Version 1
: Received: 30 October 2023 / Approved: 30 October 2023 / Online: 31 October 2023 (05:20:23 CET)
How to cite:
Wang, F.; Zhang, X.; Gao, G.; Li, X.; He, X. High-Precision Temperature Compensation Method for Downhole Pressure Sensors in Oil and Gas Wells Based on the C-I-WOA-BP Neural Network. Preprints2023, 2023101962. https://doi.org/10.20944/preprints202310.1962.v1
Wang, F.; Zhang, X.; Gao, G.; Li, X.; He, X. High-Precision Temperature Compensation Method for Downhole Pressure Sensors in Oil and Gas Wells Based on the C-I-WOA-BP Neural Network. Preprints 2023, 2023101962. https://doi.org/10.20944/preprints202310.1962.v1
Wang, F.; Zhang, X.; Gao, G.; Li, X.; He, X. High-Precision Temperature Compensation Method for Downhole Pressure Sensors in Oil and Gas Wells Based on the C-I-WOA-BP Neural Network. Preprints2023, 2023101962. https://doi.org/10.20944/preprints202310.1962.v1
APA Style
Wang, F., Zhang, X., Gao, G., Li, X., & He, X. (2023). High-Precision Temperature Compensation Method for Downhole Pressure Sensors in Oil and Gas Wells Based on the C-I-WOA-BP Neural Network. Preprints. https://doi.org/10.20944/preprints202310.1962.v1
Chicago/Turabian Style
Wang, F., Xintong Li and Xuzhi He. 2023 "High-Precision Temperature Compensation Method for Downhole Pressure Sensors in Oil and Gas Wells Based on the C-I-WOA-BP Neural Network" Preprints. https://doi.org/10.20944/preprints202310.1962.v1
Abstract
The Internet of Things (IoT) aims to connect all objects and facilitate data interchange, with data accuracy being paramount. While IoT has seen extensive adoption in conventional sectors, its application in the petroleum industry, especially with downhole equipment, poses challenges due to the extreme high-temperature and high-pressure conditions, making accurate measurements elusive. This Paper we aim to enhance the accuracy of downhole pressure measurements in slim-hole drilling parameter monitoring, mitigate the temperature influences on strain gauges, and surmount the intrinsic limitations of the traditional BP neural network, such as sluggish convergence speeds, high sensitivity to initial weights and biases, unstable learning rates, and a tendency to fall into local minima. This study introduces a temperature compensation model using a chaotic-initiated adaptive whale optimization algorithm (C-I-WOA) to optimize a BP neural network, which is henceforth referred to as the C-I-WOA-BP model. Initially, we harnessed chaotic mapping techniques to refine the initialization process of the whale optimization algorithm (WOA) population, thereby bolstering the heterogeneity of the algorithm. Subsequently, we integrated an adaptive weight recalibration mechanism, refined the encompassing methodology of the WOA, and bolstered its capabilities for expansive global scouting and meticulous local refinement in the solution arena. Subsequently, the WOA was used to optimize the weight coefficients of the BP neural network. Finally, we conducted a detailed comparative analysis by juxtaposing the performance metrics of the BP, C-WOA-BP, I-WOA-BP, and C-I-WOA-BP networks. The experimental results indicate that the C-I-WOA-BP model demonstrates faster convergence speed, superior global search capabilities, and enhanced local exploitation potential compared to the other models. Additionally, it provides a more stable and accurate reduction in temperature-induced errors in drilling pressure parameter measurements.
Keywords
BP neural network; temperature compensation model; chaotic mapping; whale optimization algorithm; adaptive weight
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
