Intelligent prediction of fracture parameters in ultra-deep carbonate rocks based on knowledge and data dual drive-Reference-Cited by-同舟云学术

Intelligent prediction of fracture parameters in ultra-deep carbonate rocks based on knowledge and data dual drive

Published:2022-10-06 Issue: Volume: Page:1-24
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Wan Youwei^ORCID,Wang Bei¹,Liu Xiangjun²^ORCID,Li Longxin¹,Xiong Jian²,Cai Junjun¹,Liu Xixiang¹

Affiliation:

1. Research Institute of Petroleum Exploration and Development, PetroChina Southwest Oil and Gas Field Company, Chengdu, P. R. China

2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, P. R. China

Funder

Major Science and Technology Special Project of China Thirteenth Five-year Plan

CNPC Major Science and Technology Project

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2022.2130358

Reference39 articles.

1. Fractured zones detection using conventional petrophysical logs by differentiation method and its correlation with image logs

2. Estimation of Petroleum Reservoir Parameters Using an Integrated Approach Neural Network, Principal Component Analysis and Fisher Discriminant Analysis

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

4. Fracture characterization and modeling from virtual outcrops

5. Prediction of shale gas horizontal wells productivity after volume fracturing using machine learning – an LSTM approach