Istanbul International Geophysical Conference and Oil & Gas Exhibition, Istanbul,Turkey, 17-19 September 2012.

A case study showing how integration of high-end data processing and geological information has led to improved imaging of the North Zeit Bay, Egypt. Kate McCluskey*(1), Andrew J. Davidoff (2), Olivier Hermant (1), Mark McCluskey (1), Ehab Madkor(1). (1) CGGVeritas, (2) Vegas Oil Summary The target for any contractor when reprocessing seismic data is simply to obtain improved resolution and imaging using the latest technologies. This in turn leads to a more accurate interpretation of existing and future hydrocarbon reservoirs in the fields. In this case study we present how incorporating high end processing techniques in conjunction with geological information can help overcome processing challenges presented by the data. Introduction In 2003 Vegas Oil & Gas S.A and East Zeit Petroleum Company acquired 409km2 of data in 2003 across the North West Gemsa and North Zeit Bay Concessions which are located to the west of the Gulf of Suez, Egypt (Figure 1). Since then the seismic data has been reprocessed several times in an attempt to resolve the imaging problems intrinsic to the dataset arising from acquisition issues and geological effects.

The geology of North Zeit Bay is composed of an asymmetric NE-SW graben known as the Wadi Dara. The graben is bound by the Esh El Mallah outcrop to the southwest and the Gabel El Zeit to the northeast. Right lateral transform faults associated with the Morgan Accommodation Zone bound the basin in the North (Figure 2). Major structures and graben formations are associated with the Gulf of Suez rifting and are filled with a typical succession of late Cretaceous through to late Miocene sediments. Several oil fields are known to exist in the area. Reservoir sections are in the Kareem formation below the South Gharib and Belayim evaporate sections. These bedded evaporates produce severe multiples and a significant velocity inversion which make imaging of the reservoir section and underlying geology difficult. Well and surface geology information were utilized to provide a robust refraction statics solution, whilst adaptive ground roll attenuation and Monte-Carlo residual statics led to improved data continuity. The increased resolution obtained with the use of these methods allowed 5D interpolation technologies to compensate for inconsistent fold and varying noise levels. Controlled Beam Migration, non-linear slope tomography for the pre-stack depth migration velocity model building, well information analysis and accurate structural interpretation were all essential for determining the shape and velocity of the evaporate layer. This combined approach led to improved continuity of the reflectors and better definition of faulted areas. Adaptive Ground Roll Attenuation Adaptive ground roll attenuation is a data driven method which performs 3D adaptive ground-roll and guided wave attenuation as presented by Le Meur et al (2010). This technique was particularly well suited to the dataset as it worked effectively with the irregular surface geology of the survey, even in the areas of outcropping anhydrite seen in the north east of the bounding fault (Figure 3), in areas of varying acquisition parameters (receiver line spacing went from 400 to 200m) and regions of poor geophone coupling.

Figure 1: Aerial view of the survey.

Figure 2: Cross section of the regional geology.

Istanbul International Geophysical Conference and Oil & Gas Exhibition, Istanbul,Turkey, 17-19 September 2012.

Refraction Statics After data preconditioning, and prior to first break picking an initial velocity model was built using available geological and auxiliary data. This initial model was used as a guide for the automatic first break picking step for all shot and receiver locations across the survey. In order to compute a full refraction statics solution first arrival travel time tomography was used. The generation of the initial geological model also allowed the inversion to quickly converge on a stable solution whilst avoiding bad results due to local minima issues. Another very important aspect of any inversion is the quality of the pick the algorithm is trying to model. The model driven automatic first break picking was extensively QC’ed via interactive applications. Raw and

modeled picks were compared to an interpretation of the well velocities at the well locations (Figure 4) to ensure the resulting two layer tomographic refraction statics solution was as accurate as possible. The resulting velocity model was found to honor the geological variations across the survey (Figure 5).

Figure 3: Stacks showing the adaptive ground roll attenuation process.

Figure 4: Comparison of well information to first break picks at a control point. Note the divergence of the picks owing to structurally induced azimuthally varying delay times.

Figure 5: Depth slice through the refraction statics solution velocity model at the inline shown.

Istanbul International Geophysical Conference and Oil & Gas Exhibition, Istanbul,Turkey, 17-19 September 2012.

Residual Statics A Monte-Carlo residual statics computation was used to remove the shorter wavelength statics prevalent in the data. This method, as presented by Le Meur et al. (2011), uses a non-linear approach (Simulated Annealing) in one pass to compute the hundreds of simulations required to converge on the final solution. This is a machine intensive program requiring High Performance Computing techniques which allowed us to optimize and minimize the data access time and therefore speed up the efficiency of the non-linear inversion.

5D Signal Enhancement

The application of a noise suppression option of a 5D coherency driven interpolation algorithm helped overcome noise levels in the data which are typically associated with low fold or poor geophone coupling as described by Poole et al. (2011). By working along inline, crossline, time, offset and azimuth we better preserve structure, AVA and AVAz. Regularization of the data to a denser, regular target geometry allowed wide azimuth (common offset vector) processing prior to

imaging which in turn diminishes migration noise. This multi-dimensional approach to regularization and noise attenuation allowed for improved imaging in the shallow high frequency data and the deeper lower frequency target reservoirs (Figure 6).

Depth imaging

Controlled beam migration (as described by Vinje et al. 2008) was used to precondition the data for depth velocity model building in favor of the Kirchhoff equivalent for its noise attenuation and structural imaging properties. During the depth velocity model building phase close interaction between the contractor and interpreters enabled the following six step flow to be successfully performed:

1. Shallow structural picking.

2. Non-linear slope tomography to Top Salt.

3. Interpretation of Top Salt.

4. Salt flood (using velocities extracted from the wells).

5. Interpretation of Base Salt.

6. Deep structural picking.

The resulting velocity model (Figure 7) and subsequent depth migration gave improved imaging and positioning of the target formations compared to the legacy seismic (Figure 8).

Figure 7: Depth velocity model.

Figure 6: Stacks before (top) and after 5D Signal Enhancement (bottom).

Istanbul International Geophysical Conference and Oil & Gas Exhibition, Istanbul,Turkey, 17-19 September 2012.

Conclusions We have shown that with the latest processing algorithms coupled with a priori geological knowledge substantial improvement can be made over existing datasets. As demonstrated here, close interaction between the key stakeholders and processing team is vital in order to achieve a step change in resolution and imaging.

Acknowledgements The authors would like to thank Vegas Oil, Circle Oil, Sea Dragon Energy and CGGVeritas for their permission to publish this data.

References Le Meur, D., Benjamin N., Twigger, L., Garceran, K. Delmas, L. and Poulain G., 2010, Adaptive attenuation of surface-wave noise. First Break, 28(9), 83-88. Le Meur, D. and Poulain, G., Monte-Carlo Statics on Large 3D Wide-Azimuth Data: 73rd EAGE Conference & Exhibition incorporation SPE EUROPEC 2011, EAGE, Expanded Abstracts, F003. Poole, G. 2011, Multi-dimensional Coherency Driven Denoising of Irregular Data: 73rd EAGE Conference & Exhibition incorporation SPE EUROPEC 2011, EAGE, Expanded Abstracts, D009. Vinje, V., Roberts, G. and Taylor, R., 2008, Controlled beam migration: a versatile structural imaging tool. First Break, 26(7), 109-113.

Figure 8: Comparison between legacy data (top) and final depth stack stretched to time (bottom).