Oil and Gas
Oil and Gas | Unconventional Resources
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 analysis 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.
Duration and Training Method
This is a classroom or virtual classroom course comprising a mixture of lectures, discussion, case studies, and practical exercises.
Participants will learn to:
- How to apply data analytics and machine learning for data QC.
- Assess critical relationships between petrophysical, geochemical, geothermal, and mechanical data using multivariate analysis
- Evaluate workflows for geocellular modeling of unconventional Reservoirs.
- Tell the difference between interpolation, simulation, imputation, and prediction.
- How to properly integrate data science and geostatistical static modeling.
- Develop spatial models using quality indices from integrated geomechanical, geochemical, geothermal, and petrophysical properties.
- Assess the uncertainty of geomodels of unconventional reservoirs.
- Post process static geomodels al reservoirs for dynamic flow simulation.
- Provide valuable model information to well completion and stimulation engineers.
- Understand the value of C.I.P. languages (e.g. R and Python) in geomodeling.
This course will address questions about using geocellular models for unconventional reservoirs such as:
- The difference between Conventional and Unconventional Reservoirs
- Are common modeling tools such as kriging and conditional simulation appropriate?
- Can Machine Learning be used to create geomodels?
- I’m not a developer, how can I use R and Python to help me build models?
- Is it necessary to model “shale facies” or “geothermal facies” and if so, how do we define them?
- What if my data are sparse and there is no seismic?
- How do I deal with missing values when I am building a geomodel?
- Geocellular grids or meshes; what dimensions, layering types, and granularity are required?
- How to integrate natural fractures - key considerations
- What is the role of micro-seismic data and how can it be used in a geocellular model?
- How can geocellular models be used to improve geosteering and real-time drilling?
- What are the limitations to geocellular grids and stratigraphic layering styles?
- How do geocellular grids of unconventional reservoirs respond in dynamic simulators?
- How can Completion Engineers benefit from geomodels?
The specific topics to be addressed are:
- Role of 3D geocellular models for unconventional resources
- Considerations for constructing geocellular grids or meshes for unconventional resources
- Exploratory data analytics for unconventional resources
- Types of variables
- Univariate and bivariate statistics for data quality assessment
- Review of principles of spatial modeling
- Review of Kriging and Conditional Simulation
- Review of geomodeling steps and the importance of frameworks and stratigraphy
- Workflows for shale plays and how they differ from conventional plays
- Understanding multivariate analysis techniques and machine learning
- Applications in R and Python
- Post processing - Building probability maps to identify “Sweet Spots” and improve well placement
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:
- A brief introduction to R and R Studio
- Exploratory Data Analytics
- Spatial Modeling, Kriging and Conditional Simulation
- Analytics for data reduction and data redundancy
- Principal Component Analysis and Factor Analysis
- Classification Systems and Regression Systems
- Discriminant Analysis
The actual number of exercises and methods will vary depending on available time.
Who Should Attend and Prerequisites
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 in 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.
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
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