Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process-Reference-Cited by-同舟云学术

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Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process

Published:2023-11 Issue: Volume:230 Page:212187
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Gan Chao,Wang Xiang,Wang Lu-Zhao,Cao Wei-Hua,Liu Kang-Zhi^ORCID,Gao Hui,Wu Min

Funder

Higher Education Discipline Innovation Project

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference26 articles.

1. Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network;Anemangely;J. Geophys. Eng.,2018

2. Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field;Ashrafi;J. Pet. Sci. Eng.,2019

3. Optimization of controllable drilling parameters using a novel geomechanics-based workflow;Bajolvand;J. Pet. Sci. Eng.,2022

4. Towards drilling rate of penetration prediction: Bayesian neural networks for uncertainty quantification;Bizhani;J. Pet. Sci. Eng.,2022

5. Dynamic machine learning-based optimization algorithm to improve boiler efficiency;Blackburn;J. Process Control,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data Integration Enabling Advanced Machine Learning ROP Predictions and its Applications;Day 4 Thu, May 09, 2024;2024-04-29

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