Challenges and Solutions in Stochastic Reservoir Modelling (ebook)

64.00

This book aims to bridge across different fields — geostatistics, machine learning, and Bayesian statistics — to demonstrate the common grounds in solving challenging problems of uncertainty quantification, geological realism, and data integration in reservoir prediction.

Publisher EAGE
Language ENGLISH
Publish year 2018
Pages 264 pages
ISBN 9789462822399

Description

Many advances in stochastic reservoir modelling have been introduced in the past decade. Novel method of data integration and more accurate representation of geology have been developed with the advances in spatial statistics. However, integrated approach for predictive reservoir modelling still attracts continuous effort to manage reservoir decisions under uncertainty and make better use of the increasing amounts of data and domain knowledge accumulated in the field.

Many solutions to these challenges lie in the cross-disciplinary vision, where modern rigour of computer science and statistics brought together with core geological and engineering domain expertise and basic physical conceptual thinking.

This book aims to bridge across different fields — geostatistics, machine learning, and Bayesian statistics — to demonstrate the common grounds in solving challenging problems of uncertainty quantification, geological realism, and data integration in reservoir prediction. It presents an overview of key concepts and some of the basic and more advanced algorithms for reservoir modelling and uncertainty quantification. This book includes several practical examples to reinforce the learning outcomes. A tutorial on decision making under uncertainty provides a practical way to apply integrated thinking to a real field dataset.


Vasily Demyanov has 25 years of experience in geostatistics and machine learning applications in geosciences. He received his first degree in physics from Moscow State University, Moscow, Russia, followed by the PhD degree in physics and math (1998). In 2003, Vasily joined Heriot-Watt University, Edinburgh, UK, where he is currently an Associate Professor. His research interest broadly covers the use of spatial statistics and artificial intelligence for reservoir modelling, inverse problems, and uncertainty quantification for Earth systems’ predictions.


Dan Arnold has 12 years of experience in reservoir modelling and uncertainty quantification. Dan started in mining with a degree at Camborne School of Mines, Penryn, UK, followed by a stint with Schlumberger. He then completed the PhD degree in petroleum engineering at Heriot-Watt University, Edinburgh, UK. He has continued his work there, currently serving as an Assistant Professor in reservoir modelling. His area of research interests covers uncertainty quantification, geological modelling, simulation, and optimisation of reservoirs.

Additional information

Author

Publisher

Book type

Ebook

Co author

Language

ENGLISH

Pages

264

Year of publication

2018

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