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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Bibliographic Details
Published in:IEEE access Vol. 12; p. 1
Main Authors: Gao, Yi, Wang, Na, Ma, Yihao
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 modeling, which cannot meet the accuracy and stability requirements of wellbore trajectory prediction. The long short-term memory neural network (LSTM) for predicting time series can achieve accurate prediction, but there are problems such as difficulty in adjusting the hyperparameters of the LSTM model, slow convergence speed, and easy overfitting. This paper absorbs the advantages of the LSTM algorithm, ridge regression (L2 regularization), and sparrow optimization algorithm (SSA) in machine learning and proposes a well trajectory prediction model of steerable drilling based on L2 regularization and SSA optimized LSTM (L2-SSA-LSTM). The model takes the LSTM hyperparameter as the parameter optimization goal of SSA and adds L2 regularization to the model to prevent model overfitting to complete modeling and prediction. The experiment was conducted using measured data sets from directional drilling in two different oilfields. The results show that compared with the back propagation algorithm (BP), consolidated memory gated recurrent unit (CMGRU), dual-thread gated recurrent unit (DTGRU), Attention-based Spatiotemporal Graph Recurrent Neural Network (ASTG-RNN), LSTM, and the L2-SSA-LSTM prediction model has significantly higher accuracy in predicting directional drilling trajectories than other models and has the better predictive ability.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3347611