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Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
Best Practices, Tools, and Case Studies

by Patrick Bangert

  • Publisher : Gulf Professional Publishing
  • Release : 2021-03-04
  • Pages : 306
  • ISBN : 0128209143
  • Language : En, Es, Fr & De
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Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry
Including Geosciences, Reservoir Engineering, and Production Engineering with Python

by Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli

  • Publisher : Apress
  • Release : 2020-11-03
  • Pages : 300
  • ISBN : 9781484260937
  • Language : En, Es, Fr & De
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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 Who This Book Is For Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems.

Modern Data Analytics

Modern Data Analytics
Applied AI and Machine Learning for Oil and Gas Industry

by Tatyana Plaksina

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 72
  • ISBN : 9781775371281
  • Language : En, Es, Fr & De
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Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Best Practices, Tools, and Case Studies

by Patrick Bangert

  • Publisher : Elsevier
  • Release : 2021-01-25
  • Pages : 274
  • ISBN : 0128226005
  • Language : En, Es, Fr & De
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Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Data Analytics in Reservoir Engineering

Data Analytics in Reservoir Engineering
A Book

by Sathish Sankaran,Sebastien Matringe,Mohamed Sidahmed

  • Publisher : Unknown Publisher
  • Release : 2020-10-29
  • Pages : 108
  • ISBN : 9781613998205
  • Language : En, Es, Fr & De
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Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Applications of Artificial Intelligence Techniques in the Petroleum Industry

Applications of Artificial Intelligence Techniques in the Petroleum Industry
A Book

by Abdolhossein Hemmati Sarapardeh,Aydin Larestani,Nait Amar Menad,Sassan Hajirezaie

  • Publisher : Gulf Professional Publishing
  • Release : 2020-08-26
  • Pages : 322
  • ISBN : 0128223855
  • Language : En, Es, Fr & De
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Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models
A Book

by Keith R. Holdaway,Duncan H. B. Irving

  • Publisher : John Wiley & Sons
  • Release : 2017-10-09
  • Pages : 368
  • ISBN : 1119215102
  • Language : En, Es, Fr & De
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Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Math for Programmers

Math for Programmers
3D graphics, machine learning, and simulations with Python

by Paul Orland

  • Publisher : Manning Publications
  • Release : 2021-01-12
  • Pages : 688
  • ISBN : 1617295353
  • Language : En, Es, Fr & De
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In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks

Advances in Analytics and Applications

Advances in Analytics and Applications
A Book

by Arnab Kumar Laha

  • Publisher : Springer
  • Release : 2018-09-07
  • Pages : 297
  • ISBN : 9811312087
  • Language : En, Es, Fr & De
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This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

Data Analytics in the Era of the Industrial Internet of Things

Data Analytics in the Era of the Industrial Internet of Things
A Book

by Aldo Dagnino

  • Publisher : Springer
  • Release : 2021-02-16
  • Pages : 129
  • ISBN : 9783030631383
  • Language : En, Es, Fr & De
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This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

Transactional Machine Learning with Data Streams and AutoML

Transactional Machine Learning with Data Streams and AutoML
Build Frictionless and Elastic Machine Learning Solutions with Apache Kafka Using Python

by Sebastian Maurice

  • Publisher : Apress
  • Release : 2021-08-13
  • Pages : 240
  • ISBN : 9781484270226
  • Language : En, Es, Fr & De
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Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.

Data Science Revealed

Data Science Revealed
With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning

by Tshepo Chris Nokeri

  • Publisher : Apress
  • Release : 2021-03-21
  • Pages : 252
  • ISBN : 9781484268698
  • Language : En, Es, Fr & De
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Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers

Intelligent System and Applied Material

Intelligent System and Applied Material
A Book

by Jin Hui Wu,Mao Tai Zhao,Wu Bo

  • Publisher : Trans Tech Publications Ltd
  • Release : 2012-02-10
  • Pages : 1510
  • ISBN : 3038138010
  • Language : En, Es, Fr & De
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Volume is indexed by Thomson Reuters CPCI-S (WoS). The 2012 International Conference on Intelligent Systems and Applied Material s(GSAM 2012) was the premier forum for the presentation of technological advances and research results in these fields. The proceedings comprise 288 peer-reviewed papers which should be required reading matter for anyone dealing with these topics.

Oil & Gas Science and Technology

Oil & Gas Science and Technology
Revue de L'Institut Français Du Pétrole

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Python Machine Learning for Beginners

Python Machine Learning for Beginners
Learning from Scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn, and TensorFlow for Machine Learning and Data Science

by Ai Publishing

  • Publisher : Unknown Publisher
  • Release : 2020-10-23
  • Pages : 302
  • ISBN : 9781734790153
  • Language : En, Es, Fr & De
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Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay.Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future.But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions. Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast.How Is This Book Different?AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science.Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book--references, PDFs, Python codes, and exercises--on the publisher's website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You'll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started. The topics covered include: Introduction and Environment Setup Python Crash Course Python NumPy Library for Data Analysis Introduction to Pandas Library for Data Analysis Data Visualization via Matplotlib, Seaborn, and Pandas Libraries Solving Regression Problems in ML Using Sklearn Library Solving Classification Problems in ML Using Sklearn Library Data Clustering with ML Using Sklearn Library Deep Learning with Python TensorFlow 2.0 Dimensionality Reduction with PCA and LDA Using Sklearn Click the BUY NOW button to start your Machine Learning journey.

Practical Automated Machine Learning on Azure

Practical Automated Machine Learning on Azure
Using AutoML to Build and Deploy Intelligent Solutions

by Deepak Mukunthu,Parashar Shah,Wee Hyong Tok

  • Publisher : O'Reilly Media
  • Release : 2019-10-15
  • Pages : 225
  • ISBN : 9781492055594
  • Language : En, Es, Fr & De
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Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you'll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

MACHINE LEARNING WITH PYTHON

MACHINE LEARNING WITH PYTHON
A Book

by Abhishek Vijayvargia

  • Publisher : BPB Publications
  • Release : 2018-06-02
  • Pages : 266
  • ISBN : 9387284883
  • Language : En, Es, Fr & De
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DescriptionThis book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the data.In this book we provide code examples in python. Python is the most suitable and worldwide accepted language for this. First, it is free and open source. It contains very good support from open community. It contains a lot of library, so you don't need to code everything. Also, it is scalable for large amount of data and suitable for big data technologies.This book:Covers all major areas in Machine Learning.Topics are discussed with graphical explanations.Comparison of different Machine Learning methods to solve any problem.Methods to handle real-world noisy data before applying any Machine Learning algorithm.Python code example for each concept discussed.Jupyter notebook scripts are provided with dataset used to test and try the algorithms ContentsIntroduction to Machine Learning Understanding Python Feature Engineering Data VisualisationBasic and Advanced Regression techniquesClassification Un Supervised LearningText AnalysisNeural Network and Deep Learning Recommendation System Time Series Analysis

Advances in Artificial Intelligence

Advances in Artificial Intelligence
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2004
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Bulletin of the Oil & Natural Gas Commission

Bulletin of the Oil & Natural Gas Commission
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1966
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Formulas and Calculations for Petroleum Engineering

Formulas and Calculations for Petroleum Engineering
A Book

by Cenk Temizel,Tayfun Tuna,Mehmet Melih Oskay,Luigi A. Saputelli

  • Publisher : Gulf Professional Publishing
  • Release : 2019-08-15
  • Pages : 548
  • ISBN : 0128165553
  • Language : En, Es, Fr & De
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Formulas and Calculations for Petroleum Engineering unlocks the capability for any petroleum engineering individual, experienced or not, to solve problems and locate quick answers, eliminating non-productive time spent searching for that right calculation. Enhanced with lab data experiments, practice examples, and a complimentary online software toolbox, the book presents the most convenient and practical reference for all oil and gas phases of a given project. Covering the full spectrum, this reference gives single-point reference to all critical modules, including drilling, production, reservoir engineering, well testing, well logging, enhanced oil recovery, well completion, fracturing, fluid flow, and even petroleum economics. Presents single-point access to all petroleum engineering equations, including calculation of modules covering drilling, completion and fracturing Helps readers understand petroleum economics by including formulas on depreciation rate, cashflow analysis, and the optimum number of development wells