geological parameters effecting controlled-source...

Geological Parameters Effecting Controlled-Source Electromagnetic Feasibility: A North Sea

Sand Reservoir Example Michelle Ellis and Robert Keirstead, RSI

Summary

Seismic and electromagnetic data measure very different

physical properties. To exploit these data to their fullest we

must understand how each data type is affected by sub-

surface properties. To this end we have investigated the

feasibility of using Controlled-Source Electromagnetic

(CSEM) and/or seismic surveying at a sand reservoir in the

North Sea. Different geological parameters such as fluid

composition, reservoir depth, anisotropy and porosity etc.

have been altered to determine which are important and

must be considered when considering a survey, designing

the survey and/or interpreting the results. Results show that

for this site both CSEM and seismic methods could be used

and that a combination of the two would be beneficial when

interpreting the site. It also shows the value of investigating

the effect of changing geophysical parameters. We also

develop a joint CSEM and seismic feasibility workflow for

the more general scenario.

Introduction

Controlled-Source Electromagnetic (CSEM) and seismic

methods can both be used to interpret sub-seafloor

sediments, however, they both measure very different

physical properties. CSEM is sensitive to the changes in

sediment resistivity often caused by the presence of

hydrocarbons (Eidesmo et al., 2002; Constable, 2010).

Seismic surveying uses the velocity contrast between

sedimentary layers to infer the presence of hydrocarbons.

To exploit these data to their fullest we must understand

how each data type is affected by sub-surface properties.

In this study, the effect of varying different geophysical

properties on the CSEM and seismic responses has been

investigated for a North Sea sand reservoir. The reservoir is

located approximately 2250 m below the sea floor in on

average 330 m of water. The overburden and basement

sediments consistent primarily of shale and sand with a few

coal layers below the reservoir. The investigation starts by

varying the fluid content in the reservoir and conducting a

CSEM feasibility study and AVO analysis for each

scenario. The results of these analysis show when seismic

or CSEM can be used alone to distinguish formations with

economic hydrocarbon reserves from those without and

when joint seismic and CSEM interpretation is required for

this type of geological setting. The study is then extended

to other geophysical parameters to investigate their effect

on the CSEM response.

Method

CSEM feasibility

CSEM feasibility studies are used to determine whether a

particular geological scenario/reservoir can be detected

using CSEM. A wet background model and target model

are developed from the resistivity log. The target model

may be derived from the in-situ resistivity, taken directly

from the log, or from the background model with

hydrocarbons added in to a particular sedimentary horizon.

The synthetic amplitude responses are then calculated for

both the background and target models over a range of

frequencies and source-receiver offsets (0.01-1 Hz and

0-20,000 m in the following examples). The percentage

difference is then calculated between the two models

(Figure 1). We use the following criterion: if a percentage

difference of 20% or more is obtained then the reservoir

may be detectable using CSEM.

AVO/AVA analysis

Amplitude vs. offset (AVO) and amplitude vs. angle

(AVA) analysis is a common method used to infer the

presence of hydrocarbons in reflection seismology. In this

study the well log is used to calculate full offset synthetics

using ray tracing for the background (wet) and target (in-

situ/hydrocarbon) models. The synthetics are generated for

all the models using elastic curves which have been

upscaled using a Backus average (12 m window). AVO

modeling is then constructed by extracting the trace value

from each model case synthetic gather at a specified time

Figure 1: 1D feasability study of a North Sea sand reservoir.

Percentage differences are calculated between the wet

background model and a 80 % hydrocarbon saturation target

model. White line presents the noise floor.

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Controlled-Source Electromagnetic Feasibility

(at the top of the reservoir,). The class of response indicates

whether there is potentially hydrocarbon in the reservoir.

Fluid substitution

In order to determine whether this type geological scenario

would benefit from joint seismic and CSEM surveying,

both the CSEM feasibility study and AVO analysis

preformed for various reservoir fluid contents. First, the

seismic velocities and electrical resistivities are calculated

using Gassmann’s and Archie’s equations for the wet, 10 %

gas (fizz gas), 80 % gas and 80 % oil models. Figure 2

shows a cross plot of the resistivities and p-wave velocities.

The wet model falls within the in-situ data cloud indicating

that this particular reservoir contains little to no

hydrocarbon. The change in velocity between the 10 % gas

and wet model is large enough to indicate that this type of

geology would show some response if the reservoir

contained some hydrocarbon but the change in velocity

between the 10 % gas, 80 % gas and 80 % oil is small. The

change in resistivity between the wet and 10 % gas case is

small whereas the change between the 80 % and 10% is

large. Because Archie’s equation does not differentiate

between gas and oil the 80 % gas and 80 % oil resistivities

are the same. The plot also shows that the coals, located

beneath the reservoir, have a similar resistivity to the 80 %

hydrocarbon models.

Figure 3 shows the results of the AVA analysis for each

fluid substitution scenario. As with the cross plot the results

show that the reservoir is wet. Had the reservoir contained

hydrocarbon is it uncertain whether a distinction could be

made between fizz gas and the 80 % oil and 80 % gas.

Figure 1 shows the feasibility study for the wet vs. 80 %

hydrocarbon case. The plot shows that the 20 % percentage

difference threshold is achieved and that if hydrocarbon

were presence in sufficient quantities then the CSEM could

resolve it. However, no CSEM response is observed

between the wet and in-situ resistivity models indicating

that at this particular location there is no hydrocarbon

present matching the results from the AVA analysis.

Geological parameter alterations

The results of the fluid substitution show that there is no

hydrocarbon present at this location within the reservoir.

However if hydrocarbon is present at other locations within

the reservoir CSEM could potentially resolve it. Seismic

reflection surveying could also detect the presence of

hydrocarbon but that it would have difficulty distinguishing

between low and high saturations. Therefore this location

would benefit from joint seismic and CSEM surveying.

However other factors other than hydrocarbon saturation

effect the CSEM response. Other geological parameters,

such as water depth reservoir, reservoir depth and

anisotropy, have been altered in the CSEM feasibility study

to observe their effects and to determine which of these

parameters must be taken into account.

Change water depth

Water depth is important when considering a CSEM

survey. At shallow depths the measured fields are

dominated by the airwave (Andréis and MacGregor, 2008).

Figure 4 shows the effect of changing the water depth for

this case study. It shows that as the water depth increases

the better the CSEM response but at all water depths the

target could potentially be resolved.

Change reservoir depth

As with most surface geophysical techniques CSEM loses

resolution with depth because the amount of reflected

energy decreases. Changing the reservoir depth at this

location was achieved by stretching or compressing the

Figure 3: Intercept reflectivity vs. AVA gradient for the insitu,

fizz, 80 % gas, 80% oil and wet models.

Figure 2: Cross plot of p-wave velcity and electrical resistivity

for the insitu, wet, 10 % gas, 80 % gas and 80% oil models.




thickness of the overburden sediments. Figure 4 shows the

effect of changing the reservoir depth. As expected the

shallower the target the lower the percentage difference

between the models. When the target is more than 2500 m

deep the percentage difference between the models is

below the 20 % threshold and CSEM is unable to resolve

the target.

Change reservoir thickness

The ability of CSEM to detect a target is controlled

partially by the contrast in resistivity of the target to its

surrounding sediments but also the target thickness. Fig. 4

shows the effect of changing the thickness of the target. As

the target thins the percentage change in model response

decreases until at 60 m the 20% threshold is reached.

Change anisotropy

When using the resistivity well log to develop the

background and target models we assume that the

sediments are isotropic. However it has been demonstrated

that overburden sediments, shale in particular, are often

anisotropic (Ellis et al., 2010). Figure 5 shows the effect of

including anisotropy throughout the sedimentary column.

As anisotropy is increased the change in percentage

difference between models decreases. CSEM is more

sensitive to the resistivities in the vertical direction. As

anisotropy is increased the vertical resistivity contrast

between the target sand sediments and the surrounding

shale sediments decreases. When maximum anisotropy

reaches 3, the maximum percentage difference is low, very

close to the noise floor and found at very high ranges.

Change porosity

In Figure 5 shows the change when the porosity of the

reservoir is altered. It shows that as the porosity is

decreased the difference between models increases and vice

versus. This is due to the increase in sediment resistivity as

porosity drops. It demonstrates that CSEM is not directly a

fluid indicator but rather a measure of resistivity and

Figure 4: CSEM feasabilities of a wet background and 80 % hydrocarbon. Top Row: changing water depth. Bottom Row changing reservoir depth.




resistivity contrast and changes in fluid are not the only

parameters that change sediment resistivity.

Conclusions

This study shows that for this particular dataset there is no

hydrocarbon present, however, if there were hydrocarbon

elsewhere in the reservoir a joint seismic and CSEM survey

would be able to resolve it. The CSEM feasibility studies

show the limits of CSEM surveying for this scenario. These

limits must be considered when interpreting data. Just

because CSEM can’t detect it doesn’t mean hydrocarbon is

not present, the saturation may to lower or the reservoir

thickness to thin. Also CSEM images resistivity not fluid

content and there are other parameters that alter the

resistivity of the sediments other than hydrocarbon such as

low porosity. This case study shows the value of

investigating more geophysical parameters other than just

fluid. We have therefore developed a 1D joint CSEM and

seismic feasibility workflow (Figure 6) for a more general

case. This example workflow can be used as a 1st step to

determine whether a site would benefit from joint CSEM

and seismic surveying.

Acknowledgements

The authors would like to thank RSI for permission to

publish this paper. This work has been partly supported by

the Well Integration, Seismic and Electromagnetic (WISE)

Consortium.

CSEM feasibility study

Fluid substitution

models

Review of results

CSEM feasibility study

Other geophysical parameter altered.

Calculate Synthetics

for fluid substitution model and conduct

AVO analysis

Review of results

CSEM feasibility

Joint 1D CSEM and seismic feasibility

GWLA on log data

Fluid substitution

Seismic feasibility

Determine whether

CSEM and seismic can resolve fluid content individually or jointly.

AVO analysis

Other geophysical parameter altered.

Review of results Review of results

Joint review of results.

Determine whether joint seismic and

CSEM surveying or a single survey would be

suitable.

3D modelling

Figure 6: Joint CSEM and seismic feasability work flow.

Figure 5: CSEM feasability studies of a wet background and 80 % hydrocarbon target. Top Row: changing reservoir thickness. Middle Row:

changing anisotropy. Bottom Row: changing reservoir porosity.



EDITED REFERENCES

Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2011

SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for

each paper will achieve a high degree of linking to cited sources that appear on the Web.

REFERENCES

Andréis, D., and L. M. MacGregor, 2008, Controlled-source electromagnetic sounding in shallow water:

principles and applications: Geophysics, 73, no. 1, F21–F32, doi:10.1190/1.2815721.

Constable, S., 2010, Ten years of marine CSEM for hydrocarbon exploration: Geophysics, 75, no 5,

75A67–75A81.

Eidesmo, T., S. Ellingsrud, L. M. MacGregor, S. Constable, M. C. Sinha, S. Johansen, F. N. Kong, and H.

Westerdahl, 2002, Sea bed logging (SBL): a new method for remote and direct identification of

hydrocarbon filled layers in deepwater areas: First Break, 20, 144–152.

Ellis, M. H., M. Sinha, and R. Parr, 2010, Role of fine-scale layering and grain alignment in the electrical

anisotropy of marine sediments: First Break, 28, 49–57.



http://dx.doi.org/10.1190/1.2815721

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