已发表: 07/17/2024
已发表: 07/17/2024
PROCEEDINGS, The 9th Indonesia International Geothermal Convention & Exhibition (IIGCE) 2023
Fracture permeability is one of the most important parameters for reservoir characterization and well targeting in Geothermal field. This value can be obtained from fracture modeling process based on fractures well data and conceptual model, however the uncertainty is very high due to dependency on the fracture data and limited subsurface information. Most of the time, fracture permeability parameters from fracture modeling results created by using all of fracture planes, without any further analysis or consideration on the geomechanic condition of the field to filter out the non-effective fracture planes. It will make the result of fracture permeability model will be not very optimal. To address the challenge, this study was done by doing critical stress analysis process that will give more detail results on identifying the critically stressed fractures to be used for creating fracture permeability process, instead of using all fractures planes. Main objective of this study is to show the workflow to do critical stress analysis for the fracture model. All data used for this study is published data from Utah FORGE, USA. It is an Enhanced Geothermal System (EGS) field, however the method can be applied for other geothermal systems. Input data for the workflow consist of lithological 3D model, fracture model, well data, fracture data and well logs (sonic & density). The workflow starts with loading all wells data and lithological model into the software, followed by creating 1D MEM (Mechanical Earth Model), build 3D MEM, and critical stress analysis. The result of this study is critically stressed fractures data defined as effective fractures based on geomechanic analysis. Having more accurate effective fractures data will give us information on which fractured area really has effect on the fluid flow and will lead us to better result of fracture modeling for entire field. Finally, this study can be utilized optimally for subsurface reservoir characterization and well targeting.