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: 17 th May,

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 OutlinesTalk Outlines

• Introduction

• Literature

• Methodology • Results Inversion of Synthetic data sets EM responses of the HC basins in SE Asia

• Conclusions and Recommendations

• References

ObjectiveObjective

• 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 workScope 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)

IntroductionIntroduction

Why now? Deepwater exploration expensive, risky

Marine EM a deepwater tool

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

What are marine EM?Two dominant methods: magnetotelluric (MT) and Controlled Source

EM (CSEM) methods

Global Oil and Gas ConsumptionGlobal Oil and Gas Consumption

2030

2009

1990

(85MMB)

(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 hydrocarbonCSEM to detect hydrocarbon

CSEM to detect hydrocarbonCSEM 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 CSEMStatistical 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 CSEMStatistical Analysis of CSEM

Identification of reservoir from anomalous response (Hesthammer, et al., 2010).(Hesthammer, et al., 2010).

Principles of EM SoundingPrinciples of EM Sounding

Tx Rx

Ampere’s Law

Faraday’s Law

EM instruments operate on the principle of EM induction, which is based on Ampere’s law, Faraday’s law and Ohm’s law

CSEM Workflow & Survey ProcedureCSEM Workflow & Survey Procedure

Survey Design

DataAcquisition

Processing Inversion

Workflow

50-100m300mTransmitter Receiver

(Image courtesy of Scripps Institution of Oceanography, 2009)

Model-Based InversionModel-Based Inversion

Forward modeling

Earth model Synthetic Data from FW modeling

Model-based Inversion

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 InversionFlowchart of Model-based Inversion

Inversion ModelSynthetic Model

Calculated responses Synthetic responses

Data misfit, ϕd

Add constraints, βϕm

(ϕd+βϕm)

Calculate the model parameter by using

Gram -Schmidt solver

1st and 2nd derivative constraints, C are used in this research

Derivative or Jacobian matrix

Update the model parameter

Calculate the data misfit until convergence

2

1 1

N Mj

d j ij i i

Fd m

m

2mind A m d

2 2minA m d Cm

20A m d

A m d

(0)

A dm

C Cm

0

N N

A d

C Cm

?m

Model objective functionsModel objective functions

ResultsResults

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 ModelsSynthetic Models

Depth(m)

Model A(ohm-m)

Conductive wedge

Model B(ohm-m)

Resistive Wedge

200.0 500.0 10.0

600.0 50.0 500.0

Half-space 500.0 100.0

(a) Synthetic model A (b) Synthetic model B (c) Inversion model for both A&B

(Catherine de Groot- Hedlin and Steven Constable, 2004)

Conductive wedge (Model A)Conductive wedge (Model A)

10 100 1000Resistivity (ohm m )

2500

2000

1500

1000

500

0

De

pth

(m

)

True G eom odel

t

t

t

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

(a) (b)

0 1 2 3 4 5 6 7 8 9Iteration

0.001

0.01

0.1

1

Dat

a M

isfi

t (R

MS

)

Resistive wedge (Model B)Resistive wedge (Model B)

0 1 2 3 4 5 6 7 8 9Iteration

0.001

0.01

0.1

1

10

100

Dat

a M

isfi

t (R

MS

)

1 10 100 1000Resistivity (ohmm )

2500

2000

1500

1000

500

0

De

pth

(m

)

True G eom odel

t

t

t

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

(a) (b)

Conclusion (For synthetic tests)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 basinsLocation of study basins

Figure 3. Selected basins from SE Asia

North Malay BasinNorth Malay Basin

Figure 4. Location of North Malay Basin (Modified from USGS, 2000)

Creating a resistivity modelCreating a resistivity model

Figure 6. North Malay basin geomodel for forward analysis

Figure 5. Simplified comparisons of stratigraphy from different areas in the Malay Basin province (USGS, 2002)

Results of Forward Modeling Results of Forward Modeling

0.001 0.01 0.1 1Period (sec)

1

10

App

are

nt r

esis

tivity

(O

hm

m) • Synthetic data

C alcu lated data

0.001 0.01 0.1 1Period (sec)

41

42

43

44

45

46

Pha

se (

Deg

ree)

Figure7. Apparent resistivity versus period and phase versus period

Result of Model-Based InversionResult of Model-Based Inversion

True G eom odel

F irst deriva tive Invers ion m odel

Second deriva ive inversion m odel

1 10 100Resistivity (Ohmm)

3000

2500

2000

1500

1000

500

0

Dep

th (

m)

1 10 100Resistivity (Ohmm)

3000

2500

2000

1500

1000

500

0

Dep

th (

m)

(a)10- layer inversion model (b) 75-layer inversion modelFigure 8. Comparison of the true and inversion models with a maximum smoothness in a 1stderivative (solid line) and 2ndderivative (dash line)

Conclusions 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 SoutheastAsia and EM data can be interpreted by using this 1D inversion program.

Recommendations 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 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).

Electromagnetic Sounding. Oilfield Review Spring 2009, 21 (1), 4-19. Constable, S. C., Orange, A. S., Hoversten, G. M. and Morrison, H. F. (1998), Marine magnetotellurics for petroleum exploration Part 1: A seafloor system. Geophysics, 63, 816-825, 1998. Cagniard. L (1953), Basic Theory of the Magneto-Telluric Method of Geophysical Prospecting, Geophysics 18, 1953, 605- 635. Chandola, S. K., Karim, R., Mawarni, A., Ismail, R., Shahud, N., Rahman, R., et al., (2007), Challenges in Shallow Water CSEM Surveying; A Case History from Southeast Asia. International Petroleum Technology Conference (IPTC). 

References (Cont.) References (Cont.)

Daout, F., A. Khenchaf, and J. Saillard (1994), The effect of salinity and temperature on the electromagnetic field scattered by sea water, paper presented at OCEANS '94, Brest, France. Ellingsrud, S., Eidesmo, T., Sinha, M.C., MacGregor, L.M. and Constable, S. (2002) Remote sensing of hydrocarbon layers by Sea Bed Logging (SBL): results from a cruise offshore Angola. The Leading Edge, 21, 972–982. Eidesmo, T., Ellingsrud, S., MacGregor, L.M., Constable, S., Sinha, M.C., Johansen, S., et al., (2002) Sea Bed Logging (SBL), a new method for remote and direct identification of hydrocarbon filled layers in deepwater areas. First Break, 20(3), 144–152. Eric Carlen (2010), Website of Georgia Tech education, accessed on 18 January 2010, http://www.math.gatech.edu/~carlen/1502/html/pdf/gram.pdf Fraser. Y. (2006). New techniques for frontier exploration. E&P, April 2006, 81p.

References (Cont.) References (Cont.)

Grandis, H., Widarto, D. S. and Hendro, A. (2004), Magnetotelluric (MT) Method in Hydrocarbon Exploration: A New Perspective, Jurnal Geogisika 2004/2, 14-19.Hesthammer, J., Stefatos, A. and Boulaenko, M (2010), CSEM performance in light of well results, The Leading Edge, January, 2010, 34-41. Hayt, W.H. (1958) Engineering Electromagnetic, 238-2450. Hohmann, G. W., 1987: Electromagnetic Methods in Applied Geophysics, volume 1 of Investigations in Geophysics Volume 3, chapter 5: Numerical Modeling for Electromagnetic Methods of Geophysics, 313–363. Society of Exploration Geophysicists. Keller, G. V. (1988), Rock and Mineral Properties, in Electromagnetic Methods in Applied Geophysics: Volume 1, Theory, edited by M. N. Nabighian, pp. 13-51, Society of Exploration Geophysicists, Tulsa, OK.

Leonardon, E.G. (1928), Some Observation Upon Telluric Currents and Their Applications to Electrical Prospectiong, Terrestrial Magnetism and Atmospheric Electricity 33, (March- December 1928), 91-94.