Data mining and machine learning for identifying sweet spots in shale reservoirs-Reference-Cited by-同舟云学术

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Data mining and machine learning for identifying sweet spots in shale reservoirs

Published:2017-12 Issue: Volume:88 Page:435-447
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Tahmasebi Pejman,Javadpour Farzam,Sahimi Muhammad

Funder

University of Wyoming

Nano Geosciences

Mudrock Systems Research Laboratory

MSRL

University of Texas at Austin

BP

Chevron

ConocoPhillips

Eni

Marathon

Penn

Virginia

West

Pioneer

Shell

Statoil

The Unconventionals

U.S. Intercrop

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference43 articles.

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2. Neural networks for pattern recognition;Bishop,1995

3. Hybrid adaptive calibration methods and ensemble strategy for prediction of cloud point in melamine resin production;Cernuda;Chemometrics and Intelligent Laboratory Systems,2013

4. NIR-based quantification of process parameters in polyetheracrylat (PEA) production using flexible non-linear fuzzy systems;Cernuda;Chemometrics and Intelligent Laboratory Systems,2011

5. Ten years of genetic fuzzy systems: Current framework and new trends;Cordón;Fuzzy Sets and Systems,2004

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3. Locating massive syneresis fractures in shale: An experimental study;AAPG Bulletin;2024-06

4. A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field;Natural Resources Research;2024-05-14

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