000 02251nab a2200205 4500
005 20260520001758.0
008 260224s2009 xxu
245 0 0 _aThe use of capacitance-resistance models for rapid estimation of waterflood performance and optimization
260 _a
_b
_cdic. 2009
270 _a17/06/2010 ; 16/06/2010
300 _a12 p. ; 227-238
520 _aTranscripción del resumen del autor. The capacitance–resistance model (CRM) offers the promise of rapid evaluation of waterflood performance. This semianalytical 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 and bottomhole pressure, if available, to calibrate the model against a specific reservoir. Thereafter, the model is used for predictions. We focused on three different control volumes for CRMs: the volume of the entire field, the drainage volume of each producer, and a drainage volume between each injector/producer pair. Unlike the numerical simulation approach, the CRMs use only production/injection data to predict performance, which provides simplicity and speed of calculation. Once the CRM is calibrated with historical production/injection data, we use an optimization technique to maximize the amount of oil produced by reallocating water injection rates. To verify CRM predictions, the models were tested against numerical flow-simulation results. Two case studies showed that the CRMs are able to successfully history match, and maximize the amount of oil produced by just reallocating water injection. This study introduces analytical solutions to the fundamental differential equations of the capacitance model based on superposition in time. In so doing, this approach adds flexibility, simplicity, and computational speed to the work presented previously.
581 _a3-4
773 0 _tJournal of Petroleum Science & Engineering
_g69
942 _cARTICULO
100 1 _aSayarpour, M.
_942909
100 1 _aZuluaga, E.
_925060
100 1 _aKabir, C.S.
_915994
999 _c171141
_d171141