D345 Next Generation Earth Modeling; Integrating Geostatistics, Geoscience, Engineering, and Data Science (Distance Learning)
D345 Next Generation Earth Modeling; Integrating Geostatistics, Geoscience, Engineering, and Data Science (Distance Learning)
Business Impact: In order to increase production and drive down the cost per barrel of oil equivalent (BOE), this course will enable participants to build reliable earth models of unconventional reservoirs using analytics for data insight and geostatistics for assessing uncertainty.
This class addresses the application and integration of data analytics to subsurface geomodeling for unconventional resources, including oil, gas, and geothermal. Deterministic and stochastic methods used to create static models and uncertainty assessment will be reviewed to establish a common knowledge baseline. This is followed by skill development in data analytical methods such as multivariate statistics and machine learning. Topics include kriging, conditional simulation, principal components, cluster analysis, regression, recursive portioning, neural networks, and other practical methods. The class focus is to go beyond traditional static modeling through the integration of geostatistics and data science to produce reliable models for reservoir and completion engineers.
Participants will learn how to create mappable quality indices to optimize successful well placement and formation stimulation strategies. This course pulls together geoscience, engineering, and data science to build synergistic teams, optimize successful drilling programs, reduce uncertainty, and drive down cost per barrel of oil equivalent.
A virtual classroom course divided into 8 webinar sessions, comprising lectures, discussion, case studies, and practical exercises to be completed by participants during and between sessions.
Participants will learn to:
This course will address questions about using geocellular models for unconventional reservoirs such as:
The specific topics to be addressed are:
Topic 1
Topic 2
Topic 3 - Modeling Workshop
This workshop uses simple exercises on a variety of multivariate techniques in order for participants to better understand the underlying principles. Some of the examples are from unconventional shale reservoirs and some are not. Those that are not, are classic data sets used in texts, classrooms, seminars, and online short-courses and are not necessarily geologic in nature, but clearly illustrate the methods.
Practical computer-based exercises demonstrating Multivariate Data Analytics that form the basis of the workshop include the following:
The actual number of exercises and methods will vary depending on available time.
This course has been designed for mid to senior level geoscientists (specifically geomodelers), and data scientists who are working with geomodelers. Familiarity with geostatistics and practical geocellular modeling is assumed. Managers and others who have previous experience of building geomodels and wish to develop a better understanding of how to apply geomodeling and data science techniques to unconventional reservoirs could also benefit from this course.
Skilled Application level course N058 (Reservoir Characterization and Geostatistical Modeling in Field Development) is a prerequisite for this course. Participants familiar with geostatistical principles and practical geocellular modeling, who have equivalent work experience to N058, could also attend this course. Data scientists who wish to learn more about geomodeling with geostatistics could also attend this course.
Additionally, participants are expected to have a basic awareness of unconventional reservoirs, as presented in Basic Application level course N313 (Evaluating Resource Plays: The Geology and Engineering of Low Permeability Oil and Gas Reservoirs).
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Background
Dr. Yarus obtained his Ph.D. from the University of South Carolina in 1977 before joining Amoco Production Company where he worked as an exploration geologist in the Gulf of Mexico. From 1981 until 1988, he worked in exploration and production as an independent in a variety of basins throughout the Rocky Mountain States. In 1988, Jeffrey joined Marathon Oil Company’s Petroleum Technology Center in Littleton, Colorado where he introduced the company to geostatistical reservoir characterization.
Since moving to Houston in 1996, he worked as a technical manager and executive for GeoMath, a subsidiary of Beicip-Franlab, Smedvig Technologies (Roxar), and Knowledge Reservoir, Inc. In August of 2001, Jeffrey along with Dr. Richard L. Chambers, started Quantitative Geosciences, LLP, a consulting firm specializing in reservoir characterization and geostatistics. In 2006, Jeffrey, along with the QGSI staff, moved to Landmark Graphics Corporation, a division of Halliburton, where he is now the Senior Product Manager for Earth Modeling. Jeffrey is well known throughout the industry for his seminars and lectures which he has given world-wide.
Jeffrey has served as AAPG’s Computer Applications, Publications, and Reservoir Development Chairman, and has authored many papers and abstracts on geostatistics. Along with his partner Richard, he co-edited the 1995 and 2006 AAPG volumes on Stochastic Modeling and Geostatistics, and SPE’s 2007 chapter on Geologically-Based, Geostatistical Reservoir Modeling in their Petroleum Engineering Handbook.
Affiliations and Accreditation
PhD University of South Carolina
Courses Taught
N058: Reservoir Characterization and Geostatistical Modeling in Field Development
N345: Geomodeling for Unconventional Reservoirs
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