USE OF QUANTITATIVE SEISMIC ANALYSIS TO DEFINE RESERVOIR ARCHITECTURE AND VOLUMES AN EXAMPLE FROM THE JOHAN SVERDRUP FIELD

Transcription

1 US OF QUNTITTIV SISMI NLYSIS TO FIN RSRVOIR RHITTUR N VOLUMS N XMPL FROM TH JOHN SVRRUP FIL Joachim Steindl, Inge Ribland Nilssen, Ole Jøran skim¹ Olav arkved² Gregory Partyka³ ¹) et norske oljeselskap S ²) Petoro S ³) OpenGeoSolutions Inc. NPF onference Stavanger ecember 2015

2 Johan Sverdrup Reservoir a challenge for seismic resolution Imaging of the reservoir is in large parts impaired by tuning and interference from other strong reflectors. We strive to provide complementary insights, more informative than a single view of the data. Tonjer valdsnes High Mapped time thickness 1

3 Outline Seismic resolution first order challenge in reservoir characterization Introduction Logs & ores asic principles Options for visualizing thin bedded resolution xamples - Visualizing internal layering oncepts Geological Reservoir Model Physics Verification onclusions nalysis & Vizualisation Seismic

4 Thickness and Seismic Tuning the simple perspective with a binary model Tuning Thickness is λ/4 15 ms or 1/¼ fmax elow tuning, thickness is over-predicted elow tuning we may use amplitude or inversion to predict true thickness Over predict thickness Max tuning Onset of interference/tuning

5 lternatives lueing: Shape the wavelet to match the reflectivity colour of geology Reflectivity Log (geology) The blue colour of geological reflectivity Reflectivity matched to the colour of geology oloured Inversion: Shape the wavelet and phase to match the impedance colour of geology coustic Impedance Log (geology) The red colour of geological Impedance Impedance matched to the colour of geology Spectral Inversion: Remove the wavelet and analyse the spectral behaviour of the reservoir to complement the time analysis etailed analysis Spectral behavior of thin layering Spectral behaviour of thin layering seen in seismic ourtesy of SG istinguished Lecture Program

6 dding spectral decomposition to our analysis Looking only at the time section conceals details nalogy to information in pre-stack data Time (ms) Zone of interest Frequency spectra from a 200ms window Signal extends from 3-to-80Hz ominant frequency ~7-to-25Hz 5

7 Removing the wavelet overprint Looking only at the time section conceals details nalogy to ignoring offset data in a seismic evaluation Removing wavelet overprint brings out more detail No bandwidth extension Time (ms) Zone of interest Frequency spectra from a 200ms window Signal extends from 3-to-80Hz ominant frequency ~7-to-25Hz 6

8 Spectral Inversion mplitude Spectra from Input data Spectral ecomposition amplitude amplitude amplitude frequency frequency frequency time Spectral Inversion Input data Overlapping nalysis Windows time Layering architecture defined from amplitude spectra Layers are defined as soft or hard No background model required + Hard Summation of solutions time Layering - Soft Summed Layering mplitude represents confidence in layer prediction 7

9 omplementary view Input data mp 1 lued data mp Freq Freq oloured Inversion The neutral view... mp Spectral Inversion See every contrast... mp Freq Freq See the layers... No wavelet layers...

10 tour across southern Johan Sverdrup Mapped time thickness Input data < 15ms < raupne shale raupne sandstone F Well Thickness ms Shale 1 Sandstone 5 Well Thickness ms Shale 8 Sandstone 9 Well Thickness ms Shale 7 Sandstone 4 Well Thickness ms Shale 10 Sandstone 9 Well Thickness ms Shale 13 Sandstone 14 9

11 From thick to thin reservoir I Input data Input data Shale 7 Sandst 4 Shale 10 Sandst 9 Shale 13 Sandst 14

12 From thick to thin reservoir I Input data lued reflectivity Shale 7 Sandst 4 Shale 10 Sandst 9 Shale 13 Sandst 14

13 From thick to thin reservoir I Input data oloured Inversion Shale 7 Sandst 4 Shale 10 Sandst 9 Shale 13 Sandst 14

14 From thick to thin reservoir I Input data Spectral inversion Shale 7 Sandst 4 Shale 10 Sandst 9 Shale 13 Sandst 14

15 elow tuning thickness over the valdsnes High I Input data Spectral inversion Layering Shale 8 Sandst 9 Shale 7 Sandst 4 Shale 10 Sandst 9

16 Thin Jurassic sand layer in southern Johan Sverdrup Input data I Spectral Layering Shale 1 Sandst 5 Shale 8 Sandst 9

17 yeballing the thin layer I 5 ms 5 ms 9 ms 5 ms onventional Seismic Trough / peak T = 9 ms Spectral Inversion Trough / peak T = 5 ms 16

18 onfirmation through synthetic modeling

19 Spectral layering across the valdsnes High W I I Triassic raupne sand Triassic Zechstein Rotliegendes asement 18

20 Spectral layering across the valdsnes High W I I Triassic raupne sand Triassic Zechstein Rotliegendes asement 19

21 Spectral layering across the valdsnes High W I I I Triassic raupne Triassic 20

22 Section from the Tonjer fan to Torvastad S I N 3 curvature map near U N onventional PSM 3 seismic 21

23 Section from the Tonjer fan to Torvastad S I N 3 curvature map near U N Spectral Inversion Layering derived from PSM seismic 22

24 Section from the Tonjer fan to Torvastad S I N 3 curvature map near U N Spectral Inversion Layering derived from PSM seismic 23

25 Tonjer fan extracted as geobody a way to estimate volumes N 24

26 Summing-up xploit the available bandwidth View the data with different colors nalyze the data in different domains Spectral Inversion demonstrates resolution beyond the tuning thickness and resolves thin layers and geomorphology The resolution can be more than 40% higher than from conventional processed seismic data 25

27 cknowledgements The authors would like to thank the other Johan Sverdrup Unit partners Statoil, Lundin and Maersk Oil for permission to show data from the area. The interpretations, views and opinions expressed in this paper are those of the authors, and are not shared by the other unit partners. The authors would also like to thank et norske, Petoro and OpenGeoSolutions for permission to give this presentation. 26