Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

Conference · Mon Feb 10 00:00:00 EST 2025

OSTI ID:2519697

Xing, Pengju; Edelman, Eric; Vega-Ortiz, Carlos; Jones, Clay; DeOreo, Stephen; Stark, Mitch; Wriedt, Justin; McLennan, John

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

• Research Organization: Geysers Power Company, LLC
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
• Contributing Organization: Energy & Geoscience Institute, University of Utah, Salt Lake City, UT; Geysers Power Company; Stark Industries Geothermal Resources Consulting; Department of Chemical Engineering, University of Utah, Salt Lake City, UT
• DOE Contract Number: EE0010445
• OSTI ID: 2519697
• Report Number(s): DE-EE0010445 - GPC - 2025 Proceedings2; Journal Serial ID: SGP-TR-229
• Resource Type: Conference paper
• Conference Information: PROCEEDINGS, 50th Workshop on Geothermal Reservoir Engineering Stanford University, Stanford, California, February 10-12, 2025
• Country of Publication: United States
• Language: English

Related Subjects

15 GEOTHERMAL ENERGY
Machine learning
The Geysers
drilling performance
PDC bits