Geology - Structural Geology

New Tools and Approaches in Reservoir Quality Prediction

 

Instructor

  Dr Dave L. Cantrell (Cantrell GeoLogic and Stanford University, USA)

Duration

  1 or 2 days

Disciplines

  Geology – Geological Modeling

Level

  Foundation

Language

  English

EurGeol

  5 or 10 CPD points

Keywords

 
 BASIN ANALYSIS   CASE STUDY   DEPOSITS   DIAGENESIS   FACIES   INTEGRATION   MAPPING   POROSITY   RESERVOIR CHARACTERIZATION   RESERVOIR MODELING   SEDIMENTS   SEQUENCE STRATIGRAPHY   SPARSE DATA 

 

Next available location:

 

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

 

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.

 

Recommended reading

Cantrell, D. L., Griffiths, C. M. and Hughes, G. W., 2015, New tools and approaches in carbonate reservoir quality prediction: a case history from the Shu’aiba Formation, Saudi Arabia: Geological Society, London, Special Publications, v. 406, p. 401-425.

 

About the instructor

Dave CantrellDave 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.

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