Improving predictive models for rate of penetration in real drilling operations through transfer learning-Reference-Cited by-同舟云学术

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Improving predictive models for rate of penetration in real drilling operations through transfer learning

Published:2023-09 Issue: Volume:72 Page:102100
ISSN:1877-7503
Container-title:Journal of Computational Science
language:en
Short-container-title:Journal of Computational Science

Author:

Pacis Felix James^ORCID,Ambrus Adrian,Alyaev Sergey,Khosravanian Rasool,Kristiansen Tron Golder,Wiktorski Tomasz

Publisher

Elsevier BV

Subject

Modeling and Simulation,General Computer Science,Theoretical Computer Science

Reference41 articles.

1. Trends in U.S. oil and natural gas upstream costs,2022

2. Evaluation of PDC bit ROP models and the effect of rock strength on model coefficients;Soares;J. Nat. Gas Sci. Eng.,2016

3. A new approach to interpreting rock drillability;Bingham;Tech. Man. Repr. Oil Gas J.,1965

4. The perfect-cleaning theory of rotary drilling;Maurer;J. Pet. Technol.,1962

5. Drag-bit model including wear;Hareland,1994

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1. Applying transfer learning to address data scarcity: A case study on LWD gamma ray depth-lag remediation from Volve to another gasfield;Geoenergy Science and Engineering;2024-11

2. Rate of Penetration Prediction using Batch Normalized Deep Elman Neural Network;2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC);2024-06-05

3. Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models;Scientific Reports;2024-04-18

4. An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation;Results in Engineering;2023-12

5. Research on the Declining Trend of Shale Gas Production Based on Transfer Learning Methods;Processes;2023-10-29

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