2012 SEG SEG Las Vegas 2012 Annual Meeting Page 1

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1 3D full-waveform inversion at Mariner a shallow North Sea reservoir Marianne Houbiers 1 *, Edward Wiarda 2, Joachim Mispel 1, Dmitry Nikolenko 2, Denes Vigh 2, Bjørn-Egil Knudsen 1, Mark Thompson 1, and David Hill 2 1) Statoil ASA 2) WesternGeco/Schlumberger SUMMARY We applied isotropic acoustic 3D full-waveform inversion (FWI) to OBC data from the Mariner field, a shallow heavy-oil field in the North Sea. This resulted in a multiphase workflow that can be adapted to imaging challenges in similar geological settings. FWI improves the resolution of the velocity field compared to the benchmark velocity field from reflection tomography. The background trend of and details introduced in the velocity model correlate well with the geology from seismic data and with well logs down to reservoir level. Resulting depth images show significantly better well tie in the overburden and improved definition of sand bodies at reservoir level. INTRODUCTION Full-waveform inversion (FWI) is an inversion method that attempts to build high-resolution images of physical subsurface parameters like velocity, using simultaneously the information on traveltime, amplitude, and phase in the full recorded seismic wavefield (Tarantola, 1986, 1987; Pratt et al., 1998; Virieux and Operto, 2009). Given an initial model for the subsurface parameter(s) of interest (e.g., P-velocity), FWI aims to find an updated model by minimizing the difference between the observed and predicted data. Although the theory on FWI was developed in the eighties, FWI has only relatively recently been successfully applied to real 3D datasets (e.g., Sirgue et al., 2009; Plessix, 2009). For some of these 3D case studies the seismic image before FWI has poor quality due to presence of, for example, a gas cloud or salt body. After FWI, the seismic image inside and below the gas cloud (Sirgue et al., 2010; Ratcliffe et al., 2011) or of the flanks of the salt body (Vigh et al., 2010) is significantly improved. The impact of FWI at reservoir level is typically indirect, due to limited penetration depth of FWI when using only early arrival energy from surface seismic data. To test and develop a suitable workflow for FWI in the North Sea, and to investigate the benefit of FWI to further imaging products, a research project was initiated in which FWI was applied to Mariner OBC data. The starting point in this Mariner case study is somewhat different from other 3D case studies reported, in that we wish to investigate whether the velocity model can be improved beyond reflection tomography, which already gives a reasonable seismic image compared to problematic subsalt and gas cloud results. Moreover, the reservoir at Mariner is so shallow that the use of FWI may result in more accurate velocity information at the reservoir level. In this paper, the first results of the research project are presented. MARINER IMAGING CHALLENGES The Mariner field is located in the UK sector of the North Sea; it was discovered in 1981 and is currently under development. The water depth at Mariner is about 110m. The reservoir section is relatively shallow with a depth of m. It contains two heavy oil targets of Paleocene age: The first target is the Heimdal sands within the Lista shale, the second target is the Maureen sandstone, which lies deeper than the Lista shale. The field was imaged in 1995/1996 and again in 2008 with a conventional high-resolution 3D marine survey over the full field. Despite its shallow depth, there are several imaging challenges at Mariner. In the shallowest overburden, there is a channel crossing the field. The channel was formed sub-glacially by melting water from the overlying ice. From earlier seismic processing, it is known that the channel is filled with high-velocity material. The shallow channel can be mapped out on the seismic, but events below it show some degree of pull-up, undulations, and discontinuities. The Heimdal sands consist of a complex, disrupted channel system of remobilized unconsolidated and uncemented sand and injectites. These sands are hard to image with pressure wave (PP) data due to the low acoustic impedance contrast with the surrounding shale. AVO analysis shows that they are best seen on the far-offset stack; well logs show a clear contrast in shear impedance and V p /V s -ratio between the sand and surrounding shale. However, below the reservoir section, there is a hard basement of Devonian and older age. On top of the Devonian basement, there is locally a thin chalk layer preserved. Both chalk and basement have very high P-velocities, up to 5300m/s, and refracted P-wave energy from the basement and chalk interferes with the far-offset data at reservoir level, which influences the image of the Heimdal sands on the far-angle PP stacks. Alternatively, the AVO analysis indicates that the Heimdal sands can be imaged with converted wave (PS) data. Therefore, a small multicomponent OBC survey was acquired over part of the field in Indeed, the PS stacks provide additional information on the Heimdal sands, but they are still difficult to interpret and localize. SEG Las Vegas 2012 Annual Meeting Page 1

2 MARINER OBC DATA The 2008 OBC survey design consists of 12 cables of 6200m length each, equipped with 4C receiver stations every 25m. The cable separation is 200m and 63 sail lines are recorded parallel to the receiver cables. Each sail line is 10km long and the separation between sail lines is 100m. The shot spacing is 18.75m using flip-flop shooting. The maximum offset for this data is 8200m in the inline direction, and 4200m in the crossline direction. The lowest usable frequency for these data is around 4Hz. After acquisition, the data (PZ-summation and PS) were depth imaged with Kirchhoff PSDM, using a velocity model obtained from reflection tomography. A weak vertical transverse isotropic (VTI) anisotropy (1% delta and 2% epsilon from m down to the top Devonian) was included in the velocity model to obtain a better match between events from PZ and PS imaging, flatten the gathers, and brighten the stack. The reflection tomography method was not able to introduce the shallow high-velocity channel automatically and shallow sonic log information was not available. To avoid push-up of events below, the channel was mapped out on the seismic and the velocity inside was manually increased with 12.5% in the VTI model. This introduced a sharp velocity contrast in an otherwise smooth velocity model (Figure 1a). In addition, it resulted in low-velocity zones below the channel. The reflection tomography model and resulting VTI PZ images will be the benchmark for comparison with the FWI results. PRECONDTIONING AND FWI METHODOLOGY The input data used for FWI are minimally preprocessed hydrophone data. The reason for using pressure data over PZ-sum data is that the latter still contain remnant multiple energy and shear noise (in particular, around the shallow channel), which may hamper FWI. The main preprocessing steps include designature to zero-phase including deterministic debubble, a two-sided (separating positive and negative offsets) residual debubble operator per receiver line and shot point, random noise and mild linear noise attenuation, and amplitude scaling to correct for bad receiver coupling. No direct arrival attenuation or demultiple was applied. In fact, deterministic water-layer demultiple (DWD) and tau-p-q are struggling to attenuate the first water-bottom bounce of the water bottom and top of the shallow channel because their primaries are contained in very few live traces in this shallow marine environment compared to their multiples. It has not been tested whether a combination of DWD and PZ-summation can produce demultipled data of sufficient quality for FWI. In this case study, acoustic and isotropic FWI is performed in the time domain, because ε and δ-values are small. As the input data are not demultipled, the forward modeling in FWI includes a free surface. A high-cut filter is applied to obtain input data for each frequency range (peak frequencies at 4, 6, 8, and 10Hz, respectively). Using the source signature and a residual wavelet compensation scheme ensures global matching between embedded wavelets of the observed and predicted data in each frequency range. To mitigate interference from multiple energy and focus on specific depth ranges, FWI is performed in several phases using subsets of the input data (e.g., early arrivals with variations of inner mute, shallow reflections, and head waves only). Figure 1c shows a receiver gather and two of the mutes that are used to select input data; the majority of the reflection data is thus not used at all. The definition of the inner mute for selecting early arrivals is paramount for obtaining maximum resolution in the velocity updates and sufficient sampling of key events. Some events dominate the updates and must be excluded in some FWI phases to put more weight on target depths. The multiphase workflow ensures a top-down approach in updating the velocity model. By opening up the mute, including longer offsets, and increasing the frequency in the input data, one obtains updates at greater depth and with more details. Figure 1: A) Vertical section showing overburden with the inserted shallow channel from reflection tomography. B) Initial model for FWI. C) Example of mutes applied in two of the FWI phases. The VTI reflection tomography model is used to derive an initial velocity model for FWI. Some adjustments are made to avoid getting trapped in local minima. First, the VTI model is converted to an isotropic model. Subsequently, the shallow channel and low velocity below are cut away and interpolated (Figure 1b). In addition, the velocity at reservoir level is scaled down by a factor of to obtain a better match with existing well logs. Finally, the velocity in the basement is ramped up from 3300 to 5300m/s to get a better time-offset match between headwave events on the predicted and observed seismic data. SEG Las Vegas 2012 Annual Meeting Page 2

3 The density ρ is obtained from the P-velocity by using Gardner s rule ρ = 0.31V p The density is recomputed after each velocity update. In general, 4-5 internal iterations in each frequency range within each FWI phase results in convergence of the objective function. RESULTS AND DISCUSSION Figure 2 shows a vertical section of the benchmark and best-to-date FWI velocity obtained with the multiphase approach described before. The FWI velocity model shows better lateral and vertical resolution. Moreover, these velocity variations fit with the geology on the background seismic. For instance, the high-velocity shallow channel is reintroduced in FWI, now with lower velocity than in the reflection tomography model and with additional detail (such as the low-velocity layer above, which may be due to e.g. gas that has migrated from the shallow channel). Also on depth slices the high-velocity channel follows the seismic image very well. A refraction tomography performed in parallel is not capable of introducing the highvelocity channel. The high-velocity anomaly at m correlates with a vent-like structure on the seismic. The high velocity here may be due to strongly cemented sand, potentially caused by gas leakage (a common feature in the Tertiary). Similarly, the high-velocity layer at about 1000m correlates with the Balder tuff. To assess the accuracy of the additional details in the velocity model, various QC methods are available. Figure 3 shows a seismic line through two wells, after isotropic Kirchhoff PSDM with the best-to-date FWI velocity model, using the same preprocessed PZ-summation data as were used in the benchmark VTI reflection tomography Kirchhoff PSDM, and the same post-processing. Well picks of the key horizons are indicated. In the overburden, the well picks tie better with the seismic data in the full-angle FWI stack, in particular the top Lista (top of reservoir). Also, the events below the shallow channel are flatter and there are various places where the image is improved. At reservoir level, the image of the Heimdal sands is improved on the far-angle stack, and they correlate better with the gamma-ray log in the wells. However, the well tie of the basement is not as good. The FWI velocity matches the sonic logs quite well down to reservoir level ( m), see Figure 4a. FWI results in relatively low velocities in the Lista shale (Figure 2). This is partially due to down-scaling during preconditioning, and partially attributed to the head-wave-only FWI. The FWI phases with early arrivals cause an increase in the velocity at reservoir level. These early-arrival velocity updates are muted because they do not match with the available well logs. This effect may e.g., indicate anisotropy in the Lista shale (Prieux et al., 2011). The well log also shows that additional work must be done from the Maureen sandstone and below. The current FWI model provides an excellent starting point for this, using reflection energy for high-resolution tomography or targeted FWI. Figure 4b shows common image point (CIP) gathers for the benchmark and FWI Kirchhoff migrations. Here, FWI has clearly flattened the gathers both in the overburden and at reservoir level (e.g., at the green arrows). Again, this is not the case everywhere, in particular not at the Maureen sandstone and basement level. Figure 2: Comparison between benchmark VTI velocity model (Vp) from reflection tomography (left) and the best-to-date isotropic FWI velocity model (right). A mid-angle stack from Kirchhoff PSDM with the respective velocity model is overlain each display. SEG Las Vegas 2012 Annual Meeting Page 3

4 Figure 3: Seismic section through two wells from benchmark VTI Kirchhoff PSDM (left) and isotropic Kirchhoff PSDM with the best-to-data FWI velocity model. The upper panels are full-angle stacks (0-40 ) and the the lower panels are far-angle (30-43 ). There is better continuity and fewer undulations below the shallow channel in the FWI stack (U) and improved image various places (I). The location of some Heimdal sands from the gamma-ray logs is indicated on the far-angle images (dark blue). CONCLUSION Isotropic 3D FWI was applied to a small Mariner OBC dataset, using primarily early arrival data in a multiphase workflow. The combination of a minimum frequency of 4Hz, maximum offset of 8200m, and initial depth velocity are sufficient to update the velocity model down to reservoir level. The resulting FWI velocity model reveals more detail in the shallow and deeper overburden compared to the benchmark velocity model from reflection tomography. These velocity features were not present at all in the input FWI model. Depth images show better well ties in the overburden. The seismic image at reservoir level is also improved, albeit not everywhere. The Heimdal sands are better visible on the far-offset stacks and they match better with the well information, making them easier to map. The impact of FWI at reservoir level as used here is constrained by the limited size of the survey. Reflection energy may indeed be required to further improve the velocity model at reservoir level and below. Nevertheless, the case study shows that FWI in this North Sea environment opens up possibilities in imaging and interpretation. Figure 4: A) Comparison of velocity from FWI with well log and reflection tomography. B) CIP gathers from Kirchhoff PSDM with reflection tomography and FWI velocity model. ACKNOWLEDGEMENTS The authors thank Statoil ASA, WesternGeco/Schlumberger, and the partners in the Mariner license ENI Norge AS and Nautical Petroleum for permission to publish the results. SEG Las Vegas 2012 Annual Meeting Page 4

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2012 SEG Technical Program Expanded Abstracts have been copy edited so t hat 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 Plessix, R.-E., 2009, Three-dimensional frequency-domain full-waveform inversion with an iterative solver: Geophysics, 74, no. 6, WCC149 WCC157. Pratt, R. G., C. Shin, and G. J. Hicks, 1998, Gauss -Newton and full Newton methods in frequency -space seismic waveform inversion: Geophysical Journal International, 133, Prieux, V., R. Brossier, Y. Ghola mi, S. Operto, J. Virieux, O. I. Barkved, and J. H. Kommedal, 2011, On the footprint of anisotropy on isotropic full waveform inversion: The Valhall case: Geophysical Journal International, 187, no. 3, Ratcliffe, A., C. Win, V. Vinje, G. Conroy, M. Warner, A. Umpleby, I. Stekl, T. Nangoo, and A. Bertrand, 2011, Full waveform inversion: A North Sea OBC case study: 81st Annual International Meeting, SEG, Expanded Abstracts, Sirgue, L., O. I. Barkved, J. Dellinger, J. Etgen, U. Albertin, and J. H. Kommedal, 2010, Full waveform inversion: The next leap forward in imaging at Valhall: First Break, 28, Sirgue, L., O. I. Barkved, J. P. van Gestel, O. J. Askim, and J. H. Kommedal, 2009, 3D waveform inversion on Valhall wide -azimuth OBC: 71st Conference and Exhibition, EAGE Extended Abstracts, U038. Tarantola, A., 1986, A strategy for nonlinear inversion of seismic reflection data: Geophysics, 51, Tarantola, A., 1987, Inverse problem theory: Methods for data fitting and model par ameter estimation: Elsevier Science. Vigh, D., B. Starr, J. Kapoor, and H. Li, 2010, 3D Full waveform inversion on a GoM data set: 80th Annual International Meeting, SEG, Expanded Abstracts, Virieux, J., and S. Operto, 2009, An overview of full -waveform inversion in exploration geophysics: Geophysics, 74, no. 6, WCC1 WCC26. SEG Las Vegas 2012 Annual Meeting Page 5