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Mature waterfloods often present significant Reservoir Management challenges. After an initial boost in oil production, water cuts tend to increase and flood performance starts to decline. Complex reservoirs that have been producing for decades through hundreds or thousands of wells are notoriously challenging to model. Creating and history-matching a simulation model usually take several months for subsurface teams, and operational teams can rarely rely on these models to make reservoir management decisions. In this paper, a novel methodology is presented that is being used in practice on large waterfloods or strong aquifer-supported reservoirs, to support operational decisions in near real-time. The proposed technology relies on a reduced-physics, data-driven reservoir model to quickly build and match a reservoir model that can be used to optimize waterfloods. The first stage of the workflow involves collecting and validating the field data, including rock and fluid properties, production, injection and pressure data as well as well information, such as trajectories and historical perforations. The reservoir behavior is then modeled following an approach similar to that of Thiele and Batycky (2006) in the context of streamline simulation. The model represents the reservoir as a network of inter-well connections described by their strengths and efficiencies. Contrary to traditional streamline-based method, the strength of connection is rather determined through the solution of a numerical tracer test, which generalizes the method to unstructured or locally refined grids as well as dual permeability systems, and allows the method to account for mild compressibility effects. An empirical fractional flow model is then used to calculate the connection efficiencies. Once the model is complete and calibrated, a cutting-edge optimization algorithm is used to optimize the production-injection strategy based on this network of subsurface connections. Recommendations for adjustments in the production-injection strategies are proposed and model uncertainties are computed through a novel algorithm to compute the associated risks. A new finite-volume based time-of-flight computation algorithm is developed based on the numerical tracer solution, which, combined with the empirical fractional flow model, can give a data-driven production mapping algorithm. The proposed methodology was successfully applied to many reservoirs across the world, including several giant middle-east carbonates with hundreds of wells and decades of history. The approach consistenly identified an optimized strategy that could deliver several percentage points of incremental oil along with a reduction in water production. The methodology proposed is fast enough to build and match a new model in a few days; and updating an existing model takes less than an hour as new data comes available, avoiding expensive numerical simulations and helping engineers optimize daily production-injection strategy of reservoirs.

Parent and child wells interference is a major concern in the development of shale and tight reservoirs. Oil and gas operators typically aim to prevent fractures interference during the child well’s stimulation, which connects the parent and child well’s fracture networks. Another aspect of the parent-child well interference lies in the impact of parent-well depletion on child-well stimulation, often leading to the underperformance of child-well production. For these circumstances, reservoir simulation that combines flow and geomechanics is required to predict child-well fractures growth. To achieve this goal, a new workflow combining flow (EDFM), geomechanics (single porosity) and fracturing was developed to predict the reservoir stress change, the growth of the child-well fractures, and the production of the parent and child wells. As one of the applications, this tool was applied to a Delaware basin reservoir and enabled the asset team to better design the pad for the child wells. Multiple scenarios were analyzed for eight child wells located between two parent wells. Using this tool, we were able to predict the asymmetric growth of the fractures in the direction of the parent wells for child wells that were close enough to their parent wells. The impact of this fractures’ asymmetric growth on the production of the child wells was also quantified, based on which a better configuration of child wells was recommended to mitigate the depletion effect of parent wells.