Shared earth modeling : knowledge driven solutions for building and managing subsurface 3D geological models

405 Pages·2013·17.517 MB·English

by  Perrin, Michel, Rainaud, Jean-François

Michel PERRIN
Mines ParisTech
Jean-François RAINAUD
IFP Energies nouvelles
SHARED
EARTH
MODELING
Knowledge driven solutions
for building and managing
subsurface 3D geological models
2013
Editions TECHNIP 25 rue Ginoux, 75015 PARIS, FRANCE
FROM THE SAME PUBLISHER
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Meeting the Energy and Environmental Challenges of the Transportation Sector
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•Heavy Crude Oils
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•Corrosion and Degradation of Metallic Materials
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•A Geoscientist’s Guide to Petrophysics
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•Acido-Basic Catalysis (2vols.)
Application to Refining and Petrochemistry
C. MARCILLY
•Petroleum Microbiology (2 vols.)
Concepts. Environmental Implications. Industrial Applications
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•Physico-Chemical Analysis of Industrial Catalysts
A Practical Guide to Characterisation
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•Petrochemical Processes (2 vols.)
Technical and Economic Characteristics
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•The Technology of Catalytic Oxidations (2vols.)
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•Marine Oil Spills and Soils Contaminated by Hydrocarbons
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Foreword
When Michel and Jean-François asked me to preface this book, we discussed at length what
had prompted them to embark with their co-authors on such a comprehensive study of geo-
modeling, with all its possibilities and challenges. In the course of these fascinating and
heated conversations, they had no difficulty highlighting the central role played in their
work by geology and computer sciences, two subjects to which I have devoted nearly
30 years of my professional life. This, along with their contagious enthusiasm in communi-
cating a vision and conviction, molded by a collective and collaborative effort sustained
throughout their research work, finally convinced me to make this modest contribution to
their adventure.
Twenty-five years ago, Geoscience software packages were in their heyday. At the time,
some managers even launched programs directed at getting geologists and geophysicists
“working better together”. The IT teams collaborating with them followed the lead. The
“Data Management” function emerged but was not fully recognized until several years later.
At the beginning of the 90s, the POSC consortium – which has since become
Energistics – was created and laid the foundational standards that would enable the oil
industry to improve the efficiency of sub-surface studies. It is impossible to mention this
subject without recalling the immense contribution of Philippe Chalon in setting up the
Épicentre model. Despite the natural resistance of the industry to any collaborative initiative
in such a highly competitive world, Energistics took up the torch and has managed in these
last few years to unite oil & gas companies and service providers around common standards
such as WITSML, PRODML or RESQML.
Have these endeavors been followed by widespread application? Are they perfectly inte-
grated in the way we operate today? I wish I could say so, but I know that it has been an
uphill battle for those who have tried to introduce them in our companies. And the idea of
reaching beyond mere exchange formats has run into even greater resistance. “Knowledge
Management” and ontological methods – essential for truly sharing knowledge – are still
perceived as interesting novelties, but their significance and potential are mostly overlooked.
It is true to say that it has always been easier to introduce new tools rather than adapt
existing processes and individual working methods. In my current role promoting digital
innovation, I realize each day how much resistance to change is still one of the major
barriers to the introduction of new methods, new concepts and new ways of using them. But
let’s be clear, it takes constant vigilance – from each one of us – to resist the comfort of the
status quo.
With these different ideas in mind, I started reading the book you now have in your
hands. And I must say from the outset that I really enjoyed it, though some of the many
concepts it develops called for a little extra mental effort.
VI Shared Earth Modeling
Rather than paraphrasing them in a few lines, and failing to do them justice, I would
rather share with you the main thrust of this book as I remember it. I sincerely hope that it
will whet your appetite and encourage you to continue and read the proposal put forward by
the authors in the rest of the book.
Because what is offered throughout these chapters is a truly comprehensive approach.
It starts with a twofold observation with detailed supporting data, in chapters two and
three: the first is that geology is the guiding principle of geomodel construction, and the
second, that at each step, each interpretation made during construction must in all cases be
carefully documented to guarantee the possibility of coming back to the model, either to
introduce new information or revise the analysis. Passionate readers of “One Hundred years
of Solitude” will probably remember Aureliano Buendia (the first) fighting against “the
plague of memory loss” by labeling every object in the village of Macondo. In much the
same way, ontologies will be the labels of our geological models.
Then comes the confirmation, its certainty growing with each chapter, that this strong
will to collaborate, perceived in my first conversations with Michel and Jean-François, is no
lone voice in the wilderness but a vision shared by an entire group. In all the chapters
combined, no less than 30 experts in fields ranging from geology – of course – through geo-
physics and information architecture, to knowledge management, present the state of the art
and a vision of the not-so-distant future.
Together, they are paving the way for progress not only within the community of
geoscience disciplines, but for all teams, all companies, facing the rapid increase in the
complexity of their processes and the volumes of information that come into play in their
operation.
Together, they have learned, as they testify in this book, to make the necessary effort to
share their disciplines. What they are suggesting here is therefore not a mere inventory of
technical solutions, but a lesson in sharing. Organized and structured sharing based on
knowledge management. Mutual sharing, in the sense that each specialty involved in the
overall process retains its own specificity while adding its contribution to construction of the
model. The various examples scattered throughout the book are a clear demonstration of the
impetus that can be given by such a cross-functional approach, if not to lift mountains, at
least to have a better understanding of the subsurface.
The book’s style also reflects this approach. First of all, each chapter is deliberately
written in an accessible way, despite the depth of the topics covered. Indeed, its subject mat-
ter unfolds much like a journey of initiation, and should preferably be read in sequence.
The educational approach the authors have opted for reminded me of a quote from A.
Schopenhauer: “The greatest value of science, of abstract knowledge, is that it can be com-
municated”.
It very soon became plain to me that this book was coming at the right time. In the 60s
and 70s, when computers were introduced, companies underwent a tremendous transforma-
tion. The change mostly involved automating repetitive tasks or streamlining processes.
What has been happening in the last few years is completely different. A whole new digital
world has emerged, separate from the physical world, with prospects that could not even be
imagined five or ten years ago. The incredible progress made in computation, storage and
Foreword VII
transmission capacities is of course at the forefront of this evolution. But one cannot under-
estimate the major impact made by internet, remote tools, social networks and geolocation.
Practically everything that we use today in our personal and professional lives exists in
– and like this book, was even preceded by – a digital version. To remain in the professional
context that is examined here, the objects most emblematic of the oil industry, the produc-
tion platforms, start their lives in computers before becoming monsters of steel.
The authors of this work go on to show that even geological objects, however vastly
removed from the binary world of computer science, have come, through intelligence and
patience, to acquire a digital representation. While the accuracy of these renderings is as yet
incomplete, we learn from these pages the enormous possibilities that they already offer.
With the transition from automated or digitized systems to digital ones, the quantities of
company data available in the digital space have, inevitably, exploded. To handle them, we
must cease managing data and move on to managing knowledge. The problem today is that
the industry’s actors have not yet fully grasped all that these changes entail.
This book demonstrates that with the help of cross-disciplinary exchanges, knowledge
management approaches hold the key to obtaining a digital representation of objects as com-
plex as the geological objects of our oil industry, and therefore to processes that cannot be
attained today. As such, this work is setting an important milestone that will be remembered
in the future timeline of computer sciences! If only as it applies to the oil industry, but
certainly also more widely, because of the potential the suggested methods can bring to any
complex engineering process.
It should be noted in passing that the cross-functional approach so dear to the authors can
only benefit from the dematerialization offered by the digital world, since it is no longer nec-
essary to gather in a single physical place the increasingly international teams collaborating
on such projects.
Finally this collective work, written by 30 researchers, does not necessarily exclusively
target readers who are themselves researchers. On the contrary, these fifteen chapters could
be read on several different levels. At least two are obvious to me.
First, it can be read on a technical or scientific level – to identify the tools that will be
used in the near future by our technical teams, or to refresh our knowledge on the subject.
But it can also be read with a focus on the human experience and on organizational or
management issues.
Indeed the “universe of possibilities” that the authors unveil can only become a reality,
as the book’s conclusion encourages, if the necessary actions are taken. Companies – and
thus management lines – must on the one hand speed up the development of a digital
culture, and on the other, recognize the need, no less urgent, to introduce knowledge
management functions in our organizations. In light of the difficulties we encountered
25 years ago when we created data management functions it is time to set this change in
motion to make sure that it will, hopefully very soon, be well and truly integrated into our
processes.
Before you embark on the fascinating discovery of the following pages, I would like to
leave you with one last thought. The following quote is attributed to Seneca: “It is not
VIII Shared Earth Modeling
because things are difficult that we do not dare: it is because we do not dare that they are
difficult.” These words sprang to mind as I was finishing reading this book, as I was struck
by the fact that this collective work, beyond its specific scientific suggestions, was enjoining
us to take the initiative, to daily put into practice a behavior that our companies still too
seldom encourage: boldness.
Needless to say, you will not find what you are about to discover in a toolkit ready for
use on your desk tomorrow morning. But that should not keep us from being bold enough to
begin updating our processes and our individual and collective work methods right now.
Would we be standing today at the threshold of the digital world mentioned above if, in the
early 90s, at the very time POSC was being launched, Tim Berners-Lee and the community
of nuclear physicists working with him had not dared to assemble the concept of hypertext
and the basic building blocks (TCP and DNS) of communication technologies that led to the
invention and promotion of the world wide web? We now know how successful their initia-
tive was. I can only hope that this team’s fascinating work will have the same impact, and I
invite you to support it by closely reading the following pages.
Dominique Lefebvre
Digital Innovation Director
Total Group
Table of Contents
Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V
Introduction: Goals and organisation of this book. . . . . . . . . . . . . . . . . . . . . . . . . IX
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIII
PART 1
EARTH MODELS
Chapter 1
Earth Models as Subsurface Representations
Michel Perrin, Mara Abel
1.1 Models, Representations, Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.3 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Geological Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.1 Categories of Geological Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2.2 Earth Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2.3 Representation Purpose, Representation Choices. . . . . . . . . . . . . . . . . . . . . 13
1.2.3.1 Purpose of Representation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2.3.2 Choice of the Representation Dimension and Scale. . . . . . . . . . . . 14
1.3 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.1 Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.2 Various Types of Interpretations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.3.2.1 Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.2.2 Association . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.2.3 Data Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3.2.4 Interpretation Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4 Modeling Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.1 Expressivity Versus Operability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.2 Computation Cost. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4.3 User Driven Versus Automated Approach. . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.4.4 Modeling Philosophies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
XVI Shared Earth Modeling
Chapter 2
Earth Models for Underground Resource Exploration and Estimation
Michel Perrin, Jean-François Rainaud, Sandrine Grataloup
2.1 Sedimentary Basins and Geological Reservoirs. . . . . . . . . . . . . . . . . . . . . . . 25
2.1.1 Sedimentary Basins and Sedimentary Rocks. . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.2 Hydrocarbon Reservoirs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.3 Other Subsurface Reservoirs Having an Economic Interest. . . . . . . . . . . . . . . 28
2.2 Earth Models for Oil & Gas Reservoir Studies. . . . . . . . . . . . . . . . . . . . . . . 29
2.3 Available Data for Earth Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.3.1 Seismic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.3.2 Drilling Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3.2.1 Drilling Location. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3.2.2 Information Provided by Drillings. . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.2.3 Use of Drilling Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.3 Regional Geology Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.4 Laboratory Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4 Earth Model Building. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.1 Data Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.2 Representation of Geological Objects and their Relationships. . . . . . . . . . . . . 37
2.4.2.1 Stratigraphic Units and Surfaces. . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.2.2 Faults. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4.2.3 Folds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.3 Representation of Volumes and Properties . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.4.3.1 Lithology and Rock Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.4.3.2 Populating Volumes with Rock Properties. . . . . . . . . . . . . . . . . . . 45
2.4.4 Multiple Interpretations and Versioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5 Earth Models Considered in this Book. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Chapter 3
Earth Models Used in Petroleum Industry: Current Practice and Future Challenges
Jean-François Rainaud, Michel Perrin
3.1 Earth Modeling for Reservoir Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.1.1 Geomodeling Software Available on the Market: a Short Review. . . . . . . . . . 50
3.1.2 Activities and Workflows for Defining the Reservoir Geometry
(Structural Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.1.3 Activities and Workflows for Representing Rock Properties
(Stratigraphic Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.1.4 Activities and Workflows for Building Reservoir Models for Economic Evaluation 59
3.2 The Challenge of Data Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.1 Software Compatibility: Integrating Various Modeling Tools
into a Single Workflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.2 RESCUE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2.3 The RESQML Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Table of Contents XVII
3.3 The Challenge of Knowledge Integration. . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3.1 Current Expectations Concerning Earth Modeling . . . . . . . . . . . . . . . . . . . . 64
3.3.2 From Data Driven to Knowledge Driven Earth Models. . . . . . . . . . . . . . . . . 65
3.3.3 Issues Associated with a Knowledge Driven SEM Approach . . . . . . . . . . . . 66
3.3.3.1 Retrieving Relevant Information . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3.3.2 Representing, Formalizing and Processing Multi-disiplinary Knowledge 67
PART 2
KNOWLEDGE ORIENTED SOLUTIONS
Chapter 4
Knowledge Based Approach of a Data Intensive Problem: Seismic Interpretation
Philippe Verney, Monique Thonnat, Jean-François Rainaud
4.1 Approaches for Seismic Interpretation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.1.1 Seismic Attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.1.1.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.1.1.2 Examples of Attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.1.1.3 Joint Use of Several Seismic Attributes . . . . . . . . . . . . . . . . . . . . 73
4.1.2 Use of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1.2.1 Calibrating Meta-attributes by Using Neural Networks . . . . . . . . . 75
4.1.2.2 “Ant Tracking” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1.3 Other Promising Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.1.3.1 Classification Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.1.3.2 Large Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.1.4 Current Situation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 The Cognitive Vision Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2.1 Interest of a Unified Vision Vocabulary . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2.2 Possible Use of Cognitive Vision Methods for Seismic Interpretation . . . . . . 80
4.2.2.1 Rationale. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.2.2 Overview of a Cognitive Vision Approach for Seismic Interpretation 80
4.2.2.3 Knowledge Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.3 Overview of a Knowledge Based Methodology. . . . . . . . . . . . . . . . . . . . . . 82
4.3 Use Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.1 Geological Horizon Identification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.1.2 Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3.1.3 Visual Characterization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3.1.4 Geological Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.3.2 Fault Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.3.2.1 Fault Identification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.3.2.2 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.3.2.3 Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.3.2.4 Visual Characterization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3.2.5 Geological Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3.3 Exported Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
XVIII Shared Earth Modeling
Chapter 5
Individual Surface Representations and Optimization
Alexandra Bac, Marc Daniel, Tran Nam Van
5.1 Constraints and Requirements for Representing Geological Surfaces. . . . 95
5.1.1 Data Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.1.2 Constraints Associated With the Geological Objects That Will Be Reconstructed 97
5.1.3 Choosing a Representational Model for Geological Horizons. . . . . . . . . . . . . 97
5.1.4 Options for Representing Slowly Inclined Faulted Horizons. . . . . . . . . . . . . . 99
5.2 Horizon Simplification and Resampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.2.1 A Brief State of the Art. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2.2 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2.3 Decimation Process (Vertex Clustering). . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2.4 Iterative Edge Collapse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.3 Hole Filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.3.1 Outline of our Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.3.2 Hole Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.3.3 Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.3.4 Hole Fairing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.3.5 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.4 Detection of Fault Interruptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Chapter 6
Geological Surface Assemblage
Michel Perrin, Mathieu Poudret, Nicolas Guiard, Sébastien Schneider
6.1 Syntactic Rules Attached to Geological Objects . . . . . . . . . . . . . . . . . . . . . . 115
6.1.1 Geological Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.1.2 Geological Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.1.2.1 Surface Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.1.2.2 Surface Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
6.1.2.3 Surface Interruptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.1.3 Geological Units and Geological Blocks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2 Logical Rules for Geological Surface Assemblages. . . . . . . . . . . . . . . . . . . . 121
6.2.1 Geological Evolution Schema (GES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2.2 Geological Consistency of Elementary Surface Assemblages. . . . . . . . . . . . . 122
6.2.3 Stratigraphy Description and Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.2.4 Automated Geological Assembly. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6.2.5 Stratification Positioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.3 Topological Data Structures for Surface Assembly and Volume Description 128
6.3.1 Generalized Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6.3.1.1 Generalized Maps, an Intuitive Definition . . . . . . . . . . . . . . . . . . . 129
6.3.1.2 G-map Consistency Rules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130