Dimensionality Reduction for Seismic Attribute Analysis

Transcription

1 Dimensionality Reduction for Seismic Attribute Analysis Bradley C. Wallet, Ph.D. University of Oklahoma ConocoPhillips School of Geology and Geophysics

2 Where oil is first found is in the minds of men - Wallace Pratt

3 Motivation

4 Motivation

5 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

6 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

7 Why care about seismic data? Single pre-stack data sets can be 10 s 100 s of terabytes in size Provide good spatial coverage exploration area Used to make high dollar decisions

8 Seismic shot Courtesy of Bin Lyu

9 Common midpoint gather

10 Migration

11 Convolutional model Lithology Velocity Density Impedance Reflection Coefficients Wavelet Shale Sand Shale x = * Sand Shale

12 Seismic data (Elebiju et al., 2009)

13 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

14 These are features

15 From one comes many Seismic data Attribute 1 Attribute 2 Attribute 3 Attribute 4 Attribute 5 Attribute 6 Attribute 7 Attribute 8

16 Coherence inline inline

17 Seismic 5 km (Bahorich and Farmer, 1995)

18 (Bahorich and Farmer, 1995) Coherence Coh salt 5 km

19 Σ Spectral decomposition Reflectivity Synthetic CWT Magnitude Voices CWT magnitude pos 0 Le Nozze di Figaro (Matos and Marfurt, 2011)

20 Spectral decomposition Time (s) A A 30 Hz 15 Hz A A A A 15 Hz Map 30 Hz Map (Laughlin et al., 2002)

21 Spectral decomposition 18 Hz Red 24 Hz Green 36 Hz Blue (Bahorich et al., 2002)

22 Dip attributes z θ (dip magnitude) φ (dip azimuth) n θ x (inline dip) a θ y (crossline dip) x ψ (strike) y (Marfurt, 2006)

23 Dip attributes Minimum dip tested (-20 0 ) Dip with maximum coherence (+5 0 ) Analysis Point Maximum dip tested (+20 0 ) Instantaneous dip = dip with highest coherence (Marfurt et al, 1998)

24 Dip attributes 0 High Dip Azimuth Hue Dip Magnitude Saturation 0 N W E S (c) (Guo et al., 2008)

25 How do we assimilate all these attributes?

26 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

27 PCA Rotates attribute space New dimensions are called principal components Var(pc1) > Var(pc2) > > Var(pc d) Defines variance as information

28 PCA (Wikapedia)

29 Watonga survey

30 Complex PCA

31 Complex PCA

32 PCA

33 PCA

34 PCA

35 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

36 Linear projections Poorly separated Somewhat separated proj( ) = ξ d i= 1 i i αξwell separated

37 The Grand Tour ( s)

38 Defining the tour

39 Image Grand Tour

40 View Locked Color IGT

41 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

42 Latent spaces

43 Generative topographical maps a) N b) Cartoon illustration of GTM

44 Waka 3D Canterbury Basin, offshore New Zealand E E S S (Figure by Origin Energy) (Modified from Mitchell and Neil, 2012)

45 Seismic 36

46 Peak Frequency

47 Peak spectral magnitude 38

48 Curvedness 39

49 GLCM homogeneity 40

50 Co-rendering 41

51 GTM

52 Waveforms as attributes (Wallet et al, 2009)

53 Watonga revisited (Wallet et al, 2009)

54 Diffusion maps Form n-by-n similarity matrix Normalize rows to sum to 1 Perform PCA on diffusion matrix

55 Diffusion maps Advantages Closed form solution Direct calculation of inter-point distances Not tied to a Euclidean space Eigenvalues Disadvantages Computationally intractable for reasonable sized data sets Out of training set data are not defined in mapping

56 Diffusion maps

57 Outline Seismic data Seismic attributes PCA Image grand tour Non-linear methods Conclusions Acknowledgements

58 Conclusions The human is still the best interpreter we have Attribute overload can overwhelm interpeters Dimensionality reduction produces highly interpretable images

59 Acknowledgments Prof. Kurt Marfurt (University of Oklahoma) Mr. Victor Aarre (Schlumberger Norway Technology Center) Mr. Tao Zhao (OU) Dr. Marcilio de Matos (Petrobras) CGG Veritas, Chesapeake Energy, Anadarko Petroleum, and the Government of New Zealand

60 Acknowledgments

61 Questions?