Author ORCID Identifier
Semester
Fall
Date of Graduation
2024
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Ebrahim Fathi
Committee Member
Samuel Ameri
Committee Member
Ilkin Bilgesu
Abstract
The proposed paper assesses the geological and geomechanical characteristics of the geothermal deep direct-use MIP 1S well in the Appalachian basin. The assessment utilizes a comprehensive suite of well logs, sidewall core analysis, and well injection tests to determine continuous elastic and rock strength properties. Stress and pore pressure profiling are conducted, and a validated 1D-geomechanical model is developed. This model undergoes analysis through wellbore stability assessments and comparisons with drilling events. The 1D-geomechanical model demonstrates a high degree of agreement with wellbore observations, revealing suboptimal execution of drilling practices, particularly utilizing mist air drilling up to 8868 feet. This suboptimal execution has led to significant wellbore breakout in the well's shallow and intermediate sections. Consequently, the wellbore diameter has been excessively widened, exceeding 4 inches from the planned 12 ¼-inch diameter to over 16 inches. Such deviations could potentially provoke significant challenges related to cementing, open-hole log data accuracy, and wellbore stability. Advanced technologies like Cerebro Force™ In-Bit Sensing are used to monitor drilling performance accurately. This technology tracks critical metrics such as bit acceleration, vibration in x, y, and z directions, Gyro RPM, stick-slip indicator, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface than downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings. The implementation of machine learning techniques, such as random forest, has helped to validate the previously mentioned results. It has been determined that the drilling process of MIP 1S was not conducted efficiently. By examining the ROP prediction of two neighboring wells (MIP 3H and SW), it is evident that the actual ROP prediction for MIP 1S deviated from the real data, resulting in inaccurate predictions. In comparison to the other wells, the prediction showed a better fit.
Recommended Citation
LIWUITEKONG DONGHO, GEORGES BROWN, "1D Geo-mechanical model analysis of the MIP 1S and drilling process optimization in the Deep Appalachian basin" (2024). Graduate Theses, Dissertations, and Problem Reports. 12692.
https://researchrepository.wvu.edu/etd/12692