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An Efficient Multi-Objective Optimization Workflow for Hydraulic Fracture Placement in Unconventional Gas Reservoirs

Abstract

In last decades the demand for fast optimization strategies in unconventional reservoir management increased rapidly and caused an effort to couple shale gas numerical reservoir simulation with advanced optimization techniques for optimal hydraulic fracture placement. Judicious application of these strategies can yield higher shale gas reserve estimates and improved project revenue. In this paper we present our novel optimization framework that allows to place hydraulic fractures in presence of multiple economic and/or production objectives. At the heart of our optimization workflow lies an evolutionary-based optimization engine (binary genetic algorithm with strong elitism) that efficiently explores a multi-dimensional solution space and assesses the “goodness” of each arrangement of hydraulic fractures and their half-length based on values of multiple objectives. These objectives can be conflicting (improvement in one inevitably leads to decline in another) or non-conflicting (both objectives increase or decrease simultaneously). Our implementation of NSGA-II (non-dominated sorting genetic algorithm for multiple objectives) handles both scenarios and provides the user of the set of optimal solutions (the Pareto optimal set). We first test the performance our workflow on a suite of shale gas simulation models for a single objective (long-term discounted net present value) and then juxtapose the computational time for scenarios with conflicting and non-conflicting objectives. We observe that application of NSGA-II does help keep computational time within reasonable range and our “strong elitism” implementation of genetic algorithm maintains simulator calls at absolute necessary minimum. In addition to this, we demonstrate that our multi-objective approach has benefits of providing engineers with a set of optimal solutions rather than one specific plan with a number and spacing of hydraulic fractures and their half-length. Our novel multi-objective evolutionary optimization approach to hydraulic fracture placement is unparalleled in its flexibility. All parts of the objectives and economic parameters are fully customizable for fit the needs of a specific operator of unconventional assets. Our framework takes a popular idea of conflicting objectives from applied mathematics and brings it to petroleum industry to increase profitability of shale gas projects.