Image structure analysis for seismic interpretation structure analysis for seismic interpretation...

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Transcript of Image structure analysis for seismic interpretation structure analysis for seismic interpretation...

  • Image structure analysis for seismic interpretation

    Proefschrift

    ter verkrijging van de graad van doctoraan de Technische Universiteit Delft,

    op gezag van de Rector Magnificus prof. dr. ir. J.T. Fokkema,voorzitter van het College voor Promoties,

    in het openbaar te verdedigen op dinsdag 4 juni 2002 om 13.30 uurdoor

    Peter BAKKER

    doctorandus in de natuurkundegeboren te Linz Oostenrijk

  • Dit proefschrift is goedgekeurd door de promotor:Prof. dr. ir. L.J. van Vliet

    Samenstelling promotiecommissie:

    Rector Magnificus, voorzitterprof.dr.ir. L.J. van Vliet, Technische Universiteit Delft, promotordr. P.W. Verbeek, Technische Universiteit Delft, toegevoegd promotorprof.dr.ir. A. Gisolf, Technische Universiteit Delftprof.dr.ir. R.L. Lagendijk, Technische Universiteit Delftprof.dr.ir. F.A. Gerritsen, Philips Medical Systemsdr. G.C. Fehmers, Shell International Exploration and Production B.V.dr. W.J. Niessen, University Hospital Utrechtprof.dr. I.T. Young, Technische Universiteit Delft, reserve lid

    This project was financially supported by the Netherlands Ministry of Economic affairs,within the framework of the Innovation Oriented Research Programme (IOP Beeldverw-erking, project number IBV97005).

    Advanced School for Computing and Imaging

    This work was carried out in graduate school ASCI.ASCI dissertation series number 78.

    ISBN: 90-75691-08-4c 2002, Peter Bakker, all rights reserved.

  • Contents

    1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Traditional interpretation of 3-D seismic data . . . . . . . . . . . . . . . . 21.2 Improving the efficiency of the interpretation process . . . . . . . . . . . . 4

    1.2.1 Structure enhancement for horizon tracking . . . . . . . . . . . . . 41.2.2 Seismic attributes for detection . . . . . . . . . . . . . . . . . . . . 5

    1.3 Image processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.1 Adaptive filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    2. Linear structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.1 The representation of linear structures . . . . . . . . . . . . . . . . . . . . 12

    2.1.1 Orientation representation . . . . . . . . . . . . . . . . . . . . . . . 132.1.2 Gradient structure tensor . . . . . . . . . . . . . . . . . . . . . . . 14

    2.2 Orientation adaptive filtering . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 Edge preserving filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    2.3.1 Generalized Kuwahara filtering . . . . . . . . . . . . . . . . . . . . 222.3.2 Improving orientation estimation near borders . . . . . . . . . . . . 262.3.3 Edge preserving orientation adaptive filtering . . . . . . . . . . . . 29

    2.4 Application: Automatic fault detection . . . . . . . . . . . . . . . . . . . . 322.5 Application: Structure enhancement . . . . . . . . . . . . . . . . . . . . . 37

    3. Curvilinear structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.1 The GST for 3D plane-like curvilinear structures . . . . . . . . . . . . . . . 42

    3.1.1 The quadratic surface approximation . . . . . . . . . . . . . . . . . 423.1.2 The quadratic GST for 3D surfaces . . . . . . . . . . . . . . . . . . 433.1.3 Experimental tests and results . . . . . . . . . . . . . . . . . . . . . 45

    3.2 The GST for 2D curvilinear structures . . . . . . . . . . . . . . . . . . . . 513.3 Curvature adaptive filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 523.4 Appendix A: Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.5 Appendix B: Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 56

    4. Line-like curvilinear structures . . . . . . . . . . . . . . . . . . . . . . . . . 614.1 The GST for 3D line-like curvilinear structures . . . . . . . . . . . . . . . . 61

    4.1.1 The quadratic curve approximation . . . . . . . . . . . . . . . . . . 614.1.2 The quadratic GST for space curves . . . . . . . . . . . . . . . . . . 63

  • iv Contents

    4.2 Experimental tests and results . . . . . . . . . . . . . . . . . . . . . . . . . 664.2.1 Circle image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674.2.2 Helix image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2.3 Ellipse image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

    4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.4 Application: Channel detection . . . . . . . . . . . . . . . . . . . . . . . . 744.5 Appendix A: Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774.6 Appendix B: Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

    5. Structural analysis using a non-parametric description . . . . . . . . . . 815.1 The tracking of line-like curvilinear structures . . . . . . . . . . . . . . . . 82

    5.1.1 Application to the tracking of growth rings . . . . . . . . . . . . . . 835.1.2 Application to the tracking of sedimentary structures . . . . . . . . 84

    5.2 Non-parametric adaptive filtering . . . . . . . . . . . . . . . . . . . . . . . 885.3 Non-parametric confidence estimation . . . . . . . . . . . . . . . . . . . . . 92

    5.3.1 Application to channel detection . . . . . . . . . . . . . . . . . . . . 93

    6. Coherency estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 976.1 Coherency based on the eigenstructure of the covariance matrix . . . . . . 986.2 Coherency estimation using the GST . . . . . . . . . . . . . . . . . . . . . 1006.3 The presence of structural dip . . . . . . . . . . . . . . . . . . . . . . . . . 101

    6.3.1 Dip estimation by sampling the dip dependency . . . . . . . . . . . 1036.3.2 Comparison between the dip estimates of the GST and dip search . 104

    6.4 An experimental comparison for fault detection . . . . . . . . . . . . . . . 108

    7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137.1 Image processing approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137.2 Application to seismic interpretation . . . . . . . . . . . . . . . . . . . . . 114

    Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

    Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

    Samenvatting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

    Dankwoord . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

    Curriculum vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

  • 1. Introduction

    The growing global population and living standards cause an increasing demand for energy.Despite the many efforts made to exploit new energy sources such as solar energy andbio-mass, oil and gas continue to be the primary sources of energy. The total amount ofoil and gas produced each year is still increasing, and is very likely to continue to do so forat least 30 years. The oil industry is searching for new reservoirs on land and offshore, inincreasingly difficult environments. Furthermore, the trend in the oil industry has changedfrom producing at any cost in the seventies to a cost efficient and environmentally awareproduction today.

    Since the first seismic surveys, in the 1920s, the seismic reflection method has played animportant role in the exploration of oil and gas. The seismic method is a powerful remotesensing technique that can image the subsurface over depths from several meters to severalkilometers. The basic idea is to first generate an acoustic wave field by a localized source.This field travels down the subsurface and partly reflects at locations where the acousticrock properties change. The reflected wave field is measured by an array of localizedreceivers on the surface.

    The seismic method can be divided into three parts. It starts with the acquisition thatconsists of collecting raw data directly from the receivers. Usually, several different shotsare recorded of the same location. These different shot-records are stacked, or averaged,to reduce the influence of noise. Next, all the acquired data is processed to isolate thesignal that corresponds to the travel-time of the reflected wave field from the surface tothe reflectors. Signals due to diffraction and multiples should be suppressed. Multiples arewave fields that have been reflected more than once. Migration techniques are used to geta sharp, focused image of the subsurface. Finally, the identification of possible oil andgas reserves is done by interpretation of the processed image using geological models andinformation from well measurements such as logs and bore-hole images.

    The first 3D seismic survey was shot over a field in Texas in 1967. Since then, therehas been an increasingly rapid expansion in the application of this technology. A 3Dseismic image I(x, y, t) has two spatial coordinates (x, y) parallel to the surface and oneperpendicular time coordinate (t). The time usually corresponds to the two-way travel-time, i.e. the time it takes for a wave field to travel from the surface to a reflector and back.The typical spatial sample spacing is 12.5 m and the time resolution varies from 25 m inthe shallow part of the data to 100 m in deeper parts. Although many geological featuresare still below seismic resolution, seismic images can in favorable circumstances reveal the

  • 2 Chapter 1 Introduction

    internal structure of a reservoir. A 2D cross-section of a 3D seismic image is shown infigure 1.1. The 3D image is the shallow part of a processed 3D seismic survey, showing thesea-floor. The total time interval of the image is 0.75