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Library of Congress Cataloging-in-Publication Data

ISBN 9781119556213

Cover image: Geo/Rock Wall, 31647625 © Pzaxe | Dreamstime.com Cover design by Kris Hackerott

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

What is reservoir characterization? As you will see from this book, this is a very advanced topic so let’s break it down a bit and start form the basics. What is a reservoir? This is ‘a place where something is kept in store’. And what is characterization? That is ‘to describe the character or quality’ all according to the Webster dictionary. So, we are arrived at: ‘describe the character of something that’s kept in store’. It seems relatively benign and easy but ‘the devil is in the details’ is perhaps the best way to get the readers intrigued and immersed in this topic. So, we are left wondering what are these details where the devil resides? And here starts the story…..

In fact, a better wording would be ‘Subsurface Reservoir Characterization’ or SRC. There have been on the order of thousands of studies in reservoir characterization over the life time of this field. As such, this topic has evolved and matured with many learnings. As illustrated in this book, there are now well established and tested workflows SRC and I‘d like to go over some aspects of these understandings and workflows.

First, it is key to understand that SRC is a continuously changing, multi-discipline and multi-scale topic. For continuously changing a good example would be the recent impact of say machine learning methods. I have learned that if our data quality is good enough and there are physical relationships between reservoir data and properties, machine learning can be an excellent way to quickly uncover relationships in a multi-variable universe. However, once again, even here, the devil is in the details…. Multi-discipline is a word we easily use but have difficulty implementing. In many projects the geologist is tasked with building a static reservoir model and then passing it on to the reservoir engineer to build a dynamic model and history match production. However, it has been challenging to form a loop versus a linear workflow or for the dynamic model to be updated with new static information or cover a range of possible models that fit the data…… As for multi-scale, the discipline involves integration of data from a wide range of data, say, nanometer (electron microscope), to centimeter (cutting and core samples), to decimeter (well log), to meter (seismic) scale. Spatially most of these data are acquired within a small portion of one or several wells and geophysical data gives the capability to extrapolate away from the wells with lower resolution. Due to uncertainties in the data, rapid variations in the subsurface, and sparse sampling multi-scale integration can be a challenging task. There is a good discussion of “SURE Challenge” in the book where the author addresses the above mentioned challenges of integration incolving multitude of data set with different Scale, Unvertainty, Resoultion and Environemnet. It is suggested that different AI and Data Analytics techniques may be best equipped to handle the SURE Challenge.