Well logging restoration based on spatial-temporal information mining from muti-scale correlation graph representation-Reference-Cited by-同舟云学术

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Well logging restoration based on spatial-temporal information mining from muti-scale correlation graph representation

Published:2024-07-22 Issue: Volume: Page:1-24
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Hao Lechuan¹²³^ORCID,Cai Bing⁴,Cao Zhimin³^ORCID,Han Jian³,Li Jing-³,Qie Dongxin⁵

Affiliation:

1. Post Doctoral Mobile Station, North-East Petroleum University, Daqing, Heilongjiang, China

2. Post Doctoral Work Station, Daqing Oil Field, Daqing, Heilongjiang, China

3. School of Physic and Electronic Engineering, North-East Petroleum University, Daqing, Heilongjiang, China

4. Test Service Technology Company, Daqing Oil Field, Daqing, Heilongjiang, China

5. Department of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China

Funder

Hainan Provincial Science and Technology Special Fund

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2024.2378205

Reference47 articles.

1. Sand-ratio distribution in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos Basin: Acoustic impedance inversion-based reservoir quality prediction

2. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

3. Nonlinear estimation of missing logs from existing well log data

4. The Determination of Lithofacies Using an Optimized Neural Network and Well Log Data

5. Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA

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