Hydrocarbon exploration and production incorporate great technology challenges for the oil and gas industry. In order to meet the world's future demand for oil and gas, further technological advance is needed, which in turn requires research across multiple disciplines, including mathematics, geophysics, geology, petroleum engineering, signal processing, and computer science. This book addresses important aspects and fundamental concepts in hydrocarbon exploration and production. Moreover, new developments and recent advances in the relevant research areas are discussed, whereby special emphasis is placed on mathematical methods and modelling. The book reflects the multi-disciplinary character of the hydrocarbon production workflow, ranging from seismic data imaging, seismic analysis and interpretation and geological model building, to numerical reservoir simulation. Various challenges concerning the production workflow are discussed in detail. The thirteen chapters of this joint work, authored by international experts from academic and industrial institutions, include survey papers of expository character as well as original research articles. Large parts of the material presented in this book were developed between November 2000 and April 2004 through the European research and training network NetAGES, "Network for Automated Geometry Extraction from Seismic". The new methods described here are currently being implemented as software tools at Schlumberger Stavanger Research, one of the world's largest service providers to the oil industry.

This book is a compendium of fundamental mathematical concepts, methods, models, and their wide range of applications in diverse fields of engineering. It comprises essentially a comprehensive and contemporary coverage of those areas of mathematics which provide foundation to electronic, electrical, communication, petroleum, chemical, civil, mechanical, biomedical, software, and financial engineering. It gives a fairly extensive treatment of some of the recent developments in mathematics which have found very significant applications to engineering problems.

This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications. The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.

Advances in Geophysics is a highly-respected publication in the field of geophysics. Since 1952, each volume has been eagerly awaited, frequently consulted, and praised by researchers and reviewers alike. Now with over 50 volumes, the series contains much material still relevant today – truly an essential publication for researchers in all fields of geophysics. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.

Approximation methods are vital in many challenging applications of computational science and engineering. This is a collection of papers from world experts in a broad variety of relevant applications, including pattern recognition, machine learning, multiscale modelling of fluid flow, metrology, geometric modelling, tomography, signal and image processing. It documents recent theoretical developments which have lead to new trends in approximation, it gives important computational aspects and multidisciplinary applications, thus making it a perfect fit for graduate students and researchers in science and engineering who wish to understand and develop numerical algorithms for the solution of their specific problems. An important feature of the book is that it brings together modern methods from statistics, mathematical modelling and numerical simulation for the solution of relevant problems, with a wide range of inherent scales. Contributions of industrial mathematicians, including representatives from Microsoft and Schlumberger, foster the transfer of the latest approximation methods to real-world applications.

Over the last two decades, earth modeling has become a major investigative tool for evaluating the potential of hydrocarbon reservoirs. Earth modelling must now face new challenges since petroleum exploration no longer consists in only investigating newly identified resources, but also in re-evaluating the potential of previously investigated reservoirs in the light of new prospecting data and of revised interpretations. Earth models incorporate a variety of different interpretations made on various types of data at successive steps of the modeling process. However, current modeling procedures provide no way to link a range of data and interpretations with a final earth model. For this reason, sharing and exchanging information about the model building process is at present a major difficulty. Recently, the term “Shared Earth Modeling” has been used for expressing the idea that earth models should be built in such a way that experts and end users can have access, at any time, to all the information incorporated into the model. This information does not only concern the data, but also the knowledge that geoscientists produce by interpreting these data. Accordingly, practical solutions must be studied for operating a knowledge-driven approach of Shared Earth Modeling. This is the goal of this book. This study of earth subsurface modeling is intended for several categories of readers. It concerns in the first place geologists, engineers and managers involved in the study and evaluation of subsurface reservoirs and hydrocarbon exploration. Relying on recent progress in various fields of computer sciences, the authors present innovative solutions for solving the critical issue of knowledge exchange at key steps of the modeling process. This book will also be of interest to researchers in computer science and, more generally, to engineers, researchers and students who wish to apply advanced knowledge-based techniques to complex engineering problems. Contents : Part I. Earth Models. 1. Earth models as subsurface representations. 2. Earth models for underground resource exploration and estimation. 3. Earth models used in petroleum industry: current practice and future challenges. Part II. Knowledge oriented solutions. 4. Knowledge based approach of a data intensive problem: seismic interpretation. 5. Individual surface representations and optimization. 6. Geological surface assemblage. 7. 3D Meshes for structural, stratigraphy and reservoir frameworks. 8. The data extension issue: geological constraints applied in geostatistical processes. Part III. Knowledge formalization. 9. Ontologies and their use for geological knowledge formalization. 10. Ontologies for Interpreting geochronological relationships. 11. Building ontologies for analyzing data expressed in natural language. 12. Ontology-based rock description and interpretation. Part IV. Knowledge management & applications. 13. Ontology integration and management within data intensive engineering systems. 14. Earth modeling using web services. 15. Full scale example of a knowledge-based method for building and managing an earth model. Part V. Conclusion. Appendix. Glossary.