Abstract
The invention addresses the problem of drilling tool failure risk assessment in the oil and gas industry. Tool failure in drilling operations can lead to significant financial losses, downtime, and safety risks. The invention aims to provide a solution for predicting and preventing these failures, which is crucial for maintaining safe and efficient drilling operations.
The invention proposes a solution by combining data-driven techniques with expert knowledge. It begins with data collection, including historical data on tool performance, and involves data cleaning and pre-processing. Relevant features are identified, and a predictive model is developed using machine learning techniques. The model is validated with separate historical data, and risk assessment is performed to identify potential failure modes and assess associated risks. The integration of expert knowledge ensures that the model is accurate and relevant to domain experts, and partial dependence analysis helps evaluate parameter interactions and contributions to failure events. The use of boosting techniques and Bayesian optimization further enhances model performance.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Baker Hughes Company, "Machine Learning System and Method for Predicting Risk of Drilling Component Failure", Technical Disclosure Commons, (May 04, 2025)
https://www.tdcommons.org/dpubs_series/8073