Field Applications of Capacitance Resistive Models in Waterfloods

Abstract

Abstract Application of fast, simple and yet powerful analytic tools, capacitance-resistive models (CRMs), are demonstrated with four field examples. Most waterfloods lend themselves to this treatment. This spreadsheet-based tool is ideally suited for engineers who manage daily flood performance. We envision CRM's application to precede any detailed full-field numerical modeling. We have selected field case studies in a way to demonstrate CRMs capabilities in different settings: a tank representation of a field, its ability to determine connectivity between the producers and injectors, and understanding flood efficiencies for the entire or a portion of a field. Significant insights about the flood performance over a short period can be gained by estimating fractions of injected fluid being directed from an injector to various producers and the time taken for an injection signal to reach a producer. Injector-to-producer connectivity may be inferred directly during the course of error minimization. Because the method circumvents geologic modeling and saturation matching, it lends itself to frequent usage without intervention of expert modelers. Introduction History matching reservoir performance is a difficult inverse problem. Ordinarily, history matching entails minimizing the difference between the observed and computed response in terms of gas/oil ratio, water/oil ratio, and reservoir drainage-area pressures. Systematic approaches have emerged to simplify history matching because manual matching by adjusting global and/or local geological and flow properties is tedious and time-consuming. Two classes of matching algorithms have emerged; one dealing with an automated approach involving error minimization, and the other dealing with 3D streamline assisted property adjustments in a systematic way. Some of the automated methods used for history matching include a gradient-based approach (Thomas et al. 1972, Chen et al. 1974, Bissell et al. 1997, Yang and Watson 1998, Zhang et al. 2000, and Gomez et al. 2001), sensitivity-analysis technique (Hirasaki 1973, Dogru and Seinfeld 1981, and Watson 1989), stochastic modeling technique (Tyler et al. 1993 and Calatayud et al. 1994), and optimal-control theory (Chavent et al. 1975 and Wasserman et al. 1975). In addition, history matching with streamlines (Milliken et al. 2001, Cheng et al. 2007) has gained popularity for its computational speed. Because history matching with a single geologic model does not assure attaining the 'correct' model, uncertainty in forecasting remains. Tavassoli et al. (2004) made this point very eloquently. The lack of forecasting certainty has prompted some to pursue history matching and forecasting with ensemble of models carrying geologic uncertainty. For instance, Landa et al. (2005) by using clustered computing showed how uncertainty in static modeling can be handled in both history matching and forecasting phases. Similarly, Liu and Oliver (2005) explored applications of ensemble Kalman filter in history matching where continuous model updating with time is sought for an ensemble of initial reservoir models. In yet another approach, Sahni and Horne (2006) have used wavelets for generating multiple history-matched models using both geologic and production data uncertainty. In spite of the advances made in automated-history matching with grid-based simulations, manual history matching is the norm in most business settings. The purpose of this study is two-fold; first, to alleviate the tedious task of history matching, manual or automated, by providing clues about producer/injector connectivity, and second, to provide a day-to-day waterflood management tool without the intervention of specialists requiring high-end computing.