Abstract
Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.
Highlights
Waterflooding is one of the most widely used secondary recovery methods to improve the oil and gas production
From the production rates and cumulative productions of oil and water, both reservoir permeability and the distance between injector and producer were influential for production behavior. This implies that it will be critical to include the information on the reservoir permeability and the injector responses as an input dataset for the reliable prediction by artificial neural network (ANN) models
Application of additional input data measured at the injector, which implied geological information and sweep efficiency of reservoirs, made significant contributions to improve the performance of ANN predictions
Summary
Waterflooding is one of the most widely used secondary recovery methods to improve the oil and gas production. When reservoir pressure significantly decreases after the primary production, external energy is needed to drive the remaining oil to the production well. Uncertainty refers to the insufficiency of knowledge and information on geological properties; variability refers to the reservoir heterogeneity such as spatial differences of porosity and permeability in the reservoir (Siirila et al, 2012). Because of these challenges, numerical simulations of heterogeneous reservoirs usually require a large number of discretized grid blocks in simulation models. It is expensive and time-consuming to run full-physics simulations of heterogeneous reservoirs for every possible condition of operation
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