Machine learning in the oil and gas industry : including geosciences, reservoir engineering, and production engineering with Python /

Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of som...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Pandey, Yogendra Narayan (Autor), Rastogi, Ayush (Autor), Kainkaryam, Sribharath (Autor), Bhattacharya, Srimoyee (Autor), Saputelli, Luigi (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Berkeley, CA] : Apress, [2020]
Temas:
Petroleum engineering > Data processing.
Petroleum industry and trade > Data processing.
Artificial intelligence > Engineering applications.
Artificial intelligence > Geophysical applications.
Machine learning.
Python (Computer program language)
Technique du pétrole > Informatique.
Pétrole > Industrie et commerce > Informatique.
Intelligence artificielle > Applications en ingénierie.
Intelligence artificielle > Applications géophysiques.
Apprentissage automatique.
Python (Langage de programmation)
Artificial intelligence > Engineering applications
Petroleum industry and trade > Data processing
Petroleum engineering > Data processing
Artificial intelligence > Geophysical applications
Computer programming
Machine learning
Open source software
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Introduction
  • Chapter 1: Toward Oil and Gas 4.0
  • Major Oil and Gas Industry Sectors
  • The Upstream Industry
  • Exploration and Appraisal
  • Field Development Planning
  • Drilling and Completion
  • Production Operations
  • Abandonment
  • The Midstream Industry
  • The Downstream Industry
  • Digital Oilfields
  • Upstream Industry and Machine Learning
  • Geosciences
  • Geophysical Modeling
  • Automated Fault Interpretation
  • Automated Salt Identification
  • Seismic Interpolation
  • Seismic Inversion
  • Geological Modeling
  • Petrophysical Modeling
  • Facies Classification
  • Reservoir Engineering
  • Field Development Planning
  • Assisted History Matching
  • Production Forecasting and Reserve Estimation
  • Drilling and Completion
  • Automated Event Recognition and Classification
  • Non-Productive Time (NPT) Minimization
  • Early Kick Detection
  • Stuck Pipe Prediction
  • Autonomous Drilling Rigs
  • Production Engineering
  • Workover Opportunity Candidate Recognition
  • Production Optimization
  • Infill Drilling
  • Optimal Completion Strategy
  • Predictive Maintenance
  • Industry Trends
  • Model Interpretability
  • Exploratory Data Analysis (EDA)
  • Supervised Learning
  • Regression
  • Multiple Linear Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
  • XGBoost: eXtreme Gradient Boosting
  • Artificial Neural Network
  • Comparison of the Regression Models
  • Classification
  • Multinomial Logistic Regression
  • Support Vector Classifier
  • Decision Tree Classifier
  • Random Forest Classifier
  • k-Nearest Neighbors (k-NN)
  • Gaussian Naive Bayes Classification
  • Linear Discriminant Analysis
  • Comparison of Classification Models

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