Zare, Bahare (2024) Sonic log depth series predictions using machine learning algorithms. Masters thesis, Memorial University of Newfoundland.
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Abstract
This paper investigates the viability of predicting rock properties within wells using real-time drilling data. Among these properties, the sonic log plays a crucial role in understanding the physical characteristics of subsurface formations and helps geoscientists and drilling engineers interpret the subsurface geology and make informed decisions about well construction, drilling parameters, and reservoir performance. Successfully forecasting sonic logs has the potential to significantly improve the optimization of fracturing processes in wells with similar geological structures. To accomplish this objective, we introduced a well-structured eXtreme Gradient Boosting (XGBoost), LSTM (Long Short-Term Memory), and Random Forest (RF) models that utilize depth-series data for predicting sonic log in the field of oil and gas exploration. The data used in this research was gathered from the drilling project “A Data Analytics Approach to Energy and Safety Improvements” which received funding from the NL Offshore Oil and Gas Industry Recovery Assistance Fund.
| Item Type: | Thesis (Masters) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/16521 |
| Item ID: | 16521 |
| Additional Information: | Includes bibliographical references (pages 140-145) -- Restricted until May 21, 2025 |
| Keywords: | sonic log, machine learning, XGBoost, random forest, LSTM |
| Department(s): | Science, Faculty of > Computer Science |
| Date: | May 2024 |
| Date Type: | Submission |
| Library of Congress Subject Heading: | Oil well logging, Acoustic; Rock mechanics; Machine learning; XGBoost |
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