Optimization of Oil Well Production Prediction Model Based on Inter-Attention and BiLSTM

Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) Vol. 14; no. 5; p. 1004
Main Authors: Meng, Xin, Liu, Xingyu, Duan, Hancong, Hu, Ze, Wang, Min
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Computational linguistics
Data processing
Decision-making
Deep learning
Efficiency
Language processing
Machine learning
Mathematical optimization
Natural language interfaces
Neural networks
Oil wells
Optimization
Optimization algorithms
Petroleum industry
Petroleum mining
Prediction models
Quadratic programming
Search algorithms
Simulation
China
ISSN:2079-9292, 2079-9292
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention Mechanism (IAM) and Bidirectional Long Short-Term Memory Network (BiLSTM), optimized using a Comprehensive Search Algorithm (CSA). By incorporating the Inter-Attention Mechanism, the model enhances its capacity to model complex time-series data. The CSA, combined with Sequential Quadratic Programming (SQP) and Monotone Basin Hopping (MBH) algorithms, ensures both global and local parameter optimization. Using historical data from an oil well in Sichuan, the feasibility of the proposed model was validated, demonstrating superior accuracy and robustness compared to other prediction models and optimization algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14051004