Full Tensor CSEM and MT for Subsalt Structural Imaging in the Red Sea Implications for Seismic and Electromagnetic Integration

M a r i n e a n d o f f s h o r e t e c h n o l o g y

436 The Leading Edge April 2013

SPECIAL SECTION: M a r i n e a n d o f f s h o r e t e c h n o l o g y

Full tensor CSEM and MT for subsalt structural imaging in the Red Sea: Implications for seismic and electromagnetic integration

The mapping of complex salt, subsalt sediments, and basement structures in the Red Sea was addressed through

the integration of coincident high-resolution wide-azimuth (WAZ) 3D seismic data, full tensor gravity gradiometry (FTG), and MT/CSEM techniques. The data acquisition was designed to enable advanced multi-geophysical integration schemes to image the subsalt geologic structures. Multi-offset, multicomponent electromagnetic measurements were recorded for both MT and CSEM with overlapping frequency bands. The analysis of the data and the preliminary 3D inversion results indicate that the two methods provide coherent information for the evaporitic overburden and the subsalt sediment sections. In addition, the MT data also show sensitivity to the deep basement structures. The obtained resistivity distributions strongly correlate with imaged seismic events from the 3D migrated seismic volumes and with the lateral- and vertical-velocity variations in the salt and presalt sections. These data suggest that the acquired electromagnetic data set can support the interval velocity-depth model building in areas of low seismic sensitivity such as the intrasalt and subsalt sections.

IntroductionThe base of salt represents a seal for potential hydrocarbon traps in the Red Sea, with subsalt tertiary clastic reservoirs deposited on top of deep basement structures. Salt in the Red Sea is far from being pure mobilized halite such as in the Gulf of Mexico. The evaporites are interbedded with clastic deposits as shown by drilling results on the Egyp-tian side (SPE Subsalt Workshop, 2012), and it is referred to as the layered evaporitic sequence (LES) in this paper. The LES typically starts at shallow depths below the water bottom and is heavily deformed by extensional tectonics in-ducing relocation and plastic deformation of the evaporitic sequence. The described geologic setting suggests the veloc-ity fields within the deformed LES sequence and in the pre-salt section can be fairly complex and laterally varying. As in other salt-related geology, seismic imaging is affected by various problems related to wave propagation, such as energy

DANIELE COLOMBO, GARY MCNEICE, ERNESTO SANDOVAL CURIEL, and ADAM FOX, Geophysics Technology, EXPEC Advanced Research Center, Saudi

Aramco

penetration, multiples, and scattering, which degrade the quality of the subsalt reflected signals. The high-velocity and laterally varying overburden also introduces uncertainties in the velocity estimation needed for accurate depth imaging. For these reasons, seismic velocity model building in salt ge-ology environments is typically supported by geologic inter-pretation, and by other complementary geophysical methods such as gravity and electromagnetics.

Electromagnetic (EM) techniques, especially in the form of magnetotellurics (MT), have played a role in recent years in the geophysical exploration in areas of complex geology. Extensive applications of MT technologies have been record-ed for sub-basalt, subsalt, and thrust belt exploration (Xiao and Unsworth, 2006; de Lugao et al., 2008). These geologic scenarios are all characterized by high-velocity/high-resistiv-ity overburden with deeper targets of lower-velocity/lower resistivity nature. The application of MT is straightforward in these cases as the natural and time-variant magnetic field (source of the MT signal) is preserved through resistive bod-ies (the decay of the magnetic-field amplitude is inversely pro-portional to the square root of the resistivity), thus providing information of the deep conductive sections. Seismic waves, as discussed, typically encounter problems when propagat-ing through shallow high-velocity rocks because of the large impedance contrasts and wave-scattering effects. MT and seismic techniques therefore play a complementary role in the exploration of geologic settings characterized by complex overburden. Their reciprocal integration has been shown to be valuable for improving velocity model building for seismic imaging (Christie et al., 2010; Colombo et al., 2012) or, as a minimum, to validate interpretative hypotheses.

Controlled-source EM (CSEM) is typically used in an exploration context for detecting variations in fluid composi-tion as a direct hydrocarbon identification method. The use of CSEM for imaging of structures is less common and only a few applications have been performed to date. Controlled-source audio magnetotelluric (CSAMT, the land equivalent of marine CSEM), on the other hand, has been widely em-ployed in the industry and academia over the last 30 years

Figure 1. Multiparameter 3D model developed for the Red Sea (N direction indicated by the red arrow).

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April 2013 The Leading Edge 437


the sea-bottom sediments, the LES (salt), the sub-LES clastic sequence (subsalt), and the basement (Figure 1). The top and bottom LES surfaces were obtained from the interpretation of a grid of depth-migrated 2D seismic lines followed by in-terpolation into a 3D volume.

The top of the basement surface was obtained from the seismic interpretation supported by aeromagnetic and aero-gravity data interpreted through forward modeling and/or inversion. Sediment layering above the LES was modeled conformably with the top LES surface, while the presalt clas-tic sequence was modeled conformably with the estimated basement topography. The bulk resistivity parameters for each layer were derived from the combined analysis and up-scaling of available well logs (10 wells distributed along the Red Sea coast). The interval velocity was derived mainly from depth-imaging velocity analysis. Density distributions were obtained from the imaging velocities converted using Gard-ner’s relation for the sedimentary section, and the evaporite’s density distributions were based on existing literature and other geologic considerations. The resulting model is a realis-tic representation of the 3D distribution of velocity, density and resistivity for a specific section of the Red Sea subject to the control of the seismic interpretation. The model was used for a number of modeling exercises during the planning of the subsequent multi-geophysics acquisition, and for testing and validating various geophysical techniques including mul-tiparameter joint inversion.

The 3D resistivity model was used to design the MT and CSEM acquisition by means of 3D finite-difference model-ing and inversion. Anisotropy of 1.5 (i.e., Rv/Rh) was in-troduced for the CSEM modeling study and station spacing varying between 2.5 and 3 km on a regular mesh was tested. The MT modeling used frequencies from 0.001 to 1 Hz, which represent a realistic range for the water depth involved (400–1500 m), and the geomagnetic latitude of the Red Sea (~40°N). The range of frequencies for the CSEM acquisition was determined through sensitivity studies, where for each

frequency the effects of removing the presalt sedimentary section were modeled in terms of amplitude and phase changes observed in the in-line Ex component. MT and CSEM modeling results both suggested sen-sitivity to the presalt sedimentary sec-tion, with MT providing a broad fre-quency response from about 0.05 Hz to about 0.003 Hz. CSEM frequen-cies of 0.1, 0.3 and 0.5 Hz showed the largest response in the Ex (inline) field component for source-receiver offsets greater than 6 km. Figure 2 shows the MT responses calculated at the locations displayed on the salt thickness map. The conductive presalt sediments are well represented in the MT responses as well as the response from the basement.

for imaging deep structures. The basic principle behind the CSAMT technique is that for specific transmission frequen-cies and at sufficient distances from the source, the EM fields can be approximated by plane waves (far field) and can be in-terpreted with the same techniques developed for their natural field equivalent MT (Zonge and Hughes, 1991; Unsworth et al., 2000). The marine environment provides the ideal condi-tions for obtaining dense CSEM measurements with a broad range of offsets and azimuths with limited additional effort when already acquiring MT data. The CSEM measurements, in this case, play a dual role of defining, with high resolution, the shallow structures with the near-offset/higher-frequency data and exploring the deep section (by overlapping with the MT frequencies) with the longer-offset/low-frequency data. The combination of CSEM and MT measurements therefore provides the means for exploring complex overburden and deep structures with scalable resolution and high data redun-dancy. The multiscale and comprehensive reconstruction of properties in the complex shallow overburden opens the road to the integration of seismic and electromagnetic techniques for improved velocity model building.

In the following sections, we describe how these concepts were applied to Saudi Aramco’s Red Sea subsalt exploration problem using a combination of broadband MT and wide-azimuth/long-offset CSEM measurements, the goal being a complete EM reconstruction of the geology from mudline to basement. We also explore the potential of combined EM technologies for addressing problems of seismic velocity model building and consequent seismic imaging in the con-text of subsalt exploration.

Presurvey modeling studiesA multidisciplinary team including interpreters, depth im-agers, and EM and potential field experts closely collabo-rated to define a 3D multiparameter geophysical model for the Red Sea based on offshore 2D seismic, wells, and other geological data. Four main geological units were considered:

Figure 2. Salt thickness map from the 3D Red Sea synthetic model with the grid of MT stations at 2.5-km spacing (a); MT TE and TM responses (0°N rotation) in the frequency range 0.001–1 Hz from selected soundings (b). The red arrows show the response of the presalt sediments on the apparent resistivity and phase curves. The profile (blue line) on the base map indicates the location of the 2D inversion tests crossing a section of the salt with thickness exceeding 6 km.

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Inversion tests were carried out along the profile shown in Figure 2, which crosses the thickest section of the LES complex with maximum thickness of the salt exceeding 6 km. 2D MT inversion was performed using both transverse electric (TE) and transverse magnetic (TM) polarization modes obtained with 0.0°N rotation of the impedance data in the frequency range of 0.001-1 Hz. The 2.5D CSEM inversion of the inline Ex com-ponent (assuming an acquisition direction along the profile) was performed for frequencies of 0.1, 0.3, 0.5 and 0.7 Hz, and using offsets from 1 to 20 km. For both inversions, the top LES surface is fixed as it is assumed to have been derived from the seismic interpretation. Results are shown in Figure 3 where the MT and CSEM (Rh) inversion results are compared (a fuller dis-cussion on Rh versus Rv sensitivity will follow).

Inversion results indicate that both the MT and the CSEM data can resolve the base of the LES sequence (i.e., top of the presalt sediments) with CSEM obtaining the highest resolution. The MT data set is the only data that resolve the geometry of the basement. The above results suggest that the combination of broadband MT and long-offset/low-frequency CSEM would be able to help in deep subsalt exploration in the Red Sea.

MT/CSEM data setMT and CSEM acquisition was carried out in the summer of 2011 for three survey blocks using the parameters detailed in Table 1. The area occupied by the combined MT and CSEM acquisition co-incides with the full fold area of the wide azimuth

(WAZ) seismic acquisition and of the full tensor gravity gradiometry (FTG) acquisition. As a result, the volume of each WAZ block is equally sampled by three dense and high-resolution geophysical data sets, which provides a unique op-portunity for multi-geophysics data integration. At the time of the data acquisition, the survey was reported to be the world’s largest combined marine EM (MT+CSEM) project ever undertaken.

EM receivers were deployed on the sea floor in a stag-gered grid with a 3-km nominal offset between stations. Each block was acquired by performing multiple deployments of the receiver patches to allow acquisition of offsets of more than 30 km in the inline direction, and 12 km in the cross-line direction perpendicular to the source (i.e., four azimuthal

Figure 3. Inversion results for the synthetic MT (a) and CSEM (b) data sets along the profile shown in Figure 2. Both inversions resolve the base of salt, while only MT resolves the basement (black lines are used to indicate from top to bottom: top LES, base of salt and top of basement surfaces).

Item Specification

Blocks 3Surface Average of 2000 km2 per blockNumber of stations 650Spacing 3 km (staggered grid)Sea-bottom recording time Up to 20 days of continuous recordingInline offset (max) 25 kmCrossline offset (max) 12 km (four azimuthal lines)Measured components Ex, Ey, Bx, ByCSEM transmitted frequency 0.2 HzCSEM harmonics 0.4, 0.6, 0.8, 1.4, 2.0 Hz

Table 1. Acquisition parameters for the combined MT/CSEM survey in the Red Sea.

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receiver lines actively recording on each side of the source lines). Large broadside (i.e., crossline) offsets enabled the acquisition of electromagnetic fields more sensitive to the horizontal resistivity components than that provided by the inline data. This enables a better discrimination of electrical anisotro-py (Newman et al., 2010) and enhances the sensitivity to the conductive subsalt section.

The recorded MT and CSEM data were of good to exceptional quality and achieved usable offsets in CSEM exceeding 20 km (Table 2 and Figure 4). A preliminary look at the recorded CSEM data suggests that features such as submarine canyons and mountains in-troduce noticeable distortions in the recorded fields. This ob-servation underlies the need for an appropriate representation of the sea-bottom topography during the data interpretation/inversion phases. The first four frequencies (0.2, 0.4, 0.6, and 0.8) had good signal-to-noise at offsets of 25 km or more, while the higher harmonics (1.4 and 2.0 Hz) show a variable behavior, but in many cases also achieving reliable measure-ments exceeding 20 km of offset. The MVO plots in Figure 4 also show a remarkable level of signal for the Ey and Hx components that should be absent for 1D and 2D geology

symmetric to the source orientation. This behavior reflects the 3D nature of the subsurface.

MT time series were processed using robust remote refer-ence methods (Larsen et al., 1996). For each deployment, the EM receivers remained on the sea floor for a total average time of about 12 days and up to 20 days in a few cases, where both CSEM and natural magnetotelluric signals were record-ed (Figure 5). This extended deployment period allowed for the processing of long time-series windows for the estimation of the magnetotelluric impedance tensor components. Typi-cally, the whole available data set was utilized for the process-ing including the periods where the transmitter was active. Up to four decades of usable MT impedance estimates were

Figure 4. Normalized magnitude versus offset (MVO) plots at one receiver: fundamental transmission frequency and recorded harmonics for the inline Ex component (a); the four components of the recorded signal—Ex, Ey, Hx, and Hy (b). The cusp observed in the curves at 14,000 m offset is caused by a deep sea-bottom canyon.

Frequency (Hz) Electric data (km) Magnetic data (km)

0.2 Up to 25 km Up to 25 km0.4 Up to 25 km Up to 25 km0.6 Up to 25 km Up to 25 km0.8 Up to 4 to 25 km Up to 4 to 25 km1.4 Up to 2 to 20 km Up to 2 to 20 km2.0 Up to 2 to 16 km Up to 2 to 16 km

Table 2. Approximate offset ranges where data were of satisfactory quality for standard processed data.

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obtained for most of the soundings (Figure 6). The obtained MT data show remarkable similarity with the soundings modeled with the 3D synthetic model (Figure 2), confirming the reliability of the Red Sea multiparameter model.

The combined CSEM and MT data sets provide a high degree of redundancy in terms of the range of acquired fre-quencies with the high-resolution CSEM data set covering the higher-frequency spectrum of the EM signal and MT ex-tending the low-frequency content by three decades. CSEM and MT data overlap for approximately one decade of the EM spectrum: from 0.2 to 2.0 Hz. In this frequency range, and at a sufficient distance from the source, the CSEM signal can be approximated by plane waves (i.e., far field) behaving similarly to the natural MT signal.

We calculate the CSEM impedance tensor component Zxy (Ex and Hy signal components) at various offsets along one acquisition line and analyze their behavior at different frequencies by plotting the derived apparent resistivity and phase curves (Figure 7). The transi-tion from the near-field to the far-field behavior is marked by stable estimates of the apparent resistivity and phase (i.e., flat response). For the CSEM acquired frequencies of 0.4 to 2.0 Hz, the transition occurs between 1.5 and 2.5 km of offset with the higher frequencies reaching the far-field conditions earlier than the lower frequencies. The lowest acquired fre-quency (0.2 Hz), however, struggles to reach a stable impedance esti-mate, suggesting that the far-field (i.e., plane wave) conditions would require larger offsets. The large offsets acquired for the inline and broadside directions in the Red Sea survey en-able the estimation of the horizontal electromagnetic impedance tensor components (Zxy and Zyx), for both CSEM and MT data, for the over-lapping frequency band of the two data sets. Because of the overlapping frequencies and the similar propaga-tion behavior in the far field, the MT and CSEM data will be sensitive, to a large degree, to the same geological features.

EM data analysisIn the following sections, we analyze the acquired EM data and derive preliminary interpretations on the geologic significance of the observed patterns. For brevity, we focus our analysis on only one of the three blocks that were acquired. A visu-al examination of the data quality

Figure 5. Spectrogram of a sea-bottom EM receiver showing the signal variation over a period of 20 days. Notice the diurnal variations of the MT signal, the variations of solar activity over the recording period, and the spectral lines at 0.2 Hz and higher harmonics from the horizontal electric dipole (HED) transmitter.

Figure 6. MT soundings processed with robust reference methods and using extra-long time series (i.e., typically more than 12 days of continuous recording of MT and CSEM signals). Nearly four complete decades of usable MT impedance data are recovered.

throughout the survey area is achieved by calculating pseu-dosections for the MT data set, where the frequency-de-pendent apparent resistivity and phase for each polarization mode (i.e., transverse magnetic = TM and transverse electric = TE) are represented in attribute cubes. Given the diffusive nature of the EM signal, the apparent resistivity and phase attributes should show consistent smooth variations in the space and frequency dimensions. Likewise, CSEM pseudo-sections, or attribute maps, are typically obtained for each frequency, offset and component by representing normalized amplitude and phase values plotted at the midpoint between source and receiver. Visual inspection of the attribute maps is used for data QC purposes and for deriving preliminary qualitative interpretations of the geology before undergoing a rigorous 3D inversion process. Figure 8 shows the compar-ison between the horizontal slices of the MT apparent resis-tivity attribute at 0.1 Hz (invariant mode) compared to the CSEM normalized amplitude of the inline Ex component

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for an offset of 2.0 km and the broadside Ey component at 4.0 km offset at a frequency of 0.2 Hz. Both the MT and the CSEM methods show sensitivity to the same geology for a comparable frequency content of the signal. It should be not-ed that the MT data at this frequency display a 1D behavior (Figure 6) so that the TM and TE polarizations models are equal and their average (invariant mode) is a good represen-tation of the impedance behavior at this frequency. For sim-ilar reasons, the inline Ex and the broadside Ey components show sensitivity to the same geology and match the MT response. The dense spatial sampling of the CSEM source provides a detailed map of the lithologic variations, as can be seen in Figure 8.

The high sensitivity to the geology and the wide overlap in responses of the MT and CSEM data provide the means for reconstructing the whole 3D geoelectric structure in these Red Sea survey areas starting from the internal variations in the salt overburden, through the targeted subsalt sedimentary section, to the basement. These properties can then be used to refine the seismic velocity estimates, especially in areas that lack coherent seismic reflectivity, such as in the salt overbur-den or the deeper subsalt section. In the next paragraphs we explore the aspects of seismic-electromagnetic integration for velocity model building through single-domain inversions of the MT and CSEM data.

3D EM inversionMT and CSEM inversions were carried out starting from structural seismic constraints as derived from the 3D prestack depth migration (PSDM) of the WAZ data. The coincidence of the MT/CSEM acquisition with the full-fold area of the

WAZ enabled the definition of the top LES surface by means of seismic interpretation in the depth domain. The top LES represents the shallowest interface creating a major contrast in seismic velocity and resistivity and, for this reason, any prior information related to its geometry is of great relevance in performing 3D EM inversions. The starting model for both the MT and CSEM data inversions was a so-called salt flood model, meaning that the resistivity below the top LES interface was set to a constant resistivity value representing the resistivity of salt. An average water resistivity of 0.176 ohm.m (as measured during the acquisition) was used, while a resistivity of 0.5 ohm.m was assigned to the sediments from the sea bottom to the top LES. The resistivity of the half space below the top LES was selected by trial and error to generate the smallest initial misfit in the inverted data. All the cells in the inversion model were allowed to vary during the inversion except for the resistivity of the sea water.

The 3D MT inversion is based on a finite-difference for-ward modeling algorithm for EM field simulation (Alum-baugh et al., 1996) using a nonlinear conjugate gradient (NLCG) for minimization of the data misfit functional (Newman and Alumbaugh, 2000). To optimize computa-tional efficiency, the method uses separate grids for the mod-el parameter description and for the computational meshes used in the forward modeling. The computational meshes are optimized in size for each inverted MT frequency. Key to this approach is a proper material averaging scheme to map the conductivity parameters of the geological model to the computational grids used for the field solution in the forward problem (Commer and Newman, 2008). The MT inversion used 218 receivers with 13 frequencies spanning from 0.001

Figure 7. Apparent resistivity (a) and phase (b) calculations for inline measurements (Ex and Hy) at increasing offsets and for representative recorded frequencies. The transition from the near-field to the far-field behavior (i.e., plane waves) of the EM fields is marked by stable estimates of apparent resistivity and phase versus offset.

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to 1 Hz. The inversion model was parameterized with ap-proximately 7.7 million cells, which measure 200 × 200 m (x-y) in the core area with depth intervals of 40 m for the first km of the model and then gradually increasing up to 250 m at the depth of 10,000 m. Forward calculation mesh-es were optimized for each inverted frequency, obtaining 13 computational meshes (i.e., one per frequency) consisting of a minimum of 0.7 million cells to a maximum of 2 million cells for the lowest/highest frequencies used. The initial salt flood model built using the interpreted top LES was set to a resistivity of 50 ohm.m. The 3D MT inversion was complet-ed in 155 iterations, achieving a final rms misfit of 2.37. A snapshot of the 3D MT inversion result is shown in Figure 9 where the inverted resistivity cube is sliced in two vertical cross sections and corendered with the 3D PSDM seismic section migrated to 8 km depth. Localized modifications of the interpreted boundary between sea-floor sediments and the top of the LES were introduced by the MT inversion, indicating that the sharp shallow interface used for depth imaging might require additional refinement. The resistivi-ty of the LES was also modified by the MT inversion with areas of high-resistivity values associated to more halite con-centrations. A deep conductor was introduced consistently throughout the model below the LES sequence. The transi-tion between the resistive overburden and the deep conduc-tive package coincides with a clear reflection visible on the migrated seismic volume. This can be associated, in first anal-ysis, to the major lithological variation between the LES and the sedimentary section underneath. The conductive package is further bounded at depth by some poorly resolved high-re-sistive feature that is interpreted to represent the geoelectric expression of the basement.

The CSEM inversion methodology is based on a 3D regular mesh model representation of the conductivity structure where a finite difference time-domain approach (Maaø, 2007) is used to solve the Maxwell’s equations in the reciprocal receiver-source domain. The L2-norm data misfit is

inverted by using a quasi-Newton inversion algorithm based on the BFGS method (Byrd et al., 1995). The fundamental transmission frequency plus two harmonics (i.e., 0.2, 0.4 and 0.6 Hz) were used in the 3D CSEM inversion for all four measured field components (i.e., Ex, Ey, Hx, Hy). Inline data up to 20 km offset and data relative to four broadside lines (i.e., maximum broadside offset available) were also selected for the inversion. As per the 3D MT inversion, a salt flood model from the interpreted top LES was used as the starting model and the initial salt resistivity was set to 100 ohm.m. The model was parameterized using a regular mesh of 300 × 300 × 96 m in the x, y, z model dimensions respectively, for a total of 9.5 million cells used in the inversion. The CSEM in-version was performed for both the horizontal resistivity (Rh) and for the vertical resistivity (Rv) assuming vertical trans-verse isotropy (VTI) for the conductivity structure, with the two components uncoupled during the inversion. The initial model used for the inversion was assumed to be isotropic (Rh = Rv). The 3D CSEM inversion was completed in 135 itera-tions, achieving a final rms misfit of 3.94.

Figure 10 shows the results of the 3D CSEM inversion compared to the MT inversion along the cross section A-A’, superimposed on the depth-migrated seismic. The 3D MT and CSEM Rh inversions show consistent structures, and both show sensitivity to a conductor located below the base of the salt. The base of the resistive layer is consistent with a clear seismic reflector imaged via PSDM. The MT inversion addi-tionally shows the presence of a poorly resolved deep resistor, which is interpreted to be the expression of the basement. The obtained results are in line with the conclusions from the presurvey modeling study, which indicated sensitivity for both MT and CSEM to the conductive subsalt sediments and only MT sensitivity to the basement structures. The MT and CSEM (Rh) inversion results also show consistency with the velocity field used for the 3D PSDM, which was derived independently from the EM data interpretation. The CSEM Rv inversion results show little or no sensitivity to

Figure 8. Snapshot of the data distribution (MT and CSEM): MT apparent resistivity invariant mode at 0.1 Hz (a); inline Ex normalized amplitude at 2.0 km offset (b) and broadside Ey component at 4.0 km offset (c) at 0.2 Hz. The reference seismic depth slice is arbitrarily taken at 2000 m depth; the NW-SE lines represent the CSEM acquisition lines.

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the conductive subsalt sediments, indicating that the verti-cal current flow is obstructed by the thick resistive salt. The layered nature of the LES introduces large anisotropy in the geoelectric structure with Rv much bigger than Rh (notice the different color scales used in Figure 10 for the CSEM Rh and Rv inversion results). The first-order anisotropy in the LES is likely generated by the layered sequences of evaporites and clastic sediments (shales and/or sands) rather than by in-trinsic rock properties. Evidence of such layered composition for the evaporite sequence was found in wells drilled on the African side of the Red Sea in Egypt by Hess (SPE Subsalt Workshop, 2012). Additional aspects related to the CSEM Rv inversion are the introduction of vertically shaped con-ductors that typically do not appear in both the MT and the CSEM Rh inversions. The interpretation of these features is discussed in the next section.

Considerations for seismic-EM integrationThe primary objective for multi-geophysics acquisition in the Red Sea was the fusion of different geophysical meth-ods to de-risk the subsalt exploration. A typical workflow for achieving this task consists of interpretation-driven forward modeling performed to confirm interpretative hypothesis. This standard approach has limitations and does not fully ex-ploit the information available in multi- geophysics data sets. A more ambitious objective sought in the Red Sea was the analytical and quantitative integration of different geophysi-cal methods (i.e., seismic, gravity and EM in our case) to improve the overburden velocity estimates for seismic depth imaging. Approaches such as simultaneous joint inversion (Colombo and De Stefano, 2007; Tartaras et al., 2011) of multi-geophysics data address this problem and can become a practical method for velocity model building when struc-tural or petrophysical relations can be established among the different geophysical parameters. It is therefore impera-tive to assess the quality, the resolution and the correlation of information from various geophysical methods before em-barking in any multi-geophysics joint inversion study. With this in mind we analyze the potential of the acquired data set

Figure 10. Comparison of 3D MT inversion (a), 3D CSEM inversions Rh (b) and Rv (c) and the interval depth-velocity model from PSDM (d) along profile A-A’. The background seismic section is depth-migrated to 8 km.

Figure 9. 3D MT inversion results. The seismic volume is depth-migrated to 8 km.

for achieving effective seismic and EM integration for veloc-ity model building.

Multi-geophysical integration, whether performed with an interpretative and interactive workflow or by means of more sophisticated and analytical joint inversion schemes, requires the parameters (in this case velocity and resistivity) to be correlated. Based on what has been observed to date for the Red Sea, we can conclude that the horizontal resistivity (Rh) variations describe the main geologic units, as would be expected from the interval velocity field. The resistivity and velocity parameter distributions, as derived from inde-pendent data sets and by different inversion methodologies, display a coherent spatial correlation or in other words a

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common structure. This property, translated into the minimi-zation of the cross gradient function of the model parameter distributions, can be exploited in a joint inversion scheme for inferring velocity from resistivity.

Two important challenges exist in velocity model build-ing for subsalt exploration of the Red Sea; these consist of the definition of the top LES interface (typically done by means of geologic interpretation), and of the definition of the in-ternal lateral and vertical velocity variations within, and at the base of, the evaporitic overburden. The top LES interface represents a shallow major discontinuity for both the velocity and the resistivity parameters and accurate recovery of the velocity distribution around this shallow transition has pro-found effects on the imaging capabilities at depth. A bright reflection is typically used to distinguish the top LES interface, and the geologic interpretation of this interface is used for build-ing the velocity models for PSDM (i.e., salt flooding). In many areas of the 3D surveys, especially where layered minibasins are present, the boundary is difficult to identify, as it is not marked by a bright reflection. In such conditions, the cross analysis of the resistivity variations from the MT inversion, and from the high-er-resolution CSEM inversion, can lead to a better description of the velocity distribution across the top LES interface.

Estimation of how velocity var-ies in the evaporitic overburden is challenging when using seismic data alone due to the general lack of coher-ent intrasalt reflections, and because of low signal/noise subsalt reflected events. In such cases, the migrated image gathers are difficult to analyze for residual curvature, making the ve-locity estimation process problemat-ic. Figure 11 shows a horizontal slice cut through the resistivity and seis-mic interval velocity cubes at 2000 m depth (i.e., within the LES), with the seismic depth image corendered with the resistivity and velocity parameter distributions. As observed in the cross sections of Figure 10, the resistivity distribution from MT inversion and the Rh distribution from CSEM in-version are strongly correlated to each other, indicating sensitivity to similar geologic features. The correlation of the reconstructed resistivity patterns, with the independently derived in-terval velocity field from PSDM, also shows strong similarities. This evidence supports the concept of seis-mic-electromagnetic integration for the definition of migration velocities in the evaporitic overburden.

The analysis of the reconstructed vertical resistivity (Rv) distribution presents some interpretation chal-

lenges. As observed in the vertical cross sections, the distri-bution of Rv is different and uncorrelated to the other three parameter distributions shown in Figure 11. We can observe, however, the presence of conductors that bound and de-lineate the areas of higher resistivity and higher velocity in the seismic slice. These lateral variations can be interpreted as lithologic changes within the evaporitic complex, such as the transition between evaporitic sequences interbedded with clastic sediments and predominantly halite bodies. The conductive halo surrounding the high-resistivity features in the CSEM (Rv) inversion could be explained by a combina-tion of phenomena related to possible salt dissolution, which decreases the overall bulk resistivity of the rock formations around the halite bodies, or by the structural setup of the layered evaporites (believed to be highly anisotropic) when they subvertically onlap onto the massive halite bodies. The layered evaporite complex displays VTI anisotropic behav-ior with Rv>>Rh (�zz>>�xx, �yy) when horizontally stratified. For this reason, the horizontally layered evaporites typically appear as conductors on the MT and CSEM (Rh) inver-sions, in response to the propagation of horizontal currents and as resistors to the propagation of vertical currents in the CSEM (Rv) inversion. When subvertically stratified, the LES

Figure 11. Comparison between PSDM seismic at 2000 m (below sea level) with: 3D MT inversion (a), 3D CSEM inversion (b, d) and the interval velocity model used for PSDM (c); NW-SE lines represent the CSEM acquisition directions and points indicate the location of EM receivers.

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anisotropy becomes HTI (horizontal transverse isotropy) with Rh>>Rv (�xx, or �yy>>�zz). This would explain the ap-pearance of the vertical conductors bounding the halite bod-ies in the CSEM (Rv) inversions. The above considerations would lead to the conclusion that more complex electrical anisotropy models such as tilted transverse isotropy (TTI) in-volving the off-diagonal components of the conductivity ten-sor should be invoked to explain the propagation of CSEM fields in structured and anisotropic geology.

Conclusions and path forwardWide-azimuth seismic, FTG, MT, and CSEM data were ac-quired over three exploration blocks in the Red Sea. The dif-ferent types of geophysical data are colocated and acquired using high-resolution acquisition parameters to provide a unique opportunity for the application of multi-geophysical data integration techniques targeting primarily the improved interval velocity-depth model building for 3D PSDM. The electromagnetic data set, in particular, shows a high degree of correlation with the predominant geological features and with the independently derived PSDM interval velocity-depth model.

Both the MT and CSEM data sets show sensitivity via 3D inversion to the targeted subsalt section, and to the internal lithologic variations within the evaporitic complex. Strong anisotropy is observed in the layered evaporite sequence, which can be used as an additional attribute to discriminate lateral lithologic variations through the analysis of the Rv distribution. Additionally, the MT data show sensitivity and correlation to the deep basement features predicted by the geologic models. The overlap in frequency of the MT and CSEM data sets, and the similarity of propagation mode for long-offset CSEM signals, suggest that the combination of the CSEM and MT inversions (or their simultaneous joint inversion) would be ideal to obtain a complete 3D geoelectric model for the top LES sequence, the subsalt sedimentary sec-tion and the deep basement structure.

The initial 3D-inversion results, and the analysis carried out in the multi-geophysical domains, suggest that an effec-tive integration of the different data sets can be used to help build the interval velocity model in the depth domain. Evi-dence suggests that a proper integration of CSEM and MT data with 3D seismic has the potential to help refine the velocity distribution at the boundary between the sea-floor sediments and the top LES, the lateral velocity changes in the evaporitic complex related to lithologic variations (pure halite concentrations versus other evaporitic sequences interbedded with sediments), and the definition of the deep subsalt sedi-mentary section. Given the observed commonality of struc-ture between resistivity and velocity distributions, an effective integration of the multi-geophysical data sets can be achieved through the use of simultaneous joint inversion schemes, where the minimization of the cross-gradient functional is used to impose structural constraints. The lack of wells and of petrophysical information on velocity and resistivity for the Red Sea suggests that weak cross-domain constraints, such as the cross-gradients incorporated in a multiparameter joint

inversion procedure, would be the most suitable approach to integrate into the velocity estimations through the use of elec-tromagnetic data.

We believe that the data set acquired in the Red Sea is only starting to show the potential of multi-geophysical data inte-gration for subsalt exploration applications. The high qual-ity and dense spatial sampling of the coincident geophysical methods (WAZ seismic, MT, CSEM and FTG) make the ac-quired data sets an ideal test case to study and develop quan-titative integration schemes such as 3D joint inversion. The complexity of the geologic setting also provides the opportu-nity for analysis of electrical anisotropy on CSEM measure-ments. The large overlap of CSEM and MT measurements, in terms of frequency content and the commonality of prop-agation modes at large offsets, will enable the development of robust joint CSEM and MT interpretation schemes for exploration in complex and structured geologic areas.

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Acknowledgments: The work presented in this article was made possible only with the valued contribution of many individuals in Saudi Aramco and through the collabora-tion with service providers. We thank Saudi Aramco’s Exploration and EXPEC ARC management for allowing the publication of the results of this study. We also thank our colleagues Emad Muzaiyen and Daniel Franken for their contribution in building the 3D Red Sea multiparameter synthetic model, and Valmore Celis Franco for work on the PSDM migration velocities. Among the external collaborators, we thank are Michael Commer and Greg Newman (Lawrence Berkeley National Laborato-ries), EMGS, and Don Watts and Federico Ceci (WesternGeco).

Corresponding author: [email protected]

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Documents

Full Tensor CSEM and MT for Subsalt Structural Imaging in the Red Sea Implications for Seismic and Electromagnetic Integration