Workshop on 4D seismic and history matching

4D elastic full-waveform inversionMaking full use of the life of field seismic

datasets

Wiktor Weibull

Associate professorDepartment of Petroleum Engineering

E-mail: wiktor.w.weibull@uis.no

Stavanger, April 28, 20161 / 19

Life of field seismic (LoFS)

What is LoFS?

Permanent reservoir monitoring (PRM) using seismic data

History of LoFS:

I First LoFS experiment was inFoinaven in 1995

I Valhall installed in 2003

I Clair installed in 2006

I Ekofisk installed in 2010

I Jubarte installed in 2012

I Snorre installed in 2013

I Grane installed in 2014

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

0

50

100

150

200

250

Cove

rage

(sq.

km

)

Foin

aven

Valh

all

Clai

r

Ekof

isk

Juba

rte

Snor

reGr

ane

(Source: Eriksrud, 2014) 2 / 19

Life of field seismic

I Life of field seismic is still in its infancy

I High frequency of repetition (2 surveys per year)

I Repeatability is mainly controlled by diurnal and seasonal changes in watervelocity

I Dynamic images show changes in seismic even in obscured areas

I Data are in many ways underutilized

I Data acquired consists of 4 components (Pressure and 3 orthogonal particlemotions)

I Mostly the P component is currently being used for qualitative andquantitative 4D analysis

I Exploring the full potential of the data will help improve history matching

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Surface seismic vs. Ocean bottom seismic

(Modified from Ikelle and Amundsen, 2005) (Ikelle and Amundsen, 2005)

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Elastic wavefields

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PP and PS imaging

I PP reflections consist of incident P-wavesreflected as P-waves

I PS reflections consist of incident P-wavesreflected as S-waves

I In contrast to P-waves, S-waves are largelyinsensitive to pore fluids

I PS reflections help to illuminate areas that areobscure for PP reflections

I The conventional independent processing of PPand PS leads to depth mismatch between theimages

(Modified from Ikelle and Amundsen, 2005)

(Bertrand et al., 2013)

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Full-waveform inversion (FWI)

I FWI is a classical non-linear least squares inverseproblem

I The observed seismic data is fitted to aparameter model through a non-linearoptimization procedure seeking to minimize thedifference between observed and simulatedseismic data

I Depending on the choice of the wave equationused to simulate the seismic data, physicalphenomena such as P- to S-wave conversion, andanisotropy can be used in the inversion process

(Sirgue et al., 2009)

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Full-waveform inversion

I In FWI, the goal is to minimize the differencebetween waveforms

I Very non-linear because the waveforms are oscillatory

Waveform equation

u(x, t) =

∫d3x′G(x, t;x′, 0) ∗ S(x′, t)

(Fichtner et al., 2009)

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Full-waveform inversion

Definition of the error (data misfit)

S(m) =1

2‖u(x, t;xs)− uobs(x, t;xs)‖2,

where u is the simulated data and uobs is the recorded data.

I To mitigate the non-linearity, it is customary to add a regularization term(model misfit)

SR =1

2‖m(x)−m0(x)‖2,

where m is the estimated model, and m0 is the initial estimate.

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Full-waveform inversion

Solved using an iterative method

mk+1 = mk − αkgk,

mk model at iteration kgk gradient of S(m) at iteration kαk step length at iteration k

Gradient of S is normally computed using theadjoint state method (Chavent, 2009)

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Application to ocean bottom data from Valhall(Sirgue et al., 2009)

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Application to ocean bottom data from Tommeliten(Warner et al., 2013)

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4D FWI

Sequential strategy

m0 ubase

FWI

mn

m0 umon

FWI

mk

Data difference strategy (Zheng et al., 2011)

m0 ubase

FWI

mn

un

umon = un + (umon − ubase)

FWI

mk

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Example of 4D FWI using multi-component data(Raknes and Arntsen, 2015)

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Example of 4D FWI using multi-component data(Raknes and Arntsen, 2015)

Case 1:1% and 5% 4D anomalies

Case 2:5% 4D anomali

500m 575m

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Example of 4D FWI using multi-component data(Raknes and Arntsen, 2015)

Vp

Sequential Data difference

Vs

Sequential Data difference

1%

5%

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Example of 4D FWI using multi-component data(Raknes and Arntsen, 2015)

500 mVp Vs

575 mVp Vs

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Summary

I Full-waveform inversion(FWI) can be used to estimate changes in Vp and Vsfrom 4D multi-component ocean bottom data

I It is a technique well suited for LoFS data

I Can incorporate a large range of physical phenomena, including multiplereflections, P- and S-waves and anisotropy

I Minimal preprocessing of the data is necessary

I We still rely on conventional processing, such as reflection tomography andmigration velocity analysis in order to create a decent initial model

I In 4D applications, given good repeatability, the data difference strategy canquantify changes in elastic parameters down to 1% of the background model

I The dependence of the method on the quality of the background model is alsoreduced

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Plans for the future

I I have plans to start a group dedicated to imaging and inversion in theUniversity of Stavanger

I The group will lead to the training of students in processing andinterpretation of multi-component data

I We will focus on development of new methods to exploit all the informationcontained in this kind of data

I At the same time we will do our best to integrate geological knowledge, whichis the strength of our group, in the processing and interpretation of theseismic data

I At some point, I hope to have some cooperation with the industry, as I believethat field data is essential for testing the developed methodologies

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Bertrand, A., P. G. Folstad, B. Lyngnes, S. Buizard, H. Hoeber, N. Pham, S. de Pierrepont,J. Schultzen, and A. Grandi, 2014, Ekofisk life-of-field seismic: Operations and 4d processing:The Leading Edge, 33, 142–148.

Eriksrud, M., 2014, Seabed permanent reservoir monitoring (PRM) – A valid 4D seismictechnology for fields in the North Sea: First Break, 32, 67–73.

Fichtner, A., B. L. N. Kennett, H. Igel, and H.-P. Bunge, 2009, Full seismic waveformtomography for upper-mantle structure in the Australasian region using adjoint methods:Geophysical Journal International, 179, 1703–1725.

Ikelle, L. T., and L. Amundsen, 2005, Introduction to petroleum seismology: Society ofexploration geophysicists.

Raknes, E. B., and B. Arntsen, 2015, A numerical study of 3D elastic time-lapsefull-waveform inversion using multicomponent seismic data: GEOPHYSICS, 80, R303–R315.

Sirgue, L., O. Barkved, J. van Gestel, O. J. Askim, and J. H. Kommedal, 2009, 3D waveforminversion on Valhall Wide-azimuth OBC: Presented at the 71st Conference and Exhibition,EAGE, Expanded Abstracts, European Association of Geoscientists and Engineers.

Warner, M., A. Ratcliffe, T. Nangoo, J. Morgan, A. Umpleby, N. Shah, V. Vinje, I. Stekl, L.Guasch, C. Win, G. Conroy, and A. Bertrand, 2013, Anisotropic 3D full-waveform inversion:GEOPHYSICS, 78, R59–R80.

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