Oil & Gas Aplicações de Engenharia Registro Físico

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PREFACE 
Integration, handling data involving uncertainty and risk management are among 
key issues in geoscience and oil industry applications. In recent years there has been 
tremendous efforts to find new methods to address theses issues. As we approach 
the dawn of the next millennium, and as our problems become too complex to 
rely only on one discipline to solve them more effectively, and the cost associated 
with poor predictions (such as dry holes) increases, the need for proper integration 
of disciplines, data fusion, risk reduction and uncertainty management, and multi- 
disciplinary approaches in the petroleum industry become more important and of 
a necessity than professional curiosity. We will be forced to bring down the walls 
we have built around classical disciplines such as petroleum engineering, geology, 
geophysics and geochemistry, or at the very least make them more permeable. Our 
data, methodologies and approaches to tackle problems will have to cut across various 
disciplines. As a result, today's "integration" which is based on integration of results 
will have to give way to a new form of integration, that is, integration of disciplines. 
In addition, to solve our complex problem one needs to go beyond standard techniques 
and silicon hardware. The model needs to use several emerging methodologies and 
soft computing techniques. Soft Computing is consortium of computing methodologies 
(Fuzzy Logic (FL), Neuro-Computing (NC), Genetic Computing (GC), and Probabilistic 
Reasoning (PR) including; Genetic Algorithms (GA), Chaotic Systems (CS), Belief 
Networks (BN), Learning Theory (LT)) which collectively provide a foundation for 
the Conception, Design and Deployment of Intelligent Systems. The role model for 
Soft Computing is the Human Mind. Soft computing differs from conventional (hard) 
computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, 
and partial truth. Soft Computing is also tractable, robust, efficient and inexpensive. In 
this volume, we reveal (explore) the role of Soft Computing techniques for intelligent 
reservoir characterization and exploration. The major constituent of soft computing 
is fuzzy logic, which was first introduced by Prof. Lotfi Zadeh back in 1965. In 
1991, Prof. Zadeh introduced the Berkeley Initiative in Soft Computing (BISC) at 
the University of California, Berkeley. In 1994, a new BISC special interest group in 
Earth Sciences was formed. Broadly, Earth Sciences subsumes but is not limited to 
Geophysics (seismology, gravity, and electromagnetic), Geology, Hydrology, Borehole 
wireline log evaluation, Geochemistry, Geostatistics, Reservoir Engineering, Mineral 
Prospecting, Environmental Risk Assessment (nuclear waste, geohazard, hydrocarbon 
seepage/spill) and Earthquake Seismology. 
Soft Computing methods such as neural networks, fuzzy logic, perception-based 
logic, genetic algorithms and other evolutionary computing approaches offer an excel- 
lent opportunity to address different challenging practical problems. Those to focus on 
in this volume are the following issues: 
X PREFACE 
�9 Integrating information from various sources with varying degrees of uncertainty; 
�9 Establishing relationships between measurements and reservoir properties; and 
�9 Assigning risk factors or error bars to predictions. 
Deterministic model building and interpretation are increasingly replaced by stochas- 
tic and soft computing-based methods. The diversity of soft computing applications in 
oil field problems and prevalence of their acceptance are manifested by the overwhelm- 
ing increasing interest among the earth scientist and engineers. 
The present volume starts with an introductory article written by the editors ex- 
plaining the basic concepts of soft computing and the past/present/future trends of 
soft computing applications in reservoir characterization and modelling. It provides a 
collection of thirty (30) articles containing: (1) Introduction to Soft Computing and 
Geostatistics (6 articles in Part 1), (2) Seismic Interpretation (4 articles in Part 2), (3) 
Geology (6 articles in Part 3), (4) Reservoir and Production Engineering (5 articles in 
Part 4), (5) Integrated and Field Studies (5 articles in Part 5), and (6) General Appli- 
cations (4 articles in Part 6). Excellent contributions on applications of neural network 
fuzzy logic, evolutionary techniques, and development of hybrid models are included in 
this book. 
We would like to take this opportunity to thank all the contributors and reviewers of 
the articles. We also wish to acknowledge our colleagues who have contributed to the 
areas directly or indirectly related to the contents of this book. 
Masoud Nikravesh 
Fred Aminzadeh 
Lotfi A. Zadeh 
Berkeley