N479 Applied Statistical Modeling and Big Data Analytics
Course Facts
- Course Code:
- N479
- Duration:
- 3 days
- Type:
- Classroom
- CEU:
- 2.4 Continuing Education Units
- PDH:
- 24 Professional Development Hours
- Certificate:
- Certificate Issued Upon Completion
Summary
Buisness Impact: This training course will provide a hands-on introduction to statistical modeling and big data analytics so that particpants can use them for petroleum engineering and geoscience applications.
Topics to be covered include: (a) easy-to-understand descriptions of the commonly-used techniques, (b) case studies demonstrating the applicability, limitations and value-added proposition for these methods, and (c) hands-on problems sessions using open source and/or commercial software. This course will provide engineers and geologists with practical techniques for identifying hidden patterns and relationships in large datasets and extracting data-driven insights towards actionable information that can contribute to lower cost, improved efficiency and/or increased productivity in oil and gas operations. This class will arm petroleum engineers and geoscientists with advanced capabilities to extract new insights from E&P data that can help: (a) learn hidden patterns and relationships in geologic datasets, (b) identify production sweet spots in developed plays; (c) determine factors responsible for separating good wells from poor producers wells, (d) build fast surrogate models of reservoir performance, and (e) assist in predictive maintenance by identifying failure inducing conditions from historical records.
For a more in depth summary of D479 please use the following link to watch Dr. Mishra discuss his course in detail:
Duration and Training Method
A three-day classroom course consisting of lectures interspersed with worked examples, hands-on exercises and discussions (Days 1-2). Participants will then build and present a machine learning driven model as a capstone group projects (Day 3).
Participants will learn to:
- Apply foundational concepts in probability and statistics for basic data analysis
- Perform linear regression for building simple input-output models
- Conduct multivariate data reduction and clustering for finding sub-groups of data that have similar attributes
- Apply machine learning techniques for regression and classification for developing data-driven input-output models
- Converse with confidence about big data, data analytics and machine learning terminology and fundamental concepts, and critique statistical modeling and data analytics studies
- Foundational Concepts (Day 1)
- Big data technologies, basic data analytics and machine learning terminology/concepts
- Data, statistics, and probability
- Distributions (models, fitting distributions to data)
- Inference (Confidence limits, bootstrap, significance tests, Analysis of variance)
- Basic Regression Analysis (Day 1)
- Linear regression (univariate and multivariate regression)
- Understanding regression statistics
- Non-parametric regression
- Multivariate Data Analysis (Day 1)
- Dimension reduction (Principal component analysis)
- Cluster analysis (K-means, Hierarchical clustering, self-organizing maps)
- Data visualization
- Machine Learning Basics (Day 2)
- Overview of techniques
- Evaluating model performance (model validation, goodness-of-fit, common pitfalls)
- Variable importance
- Model aggregation
- Machine Learning for Regression and Classification (Day 2)
- Tree-based methods (decision trees, Random forest, Gradient boosting machine)
- Advanced methods (Neural network, Support vector machine
- Group Projects (Day 3)
- Participants will be divided into multi-disciplinary groups, and will develop and present a machine learning model as a capstone project
- Wrap-up (Day 3)
- Key takeaways and resources
- Data analytics do’s and don’t’s
Who should attend
This course is for designed for petroleum engineers, geoscientists, and managers interested in becoming smart users of statistical modeling and data analytics.
Prerequisites and linking courses
Participants should have a basic knowledge of statistics or should have attended N480 (Introduction to Statistical Modeling & Big Data Analytics).
Click on a name to learn more about the instructor
Srikanta Mishra
Srikanta Mishra
Background
Dr. Srikanta Mishra is Senior Research Leader and Discipline Lead for Reservoir Sciences and Data Analytics at Battelle Memorial Institute, the world's largest independent contract R&D organization. He is responsible for leading a technology portfolio related to computational modeling and data analytics for geological carbon storage, shale gas/oil development and improved oil recovery projects. Dr. Mishra has taught short courses on statistical modeling, data analytics and uncertainty quantification at various professional conferences and client locations in the US, China, Spain, Japan, India, Finland, Belgium and Switzerland.
He is author of the book “Applied Statistical Modeling and Data Analytics for the Petroleum Geosciences” recently published by Elsevier as well as ~200 technical publications. Dr. Mishra has been selected as an SPE Distinguished Lecturer for 2018-19 on the topic of Big Data Analytics. He holds a PhD degree from Stanford University, an MS degree from University of Texas and a BTech degree from Indian School of Mines – all in Petroleum Engineering.
He has also served as an Adjunct Professor of Petroleum and Geosystems Engineering at The University of Texas at Austin.
Affiliations and Accreditation
PhD Stanford University - Petroleum Engineering
MS Stanford University - Petroleum Engineering
BTech Indian School of Mines - Petroleum Engineering
Courses Taught
N479: Applied Statistical Modeling and Big Data Analytics
N480: Introduction to Statistical Modeling and Big Data Analytics
Dates for this Course
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