Estimation of Inter-Well Connections in Waterflood under Uncertainty for Application to Continuous Waterflood Optimization of Large Middle-Eastern Carbonate Reservoirs

Abstract The management of large and mature waterfloods is a notoriously challenging exercise. The vast amount of data available usually cripples reservoir simulation efforts and operational teams usually revert to simple classical engineering calculations, diagnostics plots and maps to make their decisions. Some powerful technologies based on reduced-physics modeling have been developed over the past decade to address this issue. In this paper, we present one such approach that was designed for the management of a large Middle-East carbonate waterfloods. The reservoir model used is based on the Surveillance Model proposed by Batycky et al. (2008) but differs from it in two aspects: the inter-well allocation factors are computed through the solution of a tracer equation rather than through streamline computations and the fractional flow behavior is estimated through an empirical model rather than computed numerically. Using the tracer allows an improved treatment of unstructured grids and dual-porosity systems, both features being important for the application of interest. Modifying the fractional flow model allows for the automation of the history-matching step. The model can thus integrate new data quickly and estimate the strength and efficiency of each inter-well connection. An optimization algorithm is used to translate the reservoir management strategy of the asset team in terms of an objective function and a series of constraints at the well, well-group or facility level. Constraints such as voidage replacement ratios, surface facility limits, fracturing pressures can be integrated into the optimization engine to control the field. A new uncertainty modeling process uses a Markov-Chain Monte-Carlo algorithm to evaluate the robustness of each recommended change. The less mature or less data-rich areas of the field are typically harder to calibrate and more uncertain. Decisions to change the rate of a producer or injector in those areas are more risky. The algorithm is able to quantify this risk to help the operator make a more informed decision. As the field gains in maturity, the algorithm shows how the model learns with new data and how the proposed decisions continuously gain in robustness. The application of the methodology to giant Middle-East carbonate fields is discussed. The proposed methodology was able to integrate all relevant facility, well group, individual well and reservoir constraints but remains fast enough to be run daily as new data becomes available.

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