New Tools and Approaches in Reservoir Quality Prediction
By: Dave L. Cantrell
Instructor
Dr Dave L. Cantrell
(Cantrell GeoLogic and Stanford University, USA)
Duration
16–17 September 2021:
4:00PM-8:00PM CEST
4 hours/day
Disciplines
Geology - Geological Modelling
Course Format
The EAGE Interactive Online Short Courses bring carefully selected courses of experienced instructors from industry and academia online to give participants the possibility to follow the latest education in geoscience and engineering remotely. The courses are designed to be easily digested over the course of two or three days. Participants will have the possibility to interact live with the instructor and ask questions.
Education Packages
To help you save on registration fees and better organize your learning path, we are offering Education Packages for all EAGE online courses. The packages are valid for a period of 12 months and give you credits (3, 5 or 10) to spend on courses of your choice.
Course Description
Reservoir quality prediction has historically been the “holy grail”
of reservoir geologists, yet few have been completely successful at
achieving this in a truly quantitative fashion. Most oil companies
have traditionally based their reservoir quality prediction efforts on
geostatistical models that are primarily driven by well and seismic
data, usually with some input from qualitative studies of outcrop and
observations of modern sedimentary processes. Prediction results
from such studies are often less than optimal, especially in areas
where data quality is poor and/or data coverage is sparse.
The sheer complexity of factors controlling reservoir quality in the
subsurface makes prediction challenging, especially in carbonates.
These factors include primary depositional texture and composition,
as well as a wide variety of post-depositional modifications that occur to the sediment during and after burial. Developing quantitative
tools that allow the prediction of reservoir quality ahead of the bit,
and ideally pre-drill, can provide enormous benefits for both exploration and development drilling by reducing the risk associated with
exploitation of heterogeneous intervals.
Reservoir quality prediction means different things to different people; this workshop outlines an approach that’s based on an understanding of the geological processes that control reservoir quality, and
which allows the quantitative prediction of reservoir quality (porosity
and permeability) ahead of the bit. To accomplish this, this workshop
first provides an overview of the main controls on reservoir quality in
both clastic and carbonate rocks, and then presents a new approach
to pre-drill reservoir quality prediction that involves the integration of
a variety of modelling techniques to understand, quantify and predict
the geological processes that control reservoir quality. Since the initial
reservoir quality framework is established at the time of deposition
by a variety of depositional controls, this workflow uses numerical
process models to predict initial reservoir quality; results from these
models are then modified via a series of other modeling technologies
(compaction models, kinetic cementation models, reaction transport
models, etc.) to quantify and predict various diagenetic modifications that have significantly affected reservoir quality in the interval
of interest. This approach successfully integrates these two different
technologies into one workflow that holistically predicts reservoir
quality. Several case histories will be shown in which this approach
has been successfully applied.
Course Objectives
Upon completion of the course, participants will be able to understand:
• The main controls on reservoir quality, for both clastics and carbonates
• The main principals behind a geologically process-based approach
to reservoir quality prediction
• The quality and power of geologically based predictions, as well as
some of the inherent limitations
• How geological process models can be used to assess uncertainty
in prediction results
Course Outline
Introduction to reservoir quality
• Controls on reservoir quality in clastic and in carbonate rocks
Introduction to geological process based modeling
• What is process modeling and how does it work?
• How process based modeling fits into an overall reservoir quality
prediction framework
• What differentiates process modeling from other types of geological modeling
• Key input parameters in process modeling
Overview of process modeling in siliciclastics
Case History #1: Modeling a Paleozoic sandstone reservoirs in the Middle East
Overview of process modeling in carbonates
• Distinctive aspects of carbonates
Case History #2 : Modeling a carbonate reservoir in the Middle East
Conclusions and Adjourn
Participants' Profile
The course is designed for geologists, reservoir engineers and technical managers - and for all others looking to enhance their understanding and ability to predict reservoir quality.
Prerequisites
Some knowledge of geology, geological processes, and the main challenges of reservoir quality prediction would be helpful.
About the Instructor
Dave L. Cantrell
Dave L. Cantrell has over 35 years of worldwide geologic industrial and academic experience. He graduated from the University of Tennessee with an MSc in Geology in 1982, and from the University of Manchester with a PhD in Geology in 2004. Dave began his industry career in 1982 with Exxon where he conducted numerous reservoir characterization and geological modeling studies on reservoirs in the Middle East; the Permian, Powder River, Williston, and Gulf of Mexico Basins of the USA; and the Maracaibo and Barinas Basins of Venezuela; among others. After moving to Saudi Arabia in 1997, he conducted studies on several large carbonate fields there, and lead geologic R&D for Saudi Aramco from 2000-2008; he also served as a professor and Associate Director for the College of Petroleum Engineering & Geosciences at King Fahd Petroleum & Minerals (KFUPM) from 2015-2017. He is an AAPG Certified Petroleum Geologist, a Fellow of the Geological Society of London, and an adjunct professor at Stanford University; he has published over 40 articles in peer-reviewed journals, and holds one patent.