A Study of EM responses of some
Oil and Gas basins in Southeast Asia
using Model-based Inversion
Presented by:
Khin Moh Moh Latt
ID 108337
Date
17th May, 2010
:
Talk Outlines
• Introduction
• Literature
• Methodology
• Results
 Inversion of Synthetic data sets
 EM responses of the HC basins in SE Asia
• Conclusions and Recommendations
• References
Objective
• To study on applicability of electromagnetic (EM)
method, in particular, the controlled source EM
(CSEM) method for hydrocarbon exploration by
investigating the possible EM responses of the
hydrocarbon basins in the South East Asia using
model-based inversion
Scope of work
• Review on recent applications of EM method in the world and in the
Southeast Asia region
• Revisit the CSEM survey procedure
• Collect the geological and petrophysical data of some hydrocarbon
basins in Southeast Asia to construct EM geomodel and to analysis the
forward and inverse modeling
• Modify the 1D-inversion open source codes with adding the new
smoothness constraint module in FORTRAN
• Apply the newly developed inversion program to calculate the EM
responses (i.e., apparent resistivity, phase) and 1D resistivity model
structure with depth for a number of Southeast Asia basins
(Phitsanulok, Fang, North Malay, Cuu Long and Central Burma)
Introduction
Why we consider EM methods for hydrocarbon exploration?
Seismic method is fallible
Electrical resistivity is closely linked to fluid properties and it is also
the typical properties of the rocks
Integrated interpretation is less risky, save the money from drilling dry
well
Why now?
Deepwater exploration expensive, risky
Marine EM a deepwater tool
What are marine EM?
Two dominant methods: magnetotelluric (MT) and Controlled Source
EM (CSEM) methods
Global Oil and Gas Consumption
1990
2009
(85MMB)
2030
(120MMB)
Analysts currently predict that energy production in SE Asia will increase by
85% over the next 15 years.
Many researchers believe that the marine EM methods, especially controlled
source EM, will become a standard tool in the offshore exploration toolbox.
Feather (EMGS), 2009.
CSEM to detect hydrocarbon
CSEM to detect hydrocarbon
Why only one or two basins in SE Asia have been investigated by this
CSEM method?
Lack of technology
Lack of Confidence in this method
No investment
Statistical Analysis of CSEM
In the middle of 2009, EMGS had analyzed CSEM data and focus on the
drilling results demonstrate in terms of discovery rates. 86 wells were
available for statistical analyses.
Identification of reservoir from anomalous response (Hesthammer, et al., 2010).
Statistical Analysis of CSEM
Identification of reservoir from anomalous response (Hesthammer,
(Hesthammer,etetal.,
al.,2010).
2010).
Principles of EM Sounding
EM instruments operate on the principle of EM induction, which is
based on Ampere’s law, Faraday’s law and Ohm’s law
Tx
Rx
Faraday’s Law
Ampere’s Law
CSEM Workflow & Survey Procedure
Workflow
Data
Acquisition
Survey
Design
50-100m
300m
Transmitter
Processing
Inversion
Receiver
(Image courtesy of Scripps Institution of Oceanography, 2009)
Model-Based Inversion
Forward
modeling
Earth model
Model-based
Inversion
Synthetic Data from FW
modeling
Forward modeling assumes the earth model and then the theoretical response
for that model is calculated. The model is then refined until the calculated
response matches the observed or measured field response. The model
refinements can be made using an automated iterative process or inversion.
(afterSasaki , 2009)
Flowchart of Model-based Inversion
Synthetic Model
Inversion Model
Calculated responses
Synthetic responses
N
d  
j 1
Data misfit, ϕd
Calculate the data misfit
until convergence
M F


j

d


m

 j
i
i 1 mi


d  Amd min
2
Am  d
2
0
Am  d
Derivative or
Jacobian matrix
  Am  d   Cm  min
2
Add constraints, βϕm
(ϕd+βϕm)
Calculate the model
parameter by using
Gram -Schmidt solver
Update the model
parameter
2
1st and 2nd derivative
constraints, C are used
in this research
 A d


0


C


Cm

 N N
A 
 d


 m  
(0) 
  C 
   Cm 
m  ?
2
Model objective functions
Results
1)
2)
3)
4)
5)
Please enter your input file name:
Please enter your new output file name:
Please enter 1 or 0 for ITEST selection:
Please enter the regularization parameter, :
Please enter 1 or 2 for 1st or 2nd derivative constraint:
ITEST=1 (synthetic data are generated from the model given)
ITEST=0 (actual data are needed)
1st derivative constraint method (exciting method by Sasaki)
2nd derivative constraint method (modified by the researcher)
Synthetic Models
Depth
(m)
Model A
(ohm-m)
Conductive wedge
Model B
(ohm-m)
Resistive Wedge
200.0
600.0
Half-space
500.0
50.0
500.0
10.0
500.0
100.0
(a) Synthetic model A
(b) Synthetic model B
(Catherine de
Groot- Hedlin
and Steven
Constable,
2004)
(c) Inversion model for both A&B
Conductive wedge (Model A)
(a)
1
(b)


10
Resistivity (ohmm)
100
1000
0

True Geomodel
500
t 
t 
Data Misfit (RMS)
0.1
Depth (m)
t 
0.01
1000
1500
2000
0.001
0
1
2
3
4
5
Iteration
6
7
8
9
2500
Figure 1. (a) Plot of data misfit versus iteration number for three  values
(b) Three maximally smooth models in a 1st derivative sense for three different 
Resistive wedge (Model B)
(a)
100
(b)
1
Resistivity (ohmm)
10
100
1000
0


10

1
Depth (m)
Data Misfit (RMS)
500
0.1
1000
1500
True Geomodel
t 
0.01
t 
2000
t 
0.001
0
1
2
3
4
5
Iteration
6
7
8
9
2500
Figure 2. (a) Plot of data misfit versus iteration number for three  values
(b) Three maximally smooth models in a 1st derivative sense for three different 
Conclusion (For synthetic tests)
1. Forward modeling algorithm by Sasaki (2009) could
give the good EM responses for both “resistive wedge” and
“conductive wedge” structure.
2. The new smoothness constraint (second derivative)
could give a lower data misfit and better model than the
first derivative one.
3. The value of  chosen is 0.01 for the inversion
because it gave the smallest data misfit among trial values.
Location of study basins
Figure 3. Selected basins from SE Asia
North Malay Basin
Figure 4. Location of North Malay Basin (Modified from USGS, 2000)
Creating a resistivity model
Figure 5. Simplified comparisons of stratigraphy
from different areas in the Malay Basin
province (USGS, 2002)
Figure 6. North Malay basin
geomodel for forward
analysis
Results of Forward Modeling
10
Synthetic data
Calculated data
45
Phase (Degree)
Apparent resistivity (Ohmm)
•
46
44
43
42
41
1
0.001
0.01
0.1
Period (sec)
1
0.001
0.1
0.01
Period (sec)
Figure7. Apparent resistivity versus period and phase versus period
1
Result of Model-Based Inversion
Resistivity (Ohmm)
Resistivity (Ohmm)
1
10
1
100
10
100
0
0
True Geomodel
500
500
1000
1000
1500
Depth (m)
Depth (m)
First derivative Inversion model
1500
2000
2000
2500
2500
3000
Second derivaive inversion model
3000
(a)10- layer inversion model
(b) 75-layer inversion model
Figure 8. Comparison of the true and inversion models with a maximum
smoothness in a 1stderivative (solid line) and 2ndderivative (dash line)
Conclusions
1)
The new smoothness constraint (second derivative) could be integrated to
the existing 1D inversion program. Moreover, the program became more
flexible in dealing with the regularization parameter.
2)
The new inversion code module was tested on synthetic module and it is
very stable and typically converges within five or six iterations. The results of
model-based inversion are very close to those of the synthetic module.
Therefore, it is proving that this model-based inversion could be used in
finding EM responses and the resistivity model structure of oil and gas
basins in Southeast Asia.
3)
The final geomodel resultant from the inversion program is independent of
the starting guess and it is the model of smallest roughness with the specific
misfit.
As conclusion of this research, EM method can be applied in Southeast
Asia and EM data can be interpreted by using this 1D inversion program.
Recommendations
1) The real electromagnetic data could be inverted by using the 1D inversion
program improved in this study.
2) Scripps Institution of Oceanography (SIO) has published the
OCCAM1DCSEM inversion and DIPOLE1D forward modeling source
codes on January 2010. Therefore, next study can investigate the codes
from SIO and compare with the codes used in this research.
3) The next study can focus on studying 2D  3D EM responses of the HC
basins in the Southeast Asia.
4) As this 1D inversion program is the open source code, it can be used in
academic and training of EM sounding, especially for Southeast Asia
region where the exploration of industry is growing rapidly.
References
Alistair R. Brown. (2005) Do you need marine EM methods?
Geophysical corner, 28-30.
Brady, J., Campbell, T., Fenwick, A., Campbell, C., Ferster, A., Labruzzo, T., et al.
(2009).
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