Determination of reservoir properties from the integration of CSEM and seismic data

Determination of reservoir properties from the integration of CSEM and seismic data Peter Harris, 1 Rock Solid Images, and Lucy MacGregor, 2 Offshore Hydrocarbons Mapping, discuss the advantages in reservoir interpretation of integrating data from controlled source electromagnetic (CSEM) and seismic data, illustrated by an example from the Nuggets-1 gas reservoir in the UK Northern North Sea. T he problem of remote characterization of reservoir properties is of significant economic importance to the hydrocarbon industry. For example, in exploration the ability to determine the gas saturation in an identified prospect would avoid the costly drilling of un-economic low saturation accumulations. During development and production, a detailed knowledge of the reservoir properties and geometry, and changes in these parameters through time, can aid optimization of well placement and enhance overall recovery rates. A range of geophysical techniques can be applied to this problem. Seismic data are commonly used to develop geological models of structure and stratigraphy. Amplitude variation with offset (AVO) and inversion for acoustic and elastic impedance may also be used to constrain reservoir properties such as elastic moduli and density. These can in turn be related to mineralogy, porosity, and fluid properties through rock physics relationships (for example, Mavko et al., 1998). However seismic data alone in many situations cannot give a complete picture of the reservoir. Ambiguities exist, for example, in AVO responses which may be caused either by fluid or lithological variations, and cannot be separated on the basis of the seismic data alone. The controlled source electromagnetic (CSEM) method is becoming widely used in the offshore hydrocarbon industry, and has been applied successfully in a variety of settings (see for example, Srnka et al., 2006; MacGregor et al., 2006; Moser et al., 2006). The CSEM method uses a high powered horizontal electric dipole to transmit a low frequency electromagnetic signal through the seafloor to an array of multi-component electromagnetic receivers. Variations in the received signal as the source is towed through the array of receivers are interpreted to provide the bulk electrical resistivity of the seafloor, through a combination of forward modelling, geophysical inversion, and imaging. The bulk resistivity of a porous rock is to a large degree controlled by the properties and distribution of fluids within it. Typical brine saturated sediments have a resistivity in the range 1-5 Ωm. Replacing the seawater with resistive hydrocarbon can result in an increase in the bulk resistivity of the formation by 1-2 orders of magnitude. CSEM sounding exploits this dramatic change in physical properties to distinguish water bearing formations from those containing hydrocarbons. However, as for seismic data, potential ambiguities exist in the interpretation of CSEM data. For example, tight limestones, volcanics, or salt bodies may also have high resistivity, and could give a CSEM response similar to that of a hydrocarbon reservoir. In addition, because of the diffusive nature of electromagnetic fields in the earth, the structural resolution is generally lower than that given by seismic data. Since the CSEM and seismic data are controlled by very different physical processes, it is clear that a careful combination of seismic and CSEM data, exploiting the strengths of each, can supply information which is not available or is unreliable from either type of data alone, thus reducing ambiguity and risk. A number of approaches to the integration of disparate data types have been proposed (e.g. Musil et al., 2003; Gallardo & Meju., 2004; Hoverston et al., 2006). Here we illustrate the advantages of an integrated interpretation using CSEM and seismic data collected on the Nuggets-1 gas reservoir. Nuggets-1 field The Nuggets-1 gas field lies in the UK sector of the Northern North Sea. The reservoir lies in the Eocene Frigg formation at approximately 1550 m below seafloor (Figure 1) in 115 m of water. The gas sands are characterized in the well log data by a high resistivity, low density region with a thickness of about 24 m. A CSEM dataset was collected on the Nuggets- 1 reservoir in 2005 (MacGregor et al., 2006). The results of constrained inversion of the CSEM data, co-rendered with the coincident seismic data, are also shown in Figure 1. The lateral extent of the reservoir is resolved well, although the vertical resolution of the reservoir is lower than that achievable with seismic data. Rock physics relationships Key to combining these complementary sources of information is the development of a common rock physics 1 p.harris@rocksolidimages.com. 2 lucy.macgregor@ohmsurveys.com. 2006 EAGE 15

first break volume 24, November 2006 Figure 1 Results of the 2005 CSEM survey of the Nuggets-1 reservoir co-rendered with coincident seismic data. The high resistivity red regions correspond to the gas filled reservoir, embedded in the low resistivity blue background. Also shown are acoustic impedance (AI) and resistivity logs from a well close to the centre of seismic/csem line. The gas sand has intermediate AI in the well, between low impedance shales above and high impedance brine sands below. Seismic data are shown by courtesy of TGS-Nopec. Figure 2 On the left we show the P wave velocity measured in the well against the value predicted using Faust s equation. The colour coding shows the shale fraction. The clean sands (blue points) have a predicted velocity which is slightly too high, and the velocity in the shales is predicted even less accurately. On the right, the prediction uses the Faust alternative published by Hacikoylu et al. (2006). This accounts for the presence of clay minerals in the pore space, thus improving the quality of the prediction in the shaley part of the section, however the velocity in the sands is not so well predicted as with Faust. 16 2006 EAGE

Figure 3 On the left we show the resistivity measured in the well against the value predicted using Archie s equation. The colour coding shows the shale fraction. There is a large scatter of points and most are located in the lower left of the point cloud, suggesting that the prediction is not very accurate. This is not surprising since Archie s equation is intended for use in clean sands (the blue points). On the right, the prediction uses the Waxman-Smits model. This accounts for the presence of clay minerals in the pore space, thus improving the quality of the prediction. In both cases the shallow silty section (turquoise points) is anomalous. model linking both electromagnetic and elastic properties to the underlying rock and fluid properties. There are two different approaches to formulating and using rock physics models. The first approach is to relate seismic and electrical rock properties directly allowing, for example, the prediction of seismic velocity from a logged resistivity value. Such relationships may be used as soft constraints in a joint inversion of electromagnetic and seismic data to couple the different physical models. However, such an approach may bias the solution obtained from the inversion if the relationship between contrasting physical parameters is not well described by the chosen model. An alternative is to use these relationships to compare directly the seismic and electromagnetic models derived from independent inversions. The areas where the relationships between parameters fail are often those of most interest since they may indicate subsurface areas having anomalous properties. A well-known example of these relationships is the Faust equation, relating P wave velocity and resistivity. We have found that an alternative to the Faust equation published by Hacikoylu et al. (2006) is more satisfactory since it includes the influence of shales. Figure 2 compares predictions of Vp from resistivity using both Faust and the alternative in the Nuggets-1 well. It would be preferable to use both models, with Faust applied in the clean sands and the alternative elsewhere. However, in order to do this, we must then identify the lithologies correctly across the extent of the reservoir, away from the well. The second approach addresses the more fundamental need for rock physics relationships. Our aim is to use electromagnetic and seismic data to constrain the physical properties of a reservoir, and to do this a common rock property model, from which both electric and elastic properties can be predicted, must be developed (Greer, 2001). We use the well data to choose appropriate rock physics models and to calibrate them. For example, for the sensitivity modelling described in the next section we used Hertz-Mindlin theory to model the dry rock matrix, a homogeneous fluid mixing model, and Gassmann s equations to include the fluid in the rock matrix. The end result are seismic rock properties (Vp, Vs, and density), depending on the porosity and fluid saturation, both of which were varied in the modelling, and on the mineral and fluid properties which were kept fixed after calibration with well data. The electromagnetic properties may be modelled in a similar way. Archie s equation is often used to predict resistivity from reservoir properties. It is important to bear in mind that it only holds for clean sands. The electrical properties of clay minerals are notoriously complicated, and their presence in pore space may change the effective rock conductivity significantly. Figure 3 illustrates this. We first compare the resistivity predicted from Archie s equation with the logged resistivity values. It is clear that the prediction is rather poor and there is a wide scatter of points and no linear relationship between actual and predicted values even for the clean sands. The same com- 2006 EAGE 17

first break volume 24, November 2006 parison using the Waxman-Smits model, which accounts for clay minerals in the pore space, shows a more linear trend, albeit with some scatter and some anomalous regions in the crossplot. Waxman-Smits appears to be suitable for dispersed clays. Both models produce the same prediction in the clean sands, since without clay Waxmann-Smits reduces to the Archie formula. In some North Sea fields the clays tend to have a structural component. In such cases the Indonesia model (Poupon and Leveaux, 1971) may give a better prediction than Waxman-Smits. Rock physics modelling In the Nuggets study, the main parameter of interest is the gas saturation in the reservoir. The sensitivity of acoustic and electric properties to this parameter can be investigated using rock physics modelling (Figure 4). The previous section described the method used to calculate the seismic properties of the rock, in this case acoustic impedance and elastic impedance (at 30 incidence angle) as they vary with gas saturation and porosity. Aki and Richards (1980) three term approximation was used in calculating the elastic impedance. For this sensitivity modelling, we are interested only in the clean sands in the reservoir. Archie s equation is therefore adequate for calculating resistivity. We conclude from the sensitivity modelling that, for the Nuggets-1 reservoir, the AI response varies most rapidly with porosity and is only weakly dependent on gas saturation. In the areas of good reservoir (higher gas saturation and moderate to good porosity), the EI is a very flat surface indicating that it contains little information about either reservoir property. On the other hand, the resistivity is controlled almost entirely by the gas saturation. In this particular case, we can combine the AI and CSEM data to obtain both porosity and saturation estimates. Of course this conclusion is reservoir dependent, and in other cases we may find different combinations of elastic and electro-magnetic data the most useful. Nonetheless, here we have demonstrated that a reserves-inplace estimate from combined CSEM and seismic data offers greater reliability than would be obtained from either data type alone. Figure 4 (Top) Acoustic impedance (AI) is plotted for a range of porosities and water saturations. The gas saturation is one minus water saturation. The contours of the AI are more or less vertical in the region of good reservoir (upper right of the plot), showing that AI in this well is largely sensitive to porosity and not at all to gas saturation. (Middle) Elastic impedance for the same range of porosity and saturation. The surface is rather flat, showing that EI carries little information about either porosity or saturation. (Bottom) Resistivity for the same models. The contours here are almost horizontal, showing that in this case resistivity is primarily sensitive to saturation rather than porosity. Integrated interpretation In the well, the gas sand stands out very clearly as a high resistivity, low density layer about 24 m thick (Figure 1). Figure 5 shows a comparison between surface-derived properties and those logged in the well. The left log shows the comparison between relative acoustic impendence derived from inversion of the stacked seismic data and the log. The P wave velocity of the gas sand falls between the lower velocity of the overlying shales and the higher velocity of the underlying brine sands. The presence of gas in the reservoir lowers its density, resulting in a moderate AVO effect. Since the relative acoustic impedance shown in Figure 5 is calculated from stacked 18 2006 EAGE

Figure 5 Comparison of surface derived and well log acoustic impedence and resistivity. The gas sands are characterised by regions of high resistivity and low acoustic impedence in the surface data. The lower resolution of the CSEM data is clear (red line): results from the CSEM inversion show a broad region of elevated resistivity. However the reservoir equivalent resistivity (green line) shows good agreement with the well log. data which includes this effect, rather than from a normal incidence trace, we have a low relative impedance from the surface seismic data even though the acoustic impedance of the gas sand in the well is higher than that of the overlying shales. The right hand log in Figure 5 compares a vertical section through the centre of the CSEM-derived resistivity section, with the logged resistivity values. The peak resistivity recovered by the inversion is lower than that logged in the well, and the lower resolution of the diffusive CSEM method is clear: the resistivity anomaly is smeared about its true depth. However, the transverse resistance of the reservoir (resistivity thickness product) is well constrained (MacGregor et al., 2006). The equivalent reservoir resistivity can be recovered by integrating through the zone of high resistivity, and mapping this value back into the known (and seismically resolvable) thickness of the reservoir. The resulting resistivity value is close to that observed in the well log. A crossplot of relative AI at the well trace with the resistivity resulting from inversion of the CSEM data at the location of the well, colour coded by well log resistivity, shows that the gas sand has low AI and high resistivity in the surface data (Figure 5). Comparing this with the well log data demonstrates that, using the surface data, the gas sand can be well delimited. This, coupled with careful rock physics analysis, allows us to achieve our aim, at least in a semi-quantitative sense. Calibration at the well allows us to combine the surface data and produce a gas saturation attribute (Figure 6). This is semi-quantitative: blue values represent high gas saturations and reds represent low values. The scatter of red points away from the reservoir formation is an indication of the uncertainty in the calibration process. It is clear that the CSEM data have good lateral resolution, marking the edges of the gas-bearing zones quite precisely. Vertical resolution is controlled largely by the seismic resolution. For this study, we used an empirical calibration between reservoir properties and the surface data. There are many possible approaches to this calibration, including linear and nonlinear regressions, artificial neural network based methods, and geostatistical approaches. Equally well, we may invert the rock physics models to obtain, for example, gas saturation and porosity from the CSEM-derived resistivity and the 2006 EAGE 19

first break volume 24, November 2006 seismically-derived impedances. In all cases it is essential to ensure that careful processing is performed on the seismic, CSEM, and well data to optimize signal to noise ratio, and that robust methods are used to minimize sensitivity of the result to remaining data noise. Discussion and conclusions The key to the integration of electromagnetic and seismic data lies in the rock physics. There is extensive experience in selecting appropriate models for relating reservoir properties to elastic rock properties. This study shows that the electromagnetic rock properties have to be modelled with equal care. In particular, neglecting the presence of clay minerals in the pore space by uncritical use of Archie s equation may produce erroneous results. For this particular study, sensitivity analysis showed that the combination of acoustic impedance and CSEM data was the most useful in constraining the reservoir properties of interest. This conclusion is not universal, and indeed we have found a combination of elastic impedance and CSEM data more suitable elsewhere in the North Sea. The sensitivity modelling is therefore essential to select the most informative data combinations to achieve the desired result. Here we illustrated the sensitivities of acoustic and electrical rock properties to variations in reservoir properties. An extension of this to examine the sensitivity of synthetic surface data to reservoir properties is also useful in determining optimum acquisition strategies and parameters pre-survey, and optimizing processing flows post-survey. This modelling includes overburden effects on both seismic and EM data, tuning effects on AVO in the reservoir, and other propagation phenomena. This study has highlighted the exciting possibilities that arise from the combination of data from multiple remote sensing methods, each of which measure different properties of the earth. With proper understanding, these can be combined to predict the rock and fluid properties of the subsurface. Importantly, the work has shown the ability to move towards quantitative measurement of important reservoir properties like gas saturation from remote sensing data, giving reservoir engineers unambiguous data which will better allow them to harvest hydrocarbons from the earth. Work is ongoing on developing and understanding relationships between these varied remote sensing measurements and the underlying rock physics. We believe that this has the potential to add significant value to the exploration and production industries. Figure 6 Semi-quantitative estimate of gas saturation superimposed on the seismic wiggle traces. Blue colours represent high gas saturation and reds are low saturation. The uncoloured regions are outside the bounds considered anomalous for resistivity or impedance. 20 2006 EAGE

Acknowledgements The seismic data are shown by courtesy of TGS-Nopec. The authors would like to acknowledge the support of the ITF, and thank BP, Total, ENI, and Shell for their support of the Nuggets CSEM project. We would also like to thank Total for access to the Nuggets-1 field, and their assistance in operating there. References Aki, K. and Richards, P.G. [1980] Quantitative Seismology. W.H. Freeman. Hacikoylu, P., Dvorkin, J., and Mavko, G. [2006] Resistivityvelocity transforms revisited. The Leading Edge, 25, 1006-1009. Hoverston, 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, 71, C1-C13. Gallardo, L.A. and Meju, M.A. [2004] Joint two-dimensional DC resistivity and seismic travel time inversion with cross gradient constraints. J. Geophys. Res., 109, 1-11 Greer, A. [2001] Joint interpretation of electromagnetic and seismic data: Investigating zero age crust. PhD thesis, University of Cambridge. MacGregor, L., Andreis, D., Tomlinson, J., and Barker, N. [2006] Controlled source electromagnetic imaging on the Nuggets-1 reservoir. The Leading Edge, 25, 984-992. Mavko, G., Mukerjii, T., and Dvorkin, J. [1998] The Rock Physics Handbook: Tools for seismic analysis in porous media. Cambridge University Press. Moser, J., Poupon, M., Meyer, H., Wojcik, C., Rosenquist, M., Adejonwo, A., and Smit, D. [2006] Integration of electromagnetic and seismic data to assess residual gas risk in the toe thrust belt of the deepwater Niger delta. The Leading Edge, 25, 977-982. Musil, M., Maurer, H.R., and Green, A.G. [2003] Discrete tomography and joint inversion for loosely connected or unconnected physical properties; Application to crosshole seismic and georadar data sets. Geophys. J. Int., 153, 389-402. Poupon, A. and Leveaux, J. [1971] Evaluation of water saturations in shaley formations. Trans. Soc. Prof. Well Log Analysts. 12 th Annual Logging Symposium, Paper O. Srnka, L., Carazzone, J., Ephron, M., and Eriksen, E. [2006] Remote reservoir resistivity mapping, The Leading Edge, 25, 972-975. AD 2006 EAGE 21