|
|
|
|
| LEADER |
06191nmm a2200625 u 4500 |
| 001 |
EB001932136 |
| 003 |
EBX01000000000000001095038 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 9781484260944
|
| 050 |
|
4 |
|a TN871
|
| 100 |
1 |
|
|a Pandey, Yogendra Narayan
|
| 245 |
0 |
0 |
|a Machine learning in the oil and gas industry
|b including geosciences, reservoir engineering, and production engineering with Python
|c Yogendra Narayan Pandey, Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, Luigi Saputelli
|
| 260 |
|
|
|a [Berkeley, CA]
|b Apress
|c 2020
|
| 300 |
|
|
|a 1 online resource
|b illustrations
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a Includes bibliographical references and index
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a 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
|
| 653 |
|
|
|a Petroleum engineering / Data processing
|
| 653 |
|
|
|a Python (Computer program language) / fast
|
| 653 |
|
|
|a Technique du pétrole / Informatique
|
| 653 |
|
|
|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
|
| 653 |
|
|
|a Petroleum industry and trade / Data processing / fast
|
| 653 |
|
|
|a Artificial intelligence / Geophysical applications / http://id.loc.gov/authorities/subjects/sh2002004417
|
| 653 |
|
|
|a Open source software / fast
|
| 653 |
|
|
|a Pétrole / Industrie et commerce / Informatique
|
| 653 |
|
|
|a Petroleum engineering / Data processing / fast
|
| 653 |
|
|
|a Petroleum industry and trade / Data processing
|
| 653 |
|
|
|a Machine learning / fast
|
| 653 |
|
|
|a Artificial intelligence / Engineering applications / http://id.loc.gov/authorities/subjects/sh2009007735
|
| 653 |
|
|
|a Apprentissage automatique
|
| 653 |
|
|
|a Intelligence artificielle / Applications en ingénierie
|
| 653 |
|
|
|a Artificial intelligence / Engineering applications / fast
|
| 653 |
|
|
|a Python (Langage de programmation)
|
| 653 |
|
|
|a Computer programming / fast
|
| 653 |
|
|
|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
|
| 653 |
|
|
|a Artificial intelligence / Geophysical applications / fast
|
| 653 |
|
|
|a Intelligence artificielle / Applications géophysiques
|
| 700 |
1 |
|
|a Rastogi, Ayush
|e author
|
| 700 |
1 |
|
|a Kainkaryam, Sribharath
|e author
|
| 700 |
1 |
|
|a Bhattacharya, Srimoyee
|e author
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 028 |
5 |
0 |
|a 10.1007/978-1-4842-6094-4
|
| 776 |
|
|
|z 1484260937
|
| 776 |
|
|
|z 9781484260944
|
| 776 |
|
|
|z 1484260945
|
| 776 |
|
|
|z 9781484260937
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781484260944/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 381
|
| 082 |
0 |
|
|a 622.3380285
|
| 082 |
0 |
|
|a 620
|
| 520 |
|
|
|a 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 some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will Learn Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used Study interesting industry problems that are good candidates for being solved by machine and deep learning Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry
|