The use of capacitance-resistance models to optimize injection allocation and well location in water floods
Reservoir management strategies traditionally attempt to combine and balance complex geophysical, petrophysical, thermodynamic and economic factors to determine an optimal method to recover hydrocarbons from a given reservoir. Reservoir simulators have traditionally been too large and run times too long to allow for rigorous solution in conjunction with an optimization algorithm. It has also proven very difficult to marry an optimizer with the large set of nonlinear partial differential equations required for accurate reservoir simulation. A simple capacitance-resistance model (CRM) that characterizes the connectivity between injection and production wells can determine an injection scheme maximizes the value of the reservoir asset. Model parameters are identified using linear and nonlinear regression. The model is then used together with a nonlinear optimization algorithm to compute a set of future injection rates which maximize discounted net profit. This research demonstrates that this simple dynamic model provides an excellent match to historic data. Based on three case studies examining actual reservoirs, the optimal injection schemes based on the capacitance-resistive model yield a predicted increase in hydrocarbon recovery of up to 60% over the extrapolated exponential historic decline. An advantage of using a simple model is its ability to describe large reservoirs in a straightforward way with computation times that are short to moderate. However, applying the CRM to large reservoirs with many wells presents several new challenges. Reservoirs with hundreds of wells have longer production histories – new wells are created, wells are shut in for varying periods of time and production wells are converted to injection wells. Additionally, ensuring that the production data to which the CRM is fit are free from contamination or corruption is important. Several modeling techniques and heuristics are presented that provide a simple, accurate reservoir model that can be used to optimize the value of the reservoir over future time periods. In addition to optimizing reservoir performance by allocating injection, this research presents a few methods that use the CRM to find optimal well locations for new injectors. These algorithms are still in their infancy and represent the best ideas for future research.