Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs-Reference-Cited by-同舟云学术

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Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

Published:2022-09-05 Issue: Volume: Page:
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Gu Yufeng¹^ORCID,Zhang Shenghan²,Wang Weidong³,Pan Lili³,Zhang Daoyong¹,Bao Zhidong⁴

Affiliation:

1. Strategic Research Center of Oil and Gas Resources Ministry of Natural Resources Beijing China

2. Sinopec Geophysical Research Institute Nanjing Jiangsu China

3. Oil Production Plant 5 PetroChina Changqing Oilfield Company Shaanxi, Xi'an China

4. College of Geosciences China University of Petroleum (Beijing) Beijing China

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13258

Reference74 articles.

1. A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs;Al‐Anazi A.;Engineering Geology,2010

2. Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

3. An incomplete correlation between pre-salt topography, top reservoir erosion, and salt deformation in deep-water Santos Basin (SE Brazil)

4. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield

5. Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia

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