Artificial lift infrastructure planning of shale gas horizontal wells considering endogenous and exogenous uncertainties

Abstract

Artificial lift methods (ALMs) lift the accumulated fluids from horizontal shale-gasproducing wells and help sustain well performance. An artificial lift infrastructure plan includes the selection of ALMs and their operating schedule. This paper presents two discrete-time large-scale nonconvex mixed-integer nonlinear programming models to solve the artificial lift infrastructure planning problem. Two equivalent mixed-integer linear programming models are formulated using the special structure of the nonlinear terms. A set of valid inequalities is defined to tighten the models and shorten solution times to two orders of magnitude, considering well production limitations. We incorporate endogenous uncertainty in ALM-dependent production rates and exogenous uncertainty in shale gas prices into the models. For a hypothetical case study under only endogenous uncertainties, the value of the stochastic solution is 5%. For the same case study, the exogenous uncertainty in gas prices does not change the optimum solution.