Evaluating Multi-target Regression Framework for Dynamic Condition Prediction in Wellbore
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in: The Arabian journal for science and engineering, Jahrgang 49.2024, Nr. June, 23.04.2024, S. 8953-8982.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
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TY - JOUR
T1 - Evaluating Multi-target Regression Framework for Dynamic Condition Prediction in Wellbore
AU - Keshavarz, Sahar
AU - Elmgerbi, Asad
AU - Vita, Petr
AU - Thonhauser, Gerhard
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/4/23
Y1 - 2024/4/23
N2 - In recent years, the focus has shifted towards leveraging physics-based modelling and data-driven analysis to predict drilling incidents and anomalies in real time, with the goal of reducing non-productive periods. However, much of this attention has directed at specific drilling operations like drilling and tripping, leaving other vital processes, such as wellbore conditioning, comparatively overlooked. The primary objective of this study is to employ data-driven techniques for predicting the dynamic state of the wellbore by utilising sensor data, operating parameters, and surface measurements. Accurate predictions are pivotal for automating these processes, promising significant savings in both redundant time and associated costs, ultimately elevating operational efficiency.In this research, the surface drilling parameters such as flowrate, rotation speed, block position, and drill string length are incorporated with the surface measurements such as hookload, pressure, and torque during wellbore conditioning operation to predict further surface sensor measurements. Different parameter settings are evaluated to find the best approach. Six supervised learning algorithms are used to select the best prediction method. The findings reveal that considering all surface parameters and measurements yields the most accurate predictions. Among various single and multi-target regression methods, including deep learning approaches, the Gaussian process and random forest models exhibit the lowest prediction errors.By reliably predicting and understanding wellbore behaviour, this research paves the way for more efficient and autonomous drilling operations in the future, bridging a critical gap in the industry's automation capabilities.
AB - In recent years, the focus has shifted towards leveraging physics-based modelling and data-driven analysis to predict drilling incidents and anomalies in real time, with the goal of reducing non-productive periods. However, much of this attention has directed at specific drilling operations like drilling and tripping, leaving other vital processes, such as wellbore conditioning, comparatively overlooked. The primary objective of this study is to employ data-driven techniques for predicting the dynamic state of the wellbore by utilising sensor data, operating parameters, and surface measurements. Accurate predictions are pivotal for automating these processes, promising significant savings in both redundant time and associated costs, ultimately elevating operational efficiency.In this research, the surface drilling parameters such as flowrate, rotation speed, block position, and drill string length are incorporated with the surface measurements such as hookload, pressure, and torque during wellbore conditioning operation to predict further surface sensor measurements. Different parameter settings are evaluated to find the best approach. Six supervised learning algorithms are used to select the best prediction method. The findings reveal that considering all surface parameters and measurements yields the most accurate predictions. Among various single and multi-target regression methods, including deep learning approaches, the Gaussian process and random forest models exhibit the lowest prediction errors.By reliably predicting and understanding wellbore behaviour, this research paves the way for more efficient and autonomous drilling operations in the future, bridging a critical gap in the industry's automation capabilities.
KW - Supervised learning
KW - Wellbore monitoring
KW - Surface drilling parameters
KW - Wellbore conditioning
KW - Multi-targetregression
KW - Multi-target regression
UR - http://www.scopus.com/inward/record.url?scp=85191098234&partnerID=8YFLogxK
U2 - 10.1007/s13369-024-09044-z
DO - 10.1007/s13369-024-09044-z
M3 - Article
VL - 49.2024
SP - 8953
EP - 8982
JO - The Arabian journal for science and engineering
JF - The Arabian journal for science and engineering
SN - 2191-4281
IS - June
ER -