Oil and Gas

Oil and Gas | Reservoir Development

Reservoir Characterization and Geostatistical Modeling in Field Development

Course Code: N058
Instructors:  Jeffrey Yarus
Course Outline:  Download
Format and Duration
5 days

Summary

This course delivers expertise in the applied geostatistical methods that are an essential underpinning of 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.

The appropriate use of these 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. 

Feedback

The course is very comprehensive and provides a good balance between theory, examples and exercises.

Duration and Training Method

This is a classroom course. Computer exercises constitute about 25% of the class time.

Course Overview

Participants will learn to:

  1. Assess modern quantitative reservoir characterization and modeling techniques.
  2. Design data quality control and analysis strategies for classical univariate and bivariate statistical methods.
  3. Appraise the similarities and differences between traditional statistical methods and geostatistical methods.
  4. Construct variograms and formulate their use in spatial analysis and “mapping” of geosciences data.
  5. Formulate strategies to provide more spatially rigorous reservoir models, using geostatistical interpolation and stochastic simulation methods.
  6. Illustrate the use of kriging and collocated co-kriging to provide the most-likely reservoir model.
  7. 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.
  8. 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.
  9. Discuss the differences between uncertainty assessment, sensitivity analysis, and risk analysis in reservoir characterization.
  10. Evaluate reservoir heterogeneity and discuss upscaling criteria to capture the scale of critical resolution that addresses stated objectives.
  11. 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.

          • Introduction
            • 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

          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.

          Jeffrey Yarus

          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
          N557: Geostatistics and Data Science; a Practical Introduction to Quantitative Spatial Modeling

          CEU: 4 Continuing Education Units
          PDH: 40 Professional Development Hours
          Certificate: Certificate Issued Upon Completion
          RPS is accredited by the International Association for Continuing Education and Training (IACET) and is authorized to issue the IACET CEU. We comply with the ANSI/IACET Standard, which is recognised internationally as a standard of excellence in instructional practices.
          We issue a Certificate of Attendance which verifies the number of training hours attended. Our courses are generally accepted by most professional licensing boards/associations towards continuing education credits. Please check with your licensing board to determine if the courses and certificate of attendance meet their specific criteria.