Exploring with Controlled Source Electro- Magnetic (CSEM) methods: from 2D profiling to 3D multi-azimuth surveying

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1 Exploring with Controlled Source Electro- Magnetic (CSEM) methods: from 2D profiling to 3D multi-azimuth surveying M. Darnet, P. Van Der Sman, R.E. Plessix, J.L. Johnson, M. Rosenquist ( 1 ) ( 1 ) Shell International Exploration and Production Summary Subsurface resistivity mapping based on Controlled Source Electromagnetic (CSEM) measurements is an attractive technology for exploration as it offers the possibility to distinguish between hydrocarbon and brine bearing prospects where conventional seismic methods prove inconclusive. In Shell, we have applied the CSEM method on a worldwide scale since 2003 to both de-risking and portfolio polarization. Early on in the development of the CSEM technique some compelling results were obtained with single 2D profiles over prospects. Unfortunately, the lack of subsurface coverage of this type of acquisition often leads to ambiguous results because the Earth rarely satisfies the 2D assumption at the scale of the CSEM experiment. To reduce such ambiguities, we have focused efforts on the development of 3D processing and inversion capabilities as well as interpretation workflows that take the complexity of the Earth into account. In this paper, we share some of our motivations behind our approach and illustrate its effectiveness with both real and synthetic data examples. The early days of CSEM surveying: from 2D profiling to 3D surveying Since the resistivity of reservoir rock is directly related to the type of pore fluids and their saturations, CSEM offers the possibility to distinguish economic hydrocarbon accumulations from other scenarios. Traditionally, this is done by acquiring CSEM data along a single 2D profile over a prospective area (e.g. Moser et al., 2006). Despite a few compelling examples (Johansen et al., 2005), it quickly became clear that the CSEM technique, because of its large depth of investigation, is not just sensitive to the elevated resistivity of the hydrocarbon-bearing prospect (e.g. Constable, 2006). The interpretation of CSEM data therefore requires to take into account other resistive formations present in its surroundings such as unexpected hydrocarbon accumulations (e.g. shallow gas, gas hydrates, stacked pays) or simply other lithologies (e.g. salt, volcanics, carbonates, marls). This can be done by modeling multiple geological scenarios in 3D and assess which ones are the most likely (e.g. Green et al., 2005, Moser et al., 2006). This process however is very labor- and time-consuming and often only leads to a qualitative interpretation of the data. The CSEM survey acquired offshore Malaysia (Darnet et al., 2007) nicely illustrates this problem. Here, hydrates were the complicating factor (figure 1).

2 a) c) b) Figure 1: Cross-section (a) and shallow depth slice (b) through the 3D conductivity model manually built to interpret a CSEM dataset acquired offshore Malaysia (after Darnet et al., 2007) c) Observed (top) and modelled (bottom) normalized CSEM response at 0.25 Hz and 5.5 km offset for various geological scenarios used to interpret qualitatively the CSEM data (for more details, please refer to Darnet et al., 2007). To allow for a more quantitative interpretation as well as reduce both turn-around time and labor cost, we internally developed an efficient 3D inversion algorithm based on the minimization of a cost function between the synthetic and actual data (Plessix and Mulder, 2007). In addition to providing a much more refined resistivity image of the subsurface in a reasonable timeframe, this approach also ensures the final resistivity model to be compatible with all available input data (multiple source frequencies, offsets, azimuths etc). This integration capacity is especially important when dealing with large datasets, for which manual quality control of the data misfits is prohibitively time-consuming. We applied our inversion workflow to the aforementioned Malaysian dataset and obtained the resistivity distribution of figure 2 in a few hours. Although the resolution of the resistivity image is much lower than presented in figure 1b, the recovered resistivity values are more reliable as the resistivity model now explains all measurements quantitatively. Moreover, these fast turn-around times make possible a sensitivity test on for instance starting model and inversion parameters improving the robustness of the final inversion result. For this particular dataset, it showed for instance that our confidence in the resistivity estimates at the target level is low as a result of the heterogeneity of the shallow subsurface.

3 Figure 2: Cross section through the conductivity model obtained after 3D inversion of the CSEM data presented on figure 1. The black wiggles correspond to the seismic reflectivity data. The age of maturity: 3D multi-azimuth CSEM surveying A natural solution to reduce the aforementioned uncertainty of the final CSEM resulting from complex resistivity structures, is to improve the sampling of the CSEM data. Given the 3D nature of shallow resistivity variations (such as on figure 1) and the incremental cost of 3D surveying versus 2D profiling, acquiring CSEM data in a 3D mode is an attractive option (e.g. Carazzone et al., 2005, Gabrielsen et al., 2009). Unfortunately, as the CSEM source is directional, 3D CSEM acquisition is not just a simple extrapolation of the 2D problem into 3D. One important requirement is that electrical anisotropy of the Earth is taken into account (e.g. Løseth et al., 2007, Jing et al., 2008). One way to do so is by acquiring azimuth-rich 3D CSEM data (e.g. Lu and Xia, 2007, Jing et al., 2008). Let us illustrate this aspect with a synthetic example inspired from the previous Malaysian case. Figure 3 shows the anisotropic resistivity model used to generate the synthetic data. As for the real case, a shallow, gas hydrate layer overlays a deep hydrocarbon bearing reservoir. The acquisition geometry is a grid of receivers at 2 km grid spacing and source lines with 1 km cross-line and 2 km in-line spacing. We further assume that all receivers are live when the source is emitting and thus build up a multiazimuth data set. After unconstrained inversion of these synthetic data, both the shallow hydrates and the deep hydrocarbon bearing reservoir are recovered on the vertical resistivity model (figure 4). They are however absent on the horizontal resistivity panel, suggesting a low sensitivity of this particular acquisition setup to thin resistive layers. The other interesting feature is that the vertical resistivity of the shallow subsurface is so accurately mapped that the presence of the deep hydrocarbon related resistive anomaly is no longer questionable. This example illustrates that in addition to the higher spatial resolution, multi-azimuth 3D acquisition also has the potential to significantly reduce the uncertainty in the final CSEM results for complex resistivity structures when compared to the traditional 2D mode. Figure 3: Top: Cross section through the horizontal (left) and vertical (right) conductivity model used to generate synthetic data Bottom: Depth section at the hydrocarbon reservoir depth (left) and at the hydrates

4 depth (right) through the vertical conductivity model. The black dots and gray lines represent the CSEM receiver and source line locations, respectively. Figure 4: Top: Cross section through the horizontal (left) and vertical (right) conductivity model after inversion of the synthetic data Bottom: Depth section at the hydrocarbon reservoir depth (left) and at the hydrates depth (right) through the vertical conductivity model after inversion. The black dots and gray lines represent the CSEM receiver and source line locations, respectively. Conclusions and future directions The previous synthetic example shows that even though 3D multi-azimuth acquisition provides both higher resolution and more robust resistivity estimates of the subsurface than conventional 2D profiling, the physics of the CSEM is still such that the spatial resolution of the resistivity images (especially vertically) remains low when compared to results from seismic imaging (e.g. top right of figure 4). Therefore, some uncertainties will remain with respect to the actual origin of the resistivity anomaly(ies) at the target level. One possible solution to overcome this limitation is by incorporating additional constraints (e.g. seismic or petrophysical ones) into the inversion process (e.g. Hansen and Mittet, 2009, Brevik et al., 2009). We believe this aspect is crucial in arriving at more reliable results. However, this is not straightforward as changes in elastic properties do not necessarily correspond to changes in resistivity and vice-versa. Moreover, it is this imaging hurdle that needs to be resolved before considering a quantitative interpretation of the resistivity image in terms of hydrocarbon presence, for instance through joint seismic/csem interpretation (e.g. Hovertsen et al. 2006, Harris et al., 2009). Most of the recent developments on the CSEM technology were motivated by the need to better handle the complexity of the Earth (especially its higher dimensionality). A further important learning was that the Earth electrical structure is strongly anisotropic and therefore that 3D multi-azimuth acquisitions as well as 3D anisotropic inversions needed to be implemented. Another aspect that has been largely neglected thus far is the fact that the Earth resistivity is also frequency dependent (e.g. Veeken et al., 2009). Could that be the next layer of complexity that needs to be considered?

5 Acknowledgments The authors wish to acknowledge Dirk Smit and John Voon for their support as well as Yip-Cheong Kok, David Ramirez Mejia, Liam Ó Súilleabháin, Johannes Singer, Chris Shen, Quintijn Van De Laarschot and Femke Vossepoel for their invaluable input. References Brevik I., Gabrielsen P.T., and J.P. Morten, 2009, The role of EM rock physics and seismic data in integrated 3D CSEM data analysis, 79th Annual International Meeting, SEG, Expanded Abstracts Carazzone J. J., O. M. Burtz, K. E. Green, and D. A. Pavlov, C. Xia, 2005, Three Dimensional Imaging of Marine CSEM Data, SEG Expanded Abstracts 24, 575; doi: / Constable, S., 2006, Marine electromagnetic methods A new tool for offshore Exploration, The Leading Edge, 25, Darnet, M., M.C.K. Choo, R.E. Plessix, M.L. Rosenquist, K.Y. Cheong, E. Sims, and J.W.K. Voon, 2007, Detecting hydrocarbon reservoirs from CSEM data in complex settings: Application to deepwater Sabah, Malaysia, Geophysics, v. 72, no. 2, doi: / Gabrielsen P. T., I. Brevik, R. Mittet and L. O. Løseth, 2009, Investigating the exploration potential for 3D CSEM using a calibration survey over the Troll Field, first break, vol. 27, Green, K. E., O. M. Burtz, L. A. Wahrmund, C. Xia, G. Zelewski, T. Clee, I. Gallegos, A. A. Martinez, M. J. Stiver, C. M. Rodriguez, and J. Zhang, 2005, R3M case studies: Detecting reservoir resistivity in complex settings: 75th Annual International Meeting, SEG, Expanded Abstracts, Hansen K.R. and R. Mittet, 2009, Incorporating seismic horizons in inversion of CSEM data, 79th Annual International Meeting, SEG, Expanded Abstracts Harris P., Du Z., MacGregor L., Olsen W., Shu R., and R. Cooper, 2009, Joint interpretation of seismic and CSEM data using well log constraints: an example from the Luva Field, first break, vol. 27, Hoversten G.M., Cassassuce F., Gasperikova E., Newman G.A., Chen J., Rubin Y., Hou Z., and Vasco D., 2006, Direct reservoir parameter estimation using joint inversion of marine seismic AVA and CSEM data: Geophysics, Vol. 71, p. C1 B13 Jing C., K. Green, and D. Willen, 2008, CSEM inversion: Impact of anisotropy, data coverage, and initial models, 78th Annual International Meeting, SEG, Expanded Abstracts, Johansen, S.E., Amundsen, H.E.F., Røsten, T., Ellingsrud, S., Eidesmo, T. and Bhuyian, A.H., 2005, Subsurface hydrocarbons detected by electromagnetic sounding. First Break, 23(3), Løseth, L.O., Ursin, B. and Amundsen, L., 2007, On the effects of anisotropy in marine CSEM. EAGE 69th Conference & Exhibition, Extended Abstracts, D034. Lu, X. and Xia, C., 2007, Understanding anisotropy in marine CSEM data. 77th SEG Annual Conference, Expanded Abstracts, Moser J., M. Poupon, H.J. Meyer, C. Wojcik and M. Rosenquist, 2006, Integration of electromagnetic and seismic data to assess residual gas risk in the toe-thrust belt of deepwater Niger Delta, The Leading Edge; August 2006; v. 25; no. 8; p ; doi: / Plessix R.E. and Mulder W.A., 2008, Resistivity imaging with controlled-source electromagnetic data: depth and data weighting, Inverse Problems, 24, (22pp), doi: / /24/3/ Veeken P., P. Legeydo, I. Pesterev, Y. Davidenko, E. Kudryavceva and S. Ivanov, 2009, Geoelectric modelling with separation between electromagnetic and induced polarization field components, First Break, vol. 27.