Impact of Well Placement in the Fractured Geothermal Reservoirs Based on Available Discrete Fractured System

Well placement optimization in a given geological setting for a fractured geothermal reservoir is a prerequisite for enhanced geothermal operations. High computational cost associated in the framework of fully coupled thermo-hydraulic-mechanical (THM) processes in a fractured reservoir simulation, makes the well positioning as a missing point in developing a field scale investigation. Here, in this study, we shed light on this topic through examining different injection-production well (doublet) position in a given real fracture network. Water and CO2 are used as working fluids for geothermal operations and importance of well positions are examined using coupled THM numerical simulations for both the fluids. Results of this study are examined through the thermal breakthrough time, mass flux and the energy extraction potential to assess the impact of well position in a two-dimensional reservoir framework. Almost ten times of the difference between the final amount of heat extraction is observed for different well position but with the same well spacing and geological characteristics. Furthermore, stress field is be a strong function of well position that is important with respect to the possibility of unwanted stress development. As part of the MEET project, this study recommends to perform similar well placement optimization study for each fracture set in a fully coupled THM manner before a field well drilling.

Selection of Representative Scenarios Using Multiple Simulation Outputs for Robust Well Placement Optimization in Greenfields

In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations can be computationally expensive. Hence, simulation runs are generally applied over a small subset of scenarios called representative scenarios (RS) approximately showing the statistical features of the full ensemble. In this work, we evaluated two workflows for robust well placement optimization using the selection of (1) representative geostatistical realizations (RGR) under geological uncertainties (Workflow A), and (2) representative (simulation) models (RM) under the combination of geological and reservoir (dynamic) uncertainties (Workflow B). In both workflows, an existing RS selection technique was used by measuring the mismatches between the cumulative distribution of multiple simulation outputs from the subset and the full ensemble. We applied the Iterative Discretized Latin Hypercube (IDLHC) to optimize the well placements using the RS sets selected from each workflow and maximizing the expected monetary value (EMV) as the objective function. We evaluated the workflows in terms of (1) representativeness of the RS in different production strategies, (2) quality of the defined robust strategies, and (3) computational costs. To obtain and validate the results, we employed the synthetic UNISIM-II-D-BO benchmark case with uncertain variables and the reference fine- grid model, UNISIM-II-R, which works as a real case. This work investigated the overall impacts of the robust well placement optimization workflows considering uncertain scenarios and application on the reference model. Additionally, we highlighted and evaluated the importance of geological and dynamic uncertainties in the RS selection for efficient robust well placement optimization.