Advanced 3D imaging of complex geoelectrical structures using towed streamer EM data
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Michael S. Zhdanov, TechnoImaging and the University of Utah; Masashi Endo and David Sunwall*,TechnoImaging; Johan Mattsson, PGS Technology AB
SummaryThe towed streamer EM system makes it possible to collect
EM data with a high production rate and over very large
survey areas. At the same time, 3D inversion of towedstreamer EM data remains a very challenging problem
because of the huge number of transmitter positions of themoving towed streamer EM system, and, correspondingly,
the huge number of forward and inverse problems needed
to be solved for every transmitter position over the large
areas of the survey. We overcome this problem by
exploiting the fact that a towed streamer EM system's
sensitivity domain is significantly smaller than the area ofthe towed streamer EM survey. We have introduced theconcept of a moving sensitivity domain, originallydeveloped for airborne EM surveys, for interpretation ofmarine EM survey data as well, which makes it possible toinvert the entire towed streamer EM surveys with noapproximations into high-resolution 3D geoelectrical seabottom models. In order to improve the accuracy andreliability of the anisotropic 3D inversion results, we havedeveloped 3D inversion method, which takes into account:1) the variable background, 2) an a priori model
constructed by anisotropic 1D inversion results, seismic
data, and well-log data, and 3) bathymetry. We haveapplied our this method to the anisotropic 3D inversion of
towed streamer EM data from the Mariner field in theNorth Sea. The results show that our method can recover amore reliable and reasonable 3D geoelectrical model, andthe technology has proven to be fast and efficient for large
amounts of towed streamer EM data in a complex
geological setting.
Introduction
Marine controlled-source electromagnetic (MCSEM)
methods are widely used for off-shore hydrocarbon (HC)exploration. It has been demonstrated in a number ofpublications that the adequate interpretation of MCSEM
data requires taking into account the electrical anisotropy ofthe sea-bottom formations. In this paper, we demonstratethat our enhanced technology for the anisotropic 3D
inversion makes it possible to produce clear images of subsurface geoelectrical structure from the EM data collectedby towed streamer EM system, which is capable ofsimultaneous seismic and EM data acquisition. The current
generation of the towed streamer EM system consists of anelectric bipole transmitter towed at a depth of 10 m below
the sea surface, and up to a 9-km-long streamer of theelectric field receivers towed at a depth of approximately
100 m.

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The towed streamer EM system enables CSEM data to beacquired over very large areas in frontier and mature basins
for higher production rates and more cost effective thanconventional marine CSEM. At the same time, 3D
inversion of the towed streamer EM data becomes a very
challenging problem because the data are acquired overrelatively large areas with a huge number of moving towed
streamer EM system positions. We overcome this problem
by exploiting the concept of the moving sensitivity domain(Zhdanov, 2010; Zhdanov and Cox, 2012; Zhdanov et al.,2014; Cox and Zhdanov, 2014), which is implemented
using the integral equation (IE) method. In the frameworkof this concept, for a given transmitter-receiver pair, theresponses and Fréchet derivatives are computed from a 3D
earth model that encapsulates the towed EM system'ssensitivity domain. The Fréchet matrix for the entire 3D
earth model is then constructed as the superposition ofFréchet derivatives from all transmitter-receiver pairs overthe entire 3D earth model. As a major vehicle of theregularized inversion, we use the Re-weighted Regularized
Conjugate Gradient (RRCG) method (Zhdanov, 2002,2009) with adaptive regularization in order to minimize theTikhonov parametric functional.
In order to recover a reliable 3D geoelectrical model, thedeveloped method of 3D anisotropic inversion takes into
account: 1) a variable geoelectrical background, 2) anappropriate a priori model, and 3) the known bathymetry.
The variable background, which can vary for different
areas including both seawater and sea-bottom formations,helps extract more accurately the anomalous EM fields due
to geoelectrical inhomogeneities, especially in the case of alarge-scale survey. An appropriate a priori model canreduce the nonuniqueness of the inverse problem
drastically. The bathymetry should be taken into accountbecause it can distort the response of the sea-bottom
formations.
We have applied this method to the anisotropic 3D
inversion of towed streamer EM data acquired over theMariner heavy oil field located in the UK North Sea, block
9/11a.
Inversion of the towed streamer EM data
The problem of inversion of geophysical data can bedescribed as a solution of the corresponding operator
equation:where is a nonlinear forward modeling operator,
is atensor of the anomalous conductivities of the model
determined on some discretization grid:

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Advanced 3D Imaging using Towed Streamer EM Data

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Case Study – Mariner fieldand stands for a data vector formed by the components of
the observed EM field.
We use the integral equation (IE) method with variablebackground (Zhdanov, 2009) for solving the forward
modeling problem, which is based on the following integral
equation for an anisotropic medium:

whereis the background electric field, andis acorresponding Green’s tensor.
The inverse problem is ill-posed, i.e., the solution can be
non-unique and unstable. The conventional way of solving
ill-posed problems, according to regularization theory(Tikhonov and Arsenin, 1977; Zhdanov, 2002) is based onminimization of the Tikhonov parametric functional:

whereis a misfit functional defined as a weighted
norm of difference between the predicted and the observed
data,
is a stabilizing functional, andis aregularization parameter.
We solve the problem of minimization of the Tikhonov
parametric functional using the re-weighted regularized
conjugate gradient (RRCG) method with adaptive
regularization parameter selection (Zhdanov, 2002). Thismethod is based on iterative updates of the conductivity
model
as shown in the following formulas:

The Mariner field is located on the East Shetland Platformof the UK North Sea approximately 150 km east of theShetland Isles (Figure 1). The Mariner oil fielddevelopment entails investments of more than USD 7billion and is the largest new offshore development in theUK in more than a decade. It is expected to start production
in 2017. The field is estimated to produce for 30 years, withaverage production of around 55,000 barrels of oil per day
over the plateau period from 2017 to 2020. The Mariner
field consists of two shallow reservoirs, the Maureen
Formation and the Heimdal Sandstones of the Lista
Formation, with nearly 2 billion barrels of oil in place andexpected reserves of more than 250 million barrels of oil.
Both formations yield heavy oil of around 12 to 14 API.
The field will be developed with a production, drilling andquarters (PDQ) platform, based on a steel jacket, with 50
active well slots, and floating storage unit (FSU) of
850,000 bbls capacity. In addition, a jack-up rig will be
used for the first four to five years. Due to the low wellflow rates and early water break-through, there is a need formany wells and a process designed to handle large liquid
rates and oil-water emulsions. All production wells will
have stand-alone sand screens and electric submersible
pumps (ESPs) for lifting.

Figure 1: The location of the Mariner field in the North Sea.

whereis a length of the iteration step;
andarethe data and model weighting matrices;
is the gradientdirection, computed using the adjoint Fréchet derivative
matrix,, and the star denotes the adjoint operator(matrix).
The Fréchet derivative is the most expensive item in theinversion not only in terms of the computation time, butalso in the computer memory required for its storage. The
number of entries in the Fréchet derivative matrix is equal
to the number of data points times the number of cells inthe inversion domain. With large amounts of data and vast
inversion regions, the computer memory requirements may
become prohibitive. To reduce the storage requirements,
we use a moving sensitivity domain approach (Zhdanovand Cox, 2012; Zhdanov et al., 2014; Cox and Zhdanov,2014) in our towed streamer EM data inversion.

© 2015 SEG
SEG New Orleans Annual Meeting

The acquisition configuration and survey layout
The main features of the towed streamer EM configuration
for the Mariner survey are shown in Figure 2. The bipole
electric current source is 800 m long with a towing depth of
10 m. The source runs at 1500 A, and the source signal is aso-called Optimized Repeated Sequence (ORS) (Mattssonet al., 2012). In this case the useful frequencies range from
0.2 to 1.2 Hz with a step of 0.2 Hz. The signal sequence is
120 s long (one shot) with the source active during the first
100 s followed by 20 s of no signal, which is used forbackground noise estimation and noise reduction
processing. The survey consisted of 10 lines separated by
500 m, as shown in Figure 3. The length of each line wasabout 15 km. Each line recorded 60 shots of 120 s lengths.

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Advanced 3D Imaging using Towed Streamer EM Data

Figure 2: Geometry and towing configuration for the Marinersurvey.

Figure 3: A map of the Mariner area showing the acquired lines
and shot points.

3D anisotropic inversion of the towed streamer EM data
As demonstrated by Zhdanov et al. (2014), we expect thatthe use of a priori seismic and well-log information canreduce the nonuniqueness of the inverse problem andimprove the resistivity image as well. The seismic andwell-log information available in Mariner field were used
to estimate the geometric structure, i.e., the horizons of thechalk layer on top of the basement as well as the depths andhorizontal extents of the resistive region associated with thereservoirs, as shown in Figure 4. The chalk layer varies in
depth below sea surface from 1400 to 1500 m with abasement underneath. The Maureen reservoir sits on top ofthe chalk, whereas the Heimdal reservoir is about 200 m
above Maureen and the chalk. The bathymetry in thesurvey region varies between 95 and 115 m with arelatively homogeneous overburden.

Figure 4: Seismic structure information showing the chalk layer
and the two reservoirs close to the chalk.

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In order to construct an appropriate 3D geoelectrical model
which is used as the starting model in the anisotropic 3D
inversion, we deployed anisotropic 1D inversion for eachsurvey line. For all 1D inversions, the bathymetry in thesurvey area was taken into account, the response data atfive frequencies from 0.2 to 1.0 Hz with 18 offsets ranging
approximately from 1750 to 7450 m were used. The final apriori model was constructed using a 3D model from theresults of 1D inversions geometrically constrained byseismic and well-log information.
In the anisotropic 3D inversion of the data collected fromall the survey lines, the inversion domain was selected tocover all sensitive parts of the subsurface. The dimensionsof the inversion domain were selected as follows: from 12000 m to 16000 m in the x direction; from -3600 m to3600 m in the y direction; from 90 m to 3000 m in the zdirection (positive downward). This rectangular region wasdiscretized into cells of 50 m x 50 m x 25 m. The selected
data for the inversion consisted of 323 shots with 18 offsets(approximately from 1750 m to 7450 m) and fivefrequencies (0.2, 0.4, 0.6, 0.8, and 1.0 Hz). The run time on
a PC cluster with 16 cluster nodes, using 2.2 GHz Xenon
Westmere processors running 4 OpenMP threads each, wasabout 17 hours.
Figure 5 shows an example of the observed and predicteddata at a frequency of 0.2 Hz along a Line 105 presented ascommon mid-point (CMP) plots.

Figure 5: Observed (left panels) and predicted (right panels) datafor in-line electric fields at 0.2 Hz along Line 105 presented as
CMP plots.

The final model after 100 iterations in the anisotropic 3D
inversion on all ten lines at the same time and with aresulting misfit of 5.4 % is shown in Figure 6 through 9.
Figure 6 shows examples of the vertical cross sections of
the 3D resistivity models along Line 105 recovered fromthe anisotropic 3D inversion. Figure 7 presents examples of
the horizontal sections of the 3D resistivity models at adepth of 1425 m recovered from the anisotropic 3Dinversion. Figures 8 and 9 show 3D perspective views of
the 3D resistivity models recovered from the anisotropic
3D inversion of the towed streamer EM data for verticaland horizontal resistivities, respectively.
It can be seen that a resistive and anisotropic anomaly is
showing up on top of a resistive chalk/basement

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Advanced 3D Imaging using Towed Streamer EM Data

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even though this information was not exactly utilized in theanisotropic 3D inversion. Also, the recovered anisotropic
feature in the reservoir volume agrees well with the known
geoelectrical characteristics of Maureen and Heimdal
reservoirs.
Conclusions
We have developed an advanced method of 3D inversionof the towed streamer EM data in order to improve theaccuracy and reliability of the inversion results. The
method takes into account all available a priori geologicaland geophysical information about the survey area. We
applied this new technology to the anisotropic 3D inversionof the towed streamer EM data acquired over the Mariner
field. It has been demonstrated that the recovered resistive
and anisotropic anomaly volume agrees well with thestructural knowledge of the Heimdal and Maureen
reservoirs, with the known geoelectrical characteristics ofthe reservoirs, as well as the values for the underburden.
The anisotropic 3D inversion and imaging of the data fromthe current generation of the towed streamer EM
acquisition system can appropriately recover theanisotropichydrocarbon-bearing
formations.
The
developed inversion algorithm is based on the 3D
contraction integral equation method with variablebackground, and the moving sensitivity domain approach.
It has proven to be fast and efficient for a large towed
streamer EM dataset in a complex geological setting.

Figure 7: Horizontal sections of the 3D resistivity distributions at a
depth of 1425 m: (a) vertical resistivity, (b) horizontal resistivity,
recovered from anisotropic 3D inversion.

Figure 8: A 3D view of the 3D vertical resistivity distribution
recovered from anisotropic 3D inversion.

Figure 9: A 3D view of the 3D horizontal resistivity distribution
recovered from anisotropic 3D inversion.
Figure 6: Vertical cross sections of the 3D resistivity distributions
below survey line 105: (a) vertical resistivity, (b) horizontal
resistivity, recovered from anisotropic 3D inversion. The reservoir
locations are inside the red circles.

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SEG New Orleans Annual Meeting

AcknowledgementsThe authors acknowledge Consortium for Electromagnetic
Modeling and Inversion (CEMI), TechnoImaging, andPetroleum-Geo Services (PGS) for support of the researchand permission to publish the results of the 3D inversion ofthe towed streamer EM Mariner survey data.

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EDITED REFERENCES
Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2015
SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata foreach paper will achieve a high degree of linking to cited sources that appear on the Web.

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Cox, L. H., and M. S. Zhdanov, 2014, 3D airborne electromagnetic inversion using a hybrid edge-based
FE-IE method with moving sensitivity domain: 84th Annual International Meeting, SEG, Expanded
Abstracts, 739–744.
Mattsson, J., P. Lindqvist, R. Juhasz, and E. Bjornemo, 2012, Noise reduction and error analysis for atowed EM System: 82nd Annual International Meeting, SEG Expanded
Abstracts, http://dx.doi.org/10.1190/segam2012-0439.1.
Mattsson, J., M. S. Zhdanov, and M. Endo, 2014, 3D anisotropic inversion of towed streamer EM dataover the Mariner field in the North Sea: 76th Conference & Exhibition, EAGE, Extended
Abstracts, http://dx.doi.org/10.3997/2214-4609.20140731.
Tikhonov, A. N., and V. Y. Arsenin, 1977, Solutions of ill-posed problems: V. H. Winston & Sons.
Zhdanov, M. S., 2002, Geophysical inverse theory and regularization problems: Elsevier.
Zhdanov, M. S., 2009, Geophysical electromagnetic theory and methods: Elsevier.
Zhdanov, M. S., 2010, Electromagnetic geophysics: Notes from the past and the road ahead: Geophysics,
75, no. 5, 75A49–75A66, http://dx.doi.org/10.1190/1.3483901.
Zhdanov, M. S., and L. H. Cox, 2012, Method of subsurface imaging using superposition of sensor
sensitivities from geophysical data acquisition systems: U. S. Patent Application 2013/0173163.
Zhdanov, M. S., M. Endo, D. Yoon, C. Čuma, J. Mattsson, and J. Midgley, 2014, Anisotropic 3Dinversion of towed-streamer electromagnetic data: Case study from the Troll West oil province:
Interpretation, 2, no. 3, SH97–SH113, http://dx.doi.org/10.1190/INT-2013-0156.1.

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