Seismic attributes as the framework for data integration throughout the oilfield life cycle

書誌事項

Seismic attributes as the framework for data integration throughout the oilfield life cycle

presented by Kurt J. Marfurt

(Distinguished instructor series, no. 21 . 2018 distinguished instructor short course)

Society of Exploration Geophysicists, c2018

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注記

"sponsored by the Society of Exploration Geophysicists"

Includes bibliographical references and index

内容説明・目次

内容説明

Useful attributes capture and quantify key components of the seismic amplitude and texture for subsequent integration with well log, microseismic, and production data through either interactive visualization or machine learning. Although both approaches can accelerate and facilitate the interpretation process, they can by no means replace the interpreter. Interpreter "grayware" includes the incorporation and validation of depositional, diagenetic, and tectonic deformation models, the integration of rock physics systematics, and the recognition of unanticipated opportunities and hazards. This book is written to accompany and complement the 2018 SEG Distinguished Instructor Short Course that provides a rapid overview of how 3D seismic attributes provide a framework for data integration over the life of the oil and gas field. Key concepts are illustrated by example, showing modern workflows based on interactive interpretation and display as well as those aided by machine learning.

目次

Preface Acknowledgments Chapter 1 Introduction Chapter 2 Seismic Attributes and What They Measure Chapter 3 Postmigration Data Conditioning and Image Enhancement Chapter 4 The Exploration Stage of the Oilfield Life Cycle Chapter 5 The Development Stage of the Oilfield Life Cycle Chapter 6 The Mature Stage of the Oilfield Life Cycle Chapter 7 Data Integration During the Rebirth Stage of the Oilfield Life Cycle: Resource Plays Chapter 8 Data Integration and a Profile of the Future Interpreter Appendix A Concepts of Linear Algebra - Correlation, Linear Regression, Covariance Matrices, Eigenvectors, and Principal Components Appendix B Multiattribute Display References Index

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