generate an equation to fit all sets of (time delay constant) and (interconnectivity between injection and producing well) values. Hereafter, the relationship between CRM parameters and reservoir and fluid properties can then be expressed explicitly. The equation which provides the least error then can be used to forecast CRM parameters in different reservoir and fluid properties The equation is useful for prediction of waterflood pilot project performance through CRM model especially when there’s no production and injection history data.