| Title | Utilizing Downhole Drilling Dynamic Data to Characterize Geomechanics of Enhanced Geothermal Reservoirs at FORGE |
|---|---|
| Authors | Emilie GENTRY, Joseph BATIR, Hamed SOROUSH, Olivier HOFFMAN, Andrew MADYAROV |
| Year | 2023 |
| Conference | Stanford Geothermal Workshop |
| Keywords | geomechanics, drilling dynamics, enhanced geothermal systems, FORGE |
| Abstract | Successful development and operations of an Enhanced Geothermal System (EGS) requires a thorough understanding of subsurface characterization with reasonable estimates of the geomechanical characteristics of the formations, including rock properties and in-situ stresses. Typically, these essential parameters are estimated in 1-D from well logs or seismic. However, these approaches are undermined by the absence of data, oversimplified empirical methods, or insufficient spatial resolution necessary for subsurface purposes. Our solution to this data challenge is Drilling Dynamic Geomechanics (DDG) technology that utilizes downhole drilling dynamics data to calculate continuous 1-D profiles of the principal stresses. Using bit vibration data measured while drilling and Electronic Drilling Recorder (EDR) data routinely measured at surface is low cost and uses readily available data, imposing no additional time for data acquisition. Downhole drilling dynamics data is available along the entire well length and provides higher resolution estimates of stresses than well logs. A supervised machine learning methodology guided by physics considerations and aided by targeted signal processing techniques is implemented to extract in-situ stresses from input data. The result provides high resolution geomechanical models and wellbore stability without logs from the surface to total depth, rock property and stress profiles along the horizontals which are useful for optimizing stimulation and completion designs and mitigating casing deformation, and real-time geomechanics and wellbore stability analysis. DDG was applied to the FORGE Well 16A78_32 dataset to understand how the workflow works on high temperature geothermal reservoirs and provide useful geomechanical insight for EGS. The results show that DDG was able to reasonably predict the rock properties and stresses with around 90% accuracy. The temperature effect seems to be captured by vibration data and the technology works in high temperature reservoirs. Therefore, DDG can be used to characterize and develop the connected and complex fracture networks critical to EGS reservoirs for optimal production. |