A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield-Reference-Cited by-同舟云学术

A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield

Published:2022-04 Issue: Volume: Page:
ISSN:2405-6561
Container-title:Petroleum
language:en
Short-container-title:Petroleum

Author:

Jafarizadeh Farshad^ORCID,Larki Babak,Kazemi Bamdad,Mehrad Mohammad^ORCID,Rashidi Sina,Ghavidel Neycharan Jalil^ORCID,Gandomgoun Mehdi^ORCID,Gandomgoun Mohammad Hossein^ORCID

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Geology,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference82 articles.

1. Decision making for reduction of nonproductive time through an integrated lost circulation prediction;Moazzeni;Petrol. Sci. Technol.,2012

2. Effect of material type and size distribution on performance of loss/seepage control material;Pilehvari,2002

3. Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field;Sabah;J. Petrol. Sci. Eng.,2021

4. Applications of Artificial Intelligence Techniques in the Petroleum Industry;Hemmati-Sarapardeh,2020

5. Near wellbore stress analysis and wellbore strengthening for drilling depleted formations;Wang,2007

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