应用基于机器学习的模型,利用测井数据预测地质力学参数

IF 2.4 4区 工程技术 Q3 ENERGY & FUELS
Manouchehr Sanei, A. Ramezanzadeh, M. R. Delavar
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引用次数: 1

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。

Applied machine learning-based models for predicting the geomechanical parameters using logging data

Applied machine learning-based models for predicting the geomechanical parameters using logging data
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来源期刊
CiteScore
5.90
自引率
4.50%
发文量
151
审稿时长
13 weeks
期刊介绍: The Journal of Petroleum Exploration and Production Technology is an international open access journal that publishes original and review articles as well as book reviews on leading edge studies in the field of petroleum engineering, petroleum geology and exploration geophysics and the implementation of related technologies to the development and management of oil and gas reservoirs from their discovery through their entire production cycle. Focusing on: Reservoir characterization and modeling Unconventional oil and gas reservoirs Geophysics: Acquisition and near surface Geophysics Modeling and Imaging Geophysics: Interpretation Geophysics: Processing Production Engineering Formation Evaluation Reservoir Management Petroleum Geology Enhanced Recovery Geomechanics Drilling Completions The Journal of Petroleum Exploration and Production Technology is committed to upholding the integrity of the scientific record. As a member of the Committee on Publication Ethics (COPE) the journal will follow the COPE guidelines on how to deal with potential acts of misconduct. Authors should refrain from misrepresenting research results which could damage the trust in the journal and ultimately the entire scientific endeavor. Maintaining integrity of the research and its presentation can be achieved by following the rules of good scientific practice as detailed here: https://www.springer.com/us/editorial-policies
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