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Convolutional Neural Network and Long Short-Term Memory Integrated ROP Prediction Model Introduced Attention Mechanism

Published: 09 December 2023 Publication History

Abstract

Accurately predicting Rate of Penetration (ROP) plays a crucial role in optimizing and controlling drilling operations. Optimizing ROP not only enhances production efficiency but also reduces costs effectively. Therefore, we propose an integrated ROP prediction model that incorporates an attention mechanism into a Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM). In order to fully exploit the valuable information within the original drilling data, we first apply Fourier transform to the live drilling data for data denoising. Subsequently, we employ mutual information correlation analysis to select input data with high relevance to ROP. The denoised data is then fed into the CNN for feature extraction. Following that, we utilize the LSTM for time series analysis, effectively leveraging the long-term sequential data. Moreover, we introduce an attention mechanism to weigh the output of the LSTM layer, thus highlighting key features and improving the prediction results. Finally, through empirical simulation, we demonstrate that our proposed approach outperforms traditional CNN and CNN-LSTM methods in terms of prediction performance, showcasing its practical value.

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  • (2024)A New ROP Prediction Method Based on CNN-LSTM-Attention2024 International Conference on New Trends in Computational Intelligence (NTCI)10.1109/NTCI64025.2024.10776136(379-383)Online publication date: 18-Oct-2024

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        ISIA '23: Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation
        December 2023
        292 pages
        ISBN:9798400709401
        DOI:10.1145/3632314
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        Published: 09 December 2023

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        • the Key Research and Development Plan Project of Shaanxi Province
        • the Analysis of Difficulties in Drilling and Completion of Lower Paleozoic Horizontal Wells in Fuxian Area of Yan'an Gas Field and Implementation Plan Optimization Design

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        ISIA 2023

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        • (2024)A New ROP Prediction Method Based on CNN-LSTM-Attention2024 International Conference on New Trends in Computational Intelligence (NTCI)10.1109/NTCI64025.2024.10776136(379-383)Online publication date: 18-Oct-2024

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