L2-SSA-LSTM prediction model of steering drilling wellbore trajectory

The high-speed vibration rotation of the drill bit during drilling causes the logging tool to be damaged or distorted, resulting in inaccurate or lost data collection. Traditional prediction methods such as dynamic modeling and geological modeling have problems such as incomplete data and difficult...

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Veröffentlicht in:	IEEE access Jg. 12; S. 1
Hauptverfasser:	Gao, Yi, Wang, Na, Ma, Yihao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Back propagation networks Computational modeling Data collection Data models Drill bits Drilling Drilling machines (tools) Dynamic models L2 regularization Logic gates long short-term memory neural network Machine learning Modelling Neural networks Oil fields Optimization Optimization methods Prediction models Predictions Predictive models Recurrent neural networks Regularization sparrow optimization algorithm Steering Steering drilling system Training Trajectory wellbore trajectory prediction
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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