2011 SEG SEG San Antonio 2011 Annual Meeting 771. Summary. Method

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1 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 subsurface 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. 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 ( 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. 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. 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. 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 (insitu/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 SEG San Antonio 2011 Annual Meeting 771

2 Controlled-Source Electromagnetic Feasibility (at the top of the reservoir,). The class of response indicates whether there is potentially hydrocarbon in the reservoir. 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 Figure 3: Intercept reflectivity vs. AVA gradient for the insitu, fizz, 80 % gas, 80% oil and wet models. 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 Figure 2: Cross plot of p-wave velcity and electrical resistivity for the insitu, wet, 10 % gas, 80 % gas and 80% oil models. 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 SEG San Antonio 2011 Annual Meeting 772

3 Controlled-Source Electromagnetic Feasibility 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. SEG San Antonio 2011 Annual Meeting 773

4 Controlled-Source Electromagnetic Feasibility 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. resistivity contrast and changes in fluid are not the only parameters that change sediment resistivity. Joint 1D CSEM and seismic feasibility Conclusions GWLA on log data 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 Seismic feasibility CSEM feasibility study Calculate Synthetics for fluid substitution model and conduct AVO analysis models CSEM feasibility study Other geophysical parameter altered. Determine whether CSEM and seismic can resolve fluid content individually or jointly. AVO analysis Other geophysical parameter altered. 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. SEG San Antonio 2011 Annual Meeting 774

5 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: / 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, 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, SEG San Antonio 2011 Annual Meeting 775