Oil and Gas
Oil and Gas | Reservoir Development
Reservoir Characterization and Geostatistical Modeling; A Data Analytical Approach to Practical Geospatial Reservoir Mapping
Business impact: The appropriate use of geostatistical methods can not only create a better understanding of the subsurface but also improve production and drive down the cost per barrel of oil equivalent.
This course delivers expertise in the applied geostatistical methods that are essential for effective modern reservoir characterization and modeling. Variograms, kriging, and stochastic simulation are thoroughly explained from their basics upwards and illustrated in their application to modeling problems.
Duration and Training Method
This is a classroom or virtual classroom course comprising a mixture of lectures, discussion, case studies, and computer exercises.
Participants will learn to:
- Assess modern quantitative reservoir characterization and modeling techniques.
- Design data quality control and analysis strategies for classical univariate and bivariate statistical methods.
- Appraise the similarities and differences between traditional statistical methods and geostatistical methods.
- Construct variograms and formulate their use in spatial analysis and “mapping” of geosciences data.
- Formulate strategies to provide more spatially rigorous reservoir models, using geostatistical interpolation and stochastic simulation methods.
- Illustrate the use of kriging and collocated co-kriging to provide the most-likely reservoir model.
- Characterize commonly used conditional simulation methods, illustrate their use, highlight their pros and cons for capturing heterogeneity, and assessing uncertainty through the construction of multiple realizations.
- Assemble the essential elements of a static 3D reservoir model from structural framework through to petrophysical integration, ready for use in dynamic simulation and other downstream operations like drilling and completion.
- Discuss the differences between uncertainty assessment, sensitivity analysis, and risk analysis in reservoir characterization.
- Evaluate reservoir heterogeneity and discuss upscaling criteria to capture the scale of critical resolution that addresses stated objectives.
- Formulate reservoir model ranking criteria and evaluate their importance within an overall development plan.
Reservoir characterization technology has changed dramatically over the last two decades. Reservoir modeling software now has a wide range of powerful statistical and geostatistical functionality and has spread rapidly through the industry as PCs have become faster and user interfaces have simplified the application of complex methods. Further, data analytics and geostatistics is becoming increasing available through popular public computing platforms such as R and Python. However, the understanding required to make optimum use of this functionality has not kept pace and users continue to struggle with understanding many fundamental principles. Hence, a number of misunderstandings and poor workflows have become commonplace. This course addresses these misunderstandings and poor workflows in order to improve your effectiveness in reservoir modeling. Classroom exercises utilize R and R Studio, but the learnings are also applicable to all other software platforms both public and commercial.
- What is reservoir characterization?
- What is geostatistics?
- Reservoir characterization today
- The basic workflow
- Overview of Classic Statistical Principles
- Exploratory data analytics and project design
- Statistical measures
- Spatial Analysis and Modeling
- Variography, variograms and modeling
- R: Workshop 1: Data Analytics and Spatial Modeling
- Geostatistical Estimation
- Principles of Kriging and Cokriging
- Kriging workflows
- Principles of Conditional Simulation and Cosimulation
- Estimation versus simulation
- Stochastic simulation
- Pixel versus object methods
- Common facies simulation algorithms
- Common continuous property algorithms
- Multivariate conditional simulation
- R Workshop 2: Kriging and Simulation
- Uncertainty Analysis and Risk
- The space of uncertainty
- Orders of uncertainty
- Visualizing uncertainty
- Using uncertainty assessment to build business cases
- Increasing production
- Decreasing the price per BOE
- Pulling it together: Building the 3D Model
- General workflow
- Size and resolution of the model
- Conceptual geological modeling: structural and stratigraphic framework
- Demonstration: Exploring the Earth Model and Post Processing
- Overview of Ranking and Upscaling
- Cells; regular, irregular, unstructured
- Common upscaling methods
- Common upscaling problems
- Excel: Workshop 3: Upscaling Exercise
Who Should Attend and Prerequisites
The course is primarily aimed at geologists and geophysicists working in field development. It is also applicable to reservoir engineers, computational scientists and data scientists interested in knowing more about petroleum reservoir modeling in order to improve data analytical and machine learning methods.
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