In the last decades rapid advances in horizontal drilling and hydraulic fracturing technologies ensure production of commercial quantities of natural gas from many unconventional reservoirs. Reservoir management and development strategies for shale and tight gas plays have evolved from ad hoc approaches to more rigorous strategies that involve numerical optimization in presence of multiple economic and production objectives and constraints. Application of an automated integrated optimization framework for placement of horizontal wellbores and transverse hydraulic fracture stages along them has potential of increasing shale gas reserves and projectsβ revenue even further.
This dissertation introduces a novel integrated evolutionary-based optimization framework for placement of horizontal wellbores and hydraulic fracture stages that allows enhancing production from shale gas formations and provides a solid foundation for future field-scale application once better understanding of shale petrophysics and geomechanics is developed. The proposed optimization workflow is developed and tested in stages. First, we summarize what has been done in the subject field previously by scholars and identify what is missing. Second, we present assumptions for the shale gas simulation model that make our framework and the simulation model applicable. Third, we pre-screen several economic and petrophysical parameters in order to identify the most significant for the subsequent sensitivities analysis. Forth, we develop evolutionary-based optimization strategy for placement of hydraulic fracture stages along a single horizontal wellbore. We investigate how sensitive the optimization results to changes in the key parameters pre-selected during pre-screening. Fifth, we enhance the framework to handle multiple horizontal producers, discuss the conditions when such approach is applicable, and extensively test this integrated workflow on a suite of simulation runs. Finally, we implement and apply multi-objective optimization approach (the improved non-dominated sorting genetic algorithm) to the problem of optimal HF stage placement in shale gas reservoirs and analyze the efficiency of our evolutionary-based optimization scheme in presence of multiple conflicting or non-conflicting objectives.
Based on our extensive testing and rigorous formulation of the optimization problem, we find that the chosen evolutionary framework is effective in calculating the optimal number of horizontal wells, the number of HF stages, their specific locations along the wells as well as their half-length. We also conclude that further computational efficiency can be achieved if minimum stage spacing and same chromosome elimination procedure are used. The multi-objective approach has been tested on conflicting and non-conflicting objectives and proved to compute the Pareto optimal front of solutions (or production scenarios) in computationally efficient manner
