Using Seismic Inversion and Net Pay to Calibrate Eagle Ford Shale Producible Resources

Bo Chen1, Dhananjay Kumar1, Anthony Uerling2, Sheryl Land2, Omar Aguirre2, Tao Jiang1, and Setiawardono Sugianto1

1BP America, 501 Westlake Park Blvd., Houston, Texas 77079

2BP America, 737 N. Eldridge Pkwy., Houston, Texas 77079

GCAGS Explore & Discover Article #00104* http://www.gcags.org/exploreanddiscover/2016/00104_chen_et_al.pdf

Posted September 13, 2016. *Abstract published in the GCAGS Transactions (see footnote reference below) and delivered as an oral presentation at the 66th Annual GCAGS Convention and 63rd Annual GCSSEPM Meeting in Corpus Christi, Texas, September 18–20, 2016.

ABSTRACT

The production outcome of an Eagle Ford well depends on many factors including reservoir properties. This paper discusses an utilization of seismic attributes to predict them and identify the high production potential areas. Multiple studies have identified the sweet spots in unconventional plays, however not many have been directly correlated with and confirmed by the production data. We studied the production data and ana-lyzed the reservoir characteristics associated with it taking into account the operation procedures. It was found that the producible resources lie in the thicker and more po-rous intervals. Both thickness and porosity can be determined with seismic data. Seis-mic inversion is utilized for porosity prediction and seismic net pay for ‘net’ thickness prediction with higher porosity. The seismic net pay, confirmed by the blind well log data, has been used to predict the producible resources for future wells in the Eagle Ford shale in South Texas. The study includes the following four steps. First, a correlation between the petrophysical net pay and the estimated production volume is established. Well logs are analyzed to establish a method to predict net pay from seismic data. Sec-ond, post migration seismic data conditioning is applied to improve seismic data quality, by attenuating noises, flattening the gathers, and balancing the frequency spectrum and amplitude across offsets. Good quality seismic data are required for seismic net pay estimation. Third, using the conditioned seismic data, colored inversion is applied to invert the reflectivity data to relative acoustic impedance. Acoustic impedance is in-versely proportional to porosity and is used to predict porosity in the lower Eagle Ford Shale. Finally, seismic net pay is calculated by detuning the relative acoustic impedance and integrating over the gross thickness intervals. To quality control (QC) the results, the predicted seismic net pay is compared with well log data and estimated production data. We found that seismic net pay in the lower Eagle Ford as an indicator of its reser-voir quality. The reliable estimation of seismic net pay requires an understanding of the rock properties, good quality well data, seismic data conditioning, well calibrated hori-zons, and accurate seismic inversion for impedance followed by porosity prediction.

Originally published as: Chen, B., D. Kumar, A. Uerling, S. Land, O. Aguirre, T. Jiang, and S. Sugianto, 2016, Using seismic inversion and net pay to calibrate Eagle Ford Shale producible resources: Gulf Coast Association of Geological Societies Transactions, v. 66, p. 927.

1

Using Seismic Inversion and Net Pay

to Calibrate Eagle Ford Shale

Producible Resources

B. Chen, D. Kumar, A. Uerling, S. Land,

O. Aguirre, T. Jiang, S. Sugianto

BP America Inc.

1

Objectives

• Identify key petrophysical properties driving

production

• Predict such rock properties from seismic

2

Outline

• Studied area

• Petrophysical analysis

• Seismic data conditioning

• Seismic (coloured) inversion

• Seismic net pay

• Conclusions

3

Production Potential Prediction

EU

R (

bcf

)

Log High Quality Shale Thickness (ft)

EU

R (

bcf

)

Seismic Net Pay (ft)

2R =0.726 2R =0.728

Log and seismic derived high quality

shale (HQS) thickness correlate well

with EUR when operation is the same.

4

Studied Area

Seismic Survey

Studied area is

located in the dry

gas window,

western portion

of Eagle Ford

play.

5

Petrophysical Analysis

Porosity (%)

Co

re P

erm

eab

ilit

y (

nD

)

0 5 10 15 0.001

0.01

0.1

1

10

100

1000

0

5

TO

C (

%)

Porosity (%) 5 10 15 20

10

High quality shale (HQS) interval: shale intervals with higher porosity,

higher permeability, higher TOC, higher gas saturation, lower clay content.

6

Colored by wells

Rock Quality versus EUR

R² = 0.38

R² = 0.73

Seismic shows

more porous and

thicker rocks than

nearby pilot

Close to fault

HQS intervals

Gross intervals

EU

R (

bcf

)

TGIP (bcf/section)

EU

R (

bcf

)

TGIP (bcf/section)

Resources in High Quality Shale

form stronger correlation with EUR

with the same completion designs.

7

Petrophysical Analysis Well A Well B Well C Well D

Ray PHIT Perm flag Ray PHIT Perm flag Ray PHIT Perm flag Ray PHIT Perm flag Gamma HQS Gamma HQS Gamma HQS Gamma HQS

50ft

154

137

TGIP (bcf/section)

Gross Interval

HQS Interval

68

22

76

74

165

134

8

Seismic Data Conditioning Before LEF Instantaneous Amplitude

After LEF Instantaneous Amplitude

Post-migration seismic data

conditioning reduced noise, increased

the resolution and improved the

amplitude fidelity.

3km

3km

Mismatch Buda LEF

After

100 ms

1km Buda LEF

UEF

D oublet

Before

100 ms

1km

UEF

20

- 10

- 20

- 30

4 0 6 0 8 0

20

- 10

- 20

- 30

4 0 6 0 8 0

(Hz)

(dB)

(Hz)

(dB) Before

After

Red: trough

Blue: peak

low

high

low

high

9

Rock physics relationships

Porosity forms a good correlation with P-wave acoustic impedance

(AI), which can be derived confidently from seismic data.

R² = 0.78

P-wave Acoustic Impedance ((ft/sec)*(g/cc)) 25,000 35,000 45,000

Poro

sity

0.04

0.08

0.12

10

Seismic (coloured) Inversion Inversion operator

Inversion QC with wells

Coloured inversion (CI) and

model-based inversion (MBI)

generated similar results.

(Kumar et al., 2014)

Coloured inversion (CI)

provides bandlimited

(relative) impedance

Log: red Seismic: blue

(Lancaster & Whitecombe, 2000)

20k 60k -12k 12k

MBI CI

11

Seismic Net Pay

12

Petrophysical net-to-gross = HQS thickness

gross reservoir thickness

Geophysical net-to-gross = average impedance value over reservoir

impedance value at 100% net (HQS)

(Connolly, 2010)

Ray Porosity AI Log AI 100% net AI Gamma Seismic Modeled

Seismic Net Pay

Tim

e (m

s)

Sei

smic

Net

to

Gro

ss

Max seismic net to gross Average band-limited

impedance Apparent thickness

Time Thickness (ms)

Ap

pa

rent T

hick

ness (m

s) Wedge model

13

Time Thickness (ms)

200

150

100

50

0

0 50 100 150 200 0 50 100 150 200

0.2

0.4

0.6

0.8

1

150

100

50

0

Seismic net-to-gross = average seismic impedance

average modeled impedance for 100% net (HQS)

true thickness (ms)

apparent thickness (ms) ×

Seismic net pay = seismic net-to-gross × apparent thickness

Top

Base data range

(Connolly, 2007)

data range

Seismic Maps

(ms) 3km

(%) 3km

(ft) 3km

0

5

10

15

0

10

20

30

40

0

200

50

100

150

Time thickness Inverted porosity

Seismic net pay

Seismic net pay more directly

characterizes the sweet spot

distributions than thickness or

porosity maps alone.

14

QC with Well Logs

0

0.03

0.06

0.09

0.12

0.15

0 2 4 6 8 10 12 14

Log porosity Seismic porosity

Po

rosi

ty

Well index

HQ

S T

hic

kn

ess

(ft)

Seismic Net Pay (ft)

2R =0.79

Seismic estimated porosity and

net pay match with well log

values with high accuracy.

15

Seismic Prediction of EUR

EU

R (

bcf

)

Log HQS thickness (ft)

EU

R (

bcf

)

Seismic Net Pay (ft)

2R =0.726 2R =0.728

The seismic net pay correlation to EUR is as good

as the correlation with well log HQS thickness.

16

Conclusions

• TGIP in HQS (high quality shale) intervals correlates

well with EUR.

• Seismic (coloured) inversion and seismic net pay

methods generate reliable porosity and HQS thickness.

• Seismic derived HQS can predict EF shale sweet spots.

Plan new wells

Identify refrac candidates

Appraise completion trials

17

Acknowledgements

• BP Lower 48 and BP Upstream Technology

• Numerous BP colleagues’ suggestions and

feedback, including C. Sullivan, W. Simpson,

C. Lazos, A. Cunningham, J. Zhang, S.

Davis, S. Li, L. Sarle, M. Blome, etc.

18

References

• Connolly, P., 2007, A simple, robust algorithm for seismic

net pay estimation: The Leading Edge, 26, 1278-1282, doi:

10.1190/1.2794386

• Connolly, P. A., 2010, Robust workflows for seismic

reservoir characterization: SEG Distinguished Lecture.

• Kumar, D., Sugianto, H., Li, S., Patel, H. and Land S., 2014,

Using relative seismic impedance to predict porosity in the

Eagle Ford shale: SEG Technical Program Expanded

Abstracts, 2688-2692, doi: 10.1190/segam2014-0473.1

• Lancaster, S. and D. Whitecombe, 2000, Fast-track Coloured

Inversion: SEG expanded abstract, doi: 10.1190/1.1815711

19

Questions

20