Oil and Gas
Oil and Gas | Data Analytics
Business Impact: This course is designed to show geoscience domain specialists how to improve cost efficiencies, and both technical and economic success through a better understanding of geostatistical methods, practical data science, and the rapidly evolving computer informatic systems.
Geoscientists are struggling to keep pace with rapidly evolving computational environments and the need to integrate modern data science approaches to improve geostatistical modeling of petroleum reservoirs. Sophisticated software for conventional petroleum reservoir modeling has successfully provided tools and workflows for almost 40 years overcoming a broad range of barriers due to reservoir structural complexity and heterogeneity. Successful modeling of unconventional reservoirs is not as well understood, providing new technical challenges to define reservoir sweet-spots, well placement, completion strategies, and production development. Both conventional and unconventional reservoir modeling are undergoing substantial changes due to rapidly changing computational environments to accommodate “big data,” high performance computing, distributed computing, and the application and integration of data science. The necessity for petroleum geoscience and engineering professionals to understand the changes that are taking place and develop collaboration skills with computer scientists, developers, and data scientists is critical in order to ensure their professional sustainability.
Duration and Training Method
This is a classroom or virtual classroom course comprising a mixture of lectures, discussion, case studies, and hands-on exercises to be completed by participants during and between sessions.
- Describe and identify basic practical uses of geostatistical principles.
- Communicate basic modeling informatic needs and objectives to computer specialists (model size, platform, memory, scalability, etc.)
- Communicate data analytics objectives for model building to data scientists (treatment of missing values, sparse data, univariate and bivariate statistics).
- Properly install R, Rmarkdown, and necessary code packages.
- Import data from Excel (or other) and build a Data Frame.
- Recognize, describe, and navigate the Integrated Development Environment (IDE) for RMarkdown.
- Run existing RMarkdown code and make simple modifications.
- Identify the important univariate and bivariate statistical metrics from input data for use in preparing and building static reservoir models.
- Describe the basic requirements for building geostatistical models.
- Modify RGeostats scripts to construct omnidirectional and directional experimental semivariograms and fit them with authorized variogram models.
- Modify RGeostats to construct kriging and conditionally simulated maps.
- Modify RMarkdown code to run Machine Learning and other data science methods to enhance static petroleum reservoir models.
- What is geostatistics?
- What are informatic systems?
- What is data science?
- Introduction to Data Science
- Data Science Principles - How is it different from geostatistics and why do we need it?
- Data Science Platforms - R and Python
- Scalability and informatic systems
- Introduction to Data Science (Cont)
- R and RStudio practicum
- Setting up R and RStudio
- Practical Data Science and Geostatistical Reservoir Modeling
- Exploratory data analytics
- Data analytics practica:
- data tables and data frames
- univariate and bi-variate analytics
- Basic Spatial Modeling
- Experimental Semivariograms
- Variogram Practicum:
- constructing the experimental semivariogram
- variogram modeling practicum:
- modeling the experimental semivariogram
- Basic Kriging and Conditional Simulation
- Kriging and Conditional Simulation
- Kriging and Conditional Simulation Practica
- Interpolation with Kriging
- Uncertainty modeling and Conditional Simulation
- Integrated multivariate analytics and geostatistics
- Data Science, Kriging and Conditional Simulation practicum:
- Closing discussion and comments
Who Should Attend and Prerequisites
This course is intended for geoscientists and engineers who are interested in learning the basic fundamentals of geostatistical reservoir modeling in the context of data science. Participants will be introduced to a variety of statistical geostatistical methods for building statical reservoir models using continuous interface (CI) languages like R and Python.
Dr. Yarus has 45 years’ experience in quantitative statistical analysis of geoscience data and computational earth modeling. Noted expert in the applied field of subsurface reservoir characterization and spatial statistics, Dr. Yarus is well known for his various publications and lectures on applied geostatistics which are given worldwide. In 2016, he received the distinguished George Matheron Lecture Award from the International Association for Mathematical Geosciences. In 1994 he published the first book on principles, methods and case studies in geostatistics for the American Association of Petroleum Geologists entitled, “Stochastic Modeling and Geostatistics.” This was followed by 2 additional edited volumes on graphic information systems and geostatistical applications in 2000, and 2006, respectively.
In 2007, Dr. Yarus was invited to contribute the first-ever dedicated chapter on “Geologically Based Geostatistics” to the Society of Petroleum Engineering’s prestigious “Petroleum Engineering Handbook.” He has worked for both Major Oil and Service Companies including Amoco, Marathon, Beicip-Franlab, and ROXAR, where he cultivated experience covering production, exploration, consulting, and software development. His experiences also included initiating and managing his own consulting company, Quantitative Geosciences (QGSI) which provided consulting and research services across many disciplines including Petroleum, Environmental, Water Resources, and Climatology. QGSI was acquired Halliburton in 2006 where Dr. Yarus was asked to manage a team of professional geoscientists, engineers, and software developers to build the successful, “DecisionSpace Earth Modeling” software. In 2013, Dr. Yarus became a Senior Technology Fellow at Halliburton where he reported to the executive staff.
Throughout his career, he has maintained strong university relationships working as an adjunct professor at the University of Colorado, Colorado School of Mines, and the University of Houston. His university responsibilities included lecturing at the graduate level and advising graduate students on their theses. Dr. Yarus’ university connections include University of Texas, Bureau of Economic Geology, Texas A&M, Stanford University, University of Utah, Tulsa University, University of Alberta, Norwegian Computing Center, University of Neuchâtel, Imperial College, and the Paris School of Mines (Mines ParisTech). He retired from Halliburton at the end of 2018 and is currently a full research professor at Case Western Reserve University engaged in research and teaching geospatio-temporal modeling of near surface, surface and subsurface phenomena, data science, and informatics systems.
Affiliations and Accreditation
PhD University of South Carolina
N058: Reservoir Characterization and Geostatistical Modeling in Field Development
N345: Geomodeling for Unconventional Reservoirs
N557: Geostatistics and Data Science; a Practical Introduction to Quantitative Spatial Modeling