Optimization Method for Fracture-Network Design under Transient and Pseudosteady Conditions Using Unified-Fracture-Design and Deep-Learning Approaches

Abstract

In unconventional shale and tight reservoirs, the concept of stimulated reservoir volume (SRV) is used to correlate the volume of total injected proppant with well performance. The SRV configuration consists of primary fractures connected to the wellbore and secondary fractures intersecting primary fractures. SRV productivity is determined by fracture conductivity, fracture dimensions, and network complexity, which also vary with time. This work presents an extension of the unified-fracture-design (UFD) approach to account for not only the pseudosteady state (PSS) but also transient flow regimes and ultimately optimize SRV for maximizing well performance. A generalized productivity index (PI) for both the transient and PSS regimes is presented to improve well performance by searching for the maximum PI over time. In addition, a surrogate model is developed to accelerate the optimization. This study demonstrates that the UFD enables the determination of the optimal fracture network conductivity and complexity that contribute to the maximum PI with a given proppant volume. The optimal SRV design is time-dependent until the PSS is reached. The surrogate model not only improves the computational efficiency but also delivers high precision, which means far less computational burden than the traditional parametric-sensitivity analysis.