1 RPSEA Project – Facies probabilities from seismic data in Mamm Creek Field Reinaldo J Michelena Kevin Godbey Patricia E Rodrigues Mike Uland April 6,

1

RPSEA Project –

Facies probabilities from seismic data in Mamm Creek Field

Reinaldo J MichelenaKevin GodbeyPatricia E RodriguesMike Uland

April 6, 2010

2

Location of Mamm Creek field

Mamm Creek

3

Area of interest

Reservoir simulation

area

Geological model

area

4

Stratigraphic column

Coastal /Alluvial Plain

Shoreface/Deltaic

Marsh, Mire, Swamp, and Estuarine

Marine Shelf/Ramp

~2,200 ft

Geological model interval

5

Intervals of interest for seismic analysis

Price Coal

UMV

MiddleSS

Rollins

Cozette

Mancos

seismic tracedensity log

Lower amplitudes (fluvial)

Higher amplitudes(transitional and marine)

beginning of coal interval

Seismic amplitudes

6

Reservoir characterization at Mamm Creek

MOTIVATION

• Effective development of Mamm Creek field requires detailed understanding of all geological features that control gas accumulation and connectivity

GOAL

• Build geological models that capture the main geological complexities of the field

Show how seismic data can be used to help mapping sand distribution in Mamm Creek field

this presentation

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Challenges from rock physics diagnostics

sands

Vp

Vs

RH

O

Vs

RH

O

Vp

Potential difficulties: 1)Sands and shales show significant overlap in log scale crossplots of elastic properties2)Presence of thin coal layers may mask “real” seismic response from surroundings sands and shales.

shales

coals

8

Data inventory

• Seismic:

– 3D, PP pre-stack, NMO corrected gathers

– 3D PS_fast and PS_slow stack volumes (not part of same survey as PP data)

• Well:

– 102 density logs. Most wells with Gamma Ray, Neutron Porosity and Resistivity

– 3 sonic logs

– 2 dipole sonic logs (one of them crossdipole)

– Formation tops from Bill Barrett Corporation

– 8 cores

– 12 FMI logs

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Summary of seismic workflow

• Perform petrophysics/rock physics diagnostics

• QC seismic data

• Precondition pre-stack gathers for AVO analysis

• Interpret PP data

• Interpret PS data (consistent with PP interpretation)

• Invert PP pre-stack data for Vp, Vs and density

• Invert PS stack data for pseudo shear impedance (Zps)

• Generate velocity model that honors all marker and horizon information in PP and PS time

• Perform time to depth conversion of seismic derived information

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Inverted seismic volumes in depth

RHO Vp Vs

Zps_fast Zps_slow

low

high

UMV

MIddleSS

Z

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Attribute crossplots

• Inverted seismic attributes (3 from PP and 2 from PS) can be cross plotted in many different ways

• After examining various combinations of these five attributes, we decided to focus only on 6 of such combinations and assess the contribution of each attribute:

– Vp vs Vs

– Vp vs RHO

– Vs vs RHO

– Zps_fast vs Zps_slow

– Vp vs Vs vs RHO

– Vp vs Vs vs RHO vs Zps_fast vs Zps_slow

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Seismic attributes at well locations (color = log scale facies)

Vp

Vs

Vp

RH

O

Vs

RH

Othick sandbackgroundbackground

Zps fast

Zps

slo

w

Transitional and marine interval

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Log scale attributes (color = lithology)

sands

Zps fast

Zps

slo

w

Vp

Vs

Vp

RH

O

Vs

RH

O


14

Seismic attributes at well locations (color = log scale facies)

Vp

Vs

Vp

RH

O

Vs

RH

Othick sandbackgroundbackground

Zps fast

Zps

slo

w


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Probabilities from seismic attribute crossplots

ChannelNo channel

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Probabilities extracted at well 14C-20, fluvial

Sand Ave 2 2 2 3 5 flags

Vp, Vs Vp, Rho Vs ,Rho Vp, Vs, Rho Vp, Vs, Rho, 3C

Gamma Lithology Ray

30 wells used for calibration

UMV_MKR (4766’MD)

TOP_GAS (5306’MD)

MKR1 (6235MD’)

100’

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Probability using Vp, Vs, RHO, Zps_fast and Zps_slow

102 wells used for calibration

0.60Thick sand probability

Stratigraphic slice

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Real vs estimated sand thickness (Vp, Vs, RHO and 3C)

Prob cutoff = 0.18

cc=0. 68

Fluvial interval

Thickness from log data (ft)

Thi

ckne

ss f

rom

sei

smic

dat

a (f

t)

30 wells used to calibrate seismic

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Thick sand flags at well locations

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Seismic derived probabilities at well locations

Probabilities from 5 attribute crossplot calibrated with 102 wells

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Thick sand flags and probabilities at well locations

Probabilities from 5 attribute crossplot calibrated with 102 wells

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Correlation coefficient* per well (marine)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.911

a-21

11c-21

12a-20

12b-20

12c-21

13a-20

14a-21

21a-21

21b-28

21d-21

22a-21

22b-21

22c-28

23d-20

24c-20

31a-21

31c-20

32a-21

32c-21

33a-21

33c-20

34a-20

34b-21

34d-20

41a-21

41c-20

42a-20

42c-20

43a-20

43b-21

43d-20

44a-21

44c-20

44d-21

Vp, Vs, RHO, PS, 30 calibration wellsVp, Vs, RHO, PS, 102 calibration wells

Well name

Cor

rela

tion

Vp, Vs, RHO, 30 calibration wellsVp, Vs, RHO, 102 calibration wells

* Real average sand vs

Estimated probability

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Sorted correlation coefficients (marine)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100

Vp, Vs, RHO, 30 calibration wells

Cor

rela

tion

Sort index

24


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100


Cor

rela

tion

Sort index

25


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100

Vp, Vs, RHO, PS, 30 calibration wellsVp, Vs, RHO, 30 calibration wellsVp, Vs, RHO, 102 calibration wells

Cor

rela

tion

Sort index

26


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100



Cor

rela

tion

Sort index

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Sorted correlation coefficients (fluvial)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100



Cor

rela

tion

Sort index

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Log scale Vs_fast vs Vs_slow (well 41A-28)

SandBackgroundBackground

Log scale Vs_fast (ft/s)

Log

scal

e V

s_sl

ow (

ft/s)

Log scale Vs_fast (ft/s)

Log

scal

e V

s_sl

ow (

ft/s)

Fluvial interval Marine interval

• Fluvial shales are more azimuthally anisotropic than marine shales• Fluvial sands are less azimuthally anisotropic than marine sands

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Conclusions

• Facies probability estimation algorithm from multidimensional crossplots of seismic attributes yields useful results even when elastic properties of sandy and background facies overlap completely

• When using PP data only, the best results are obtained when using Vp, Vs, and RHO simultaneously

• The best separation is achieved when using all five attributes (Vp, Vs, RHO, Zps_fast and Zps_slow)

• Sensitivity of PS data to azimuthal anisotropy helps to improve sand identification where sands are more anisotropic than the background

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Acknowledgments

• The authors acknowledge RPSEA (Research Partnership to Secure Energy for America) for financial support

• Thanks also to Bill Barrett Corporation for providing the data used for this study

Documents

1 RPSEA Project – Facies probabilities from seismic data in Mamm Creek Field Reinaldo J Michelena Kevin Godbey Patricia E Rodrigues Mike Uland April 6,