3D inversion of marine CSEM data: A feasibility study from the Shtokman gas field in the Barents Sea

Transcription

1 3D inversion of marine CSEM data: A feasibility study from the Shtokman gas field in the Barents Sea M. S. Zhdanov 1,2, M. Čuma 1,2, A. Gribenko 1,2, G. Wilson 2 and N. Black 2 1 The University of Utah, 2 TechnoImaging Summary We present a method and workflow for the 3D inversion for marine controlled-source electromagnetic (CSEM) data based on the 3D integral equation method with inhomogeneous background conductivity and focusing regularization with a priori terms. The use of focusing stabilizers makes it possible to recover subsurface models with sharper geoelectric contrasts and boundaries than traditional smooth stabilizers. Our inversion method is implemented in a parallelized code which makes it practical to run 3D inversion of 3D CSEM surveys to models with over a million cells. This makes it feasible to run multiple inversion scenarios that explore different regularization, a priori models, and data combinations. We present a suite of such interpretations for a synthetic 3D CSEM survey simulated from a very detailed model of the stacked anticline structures and reservoir units of the Shtokman gas field in the Russian sector of the Barents Sea. Introduction The premise of the various marine controlled-source electromagnetic (CSEM) methods is sensitivity to the lateral extents and thicknesses of resistive bodies embedded in conductive hosts. For this reason, CSEM methods have been used for de-risking exploration and appraisal with direct hydrocarbon indication. However, CSEM methods represent just part of an integrated exploration strategy where decisions are based on 3D shared earth models constructed from all available subsurface data. Maximum value is extracted from CSEM data when it is interpreted with a sound geological understanding of the potential petroleum systems and is integrated with available seismic and well data (Hesthammer et al., 2010). Quantitative interpretation of CSEM data is complicated by small responses of hydrocarbon-bearing reservoir units relative to the total fields. Moreover, CSEM data can be separated or transformed using linear operators as per seismic methods. Thus, the interpretation of CSEM is inherently reliant on iterative inversion. However, 3D CSEM inversion is inherently non-unique, meaning multiple models may satisfy the same data. To reduce ambiguity, geological prejudice is introduced through regularization. An unfortunate tendency that has emerged is the incorrect belief that a single model suffices for an inversion deliverable. It is best practice to run multiple inversion scenarios with different regularization parameters, stabilizers, data combinations and a suite of a priori models so as to discriminate any artefacts that may arise from the interpretation of a single model. To be able to practically run multiple inversion scenarios, it is important to develop rigorous but fast 3D inversion methods. Our approach to such inversion is based on the reweighted regularized conjugate gradient method where the fields and their sensitivities are computed using the 3D integral equation method with inhomogeneous background conductivity. This has been implemented in a fully parallelized code so that we can invert entire 3D CSEM surveys for models with millions of cells. This makes it practical to run multiple inversion scenarios as described earlier. We demonstrate the effectiveness of our workflow with the 3D inversion of synthetic CSEM data simulated for the Shtokman gas field located in the Russian sector of the Barents Sea.

2 Inversion methodology For modeling, we use the 3D integral equation method with inhomogeneous background conductivity (Endo et al., 2009). This enables models with arbitrary geoelectric complexity to be inverted. This form of the integral equation method has the distinct advantage that once the inhomogeneous background fields and Green s tensors have been pre-computed, they are stored and re-used in subsequent iterations, as well as in different inversion scenarios. Sensitivities are computed using a quasi-born method. The reweighted regularized conjugate gradient method is used to iterate the inversion. The use of focusing stabilizers makes it possible to recover models with sharper geoelectric boundaries and contrasts than can be obtained using traditional smooth stabilizers. For focusing stabilizers, we have the choice of using minimum support, minimum vertical support or minimum gradient support stabilizers (Zhdanov, 2002, 2009). The minimum support stabilizer recovers models with a minimum volume of anomalous resistivity. The minimum vertical support stabilizer recovers models with the minimum thickness of anomalous resistivity. The minimum gradient support stabilizer recovers models with the minimum volume of anomalous resistivity gradients. Our implementation is in a fully parallelized code which makes it practical to run large-scale 3D inversion of 3D CSEM surveys for models with millions of cells. Case study Shtokman gas field The Shtokman gas field lies in the centre of the Russian sector of the Barents Sea, approximately 500 km north of the Kola Peninsula. It is currently operated by a joint venture between Gazprom, Total and StatoilHydro. Shtokman is one of the world s largest known natural gas fields, with reserves of 3.8 tcm of gas and 37 mln t of gas condensate (Gazprom, 2009). The water depths gently vary from 320 m to 340 m over the field. The overburden sequence contains Jurrasic and Cretaceous siliciclastics of shallow marine origin. From 1800 m depth, the Shtokman reservoir sequence consists of four Middle and Upper Jurassic sandstone horizons in which the gas is trapped in an anticlinal fourway dip structure that is faulted in the crest. The reservoir horizons vary from 10 m to 80 m thickness. The porosity is between 15% and 20% permeability ranges from hundreds of millidarcies to over a darcy (Zakharov and Yunov, 1995). A 3D geoelectric model of the Shtokman field was constructed based on available geological and geophysical information (Figure 1). This model was used for simulating 3D CSEM surveys with 345 receiver positions measuring the inline and vertical electric and transverse magnetic fields. These fields were computed for 0.25 Hz, 0.5 Hz and 0.75 Hz using the integral equation method. Figure 1. Vertical cross-section of the Shtokman resistivity model.

3 Figure 2. 3D image of the Shtokman resistivity model. The vertical cross-section corresponds to Figure 1, and the horizontal section corresponds to the main reservoir unit. The array of 345 receiver positions is also shown. A vertical exaggeration of 6 was used in this image. We investigated a number of inversion scenarios. All of them commenced with no a priori models so as to not bias the performance of the following stabilizing functionals: Occam (OC), minimum norm (MN), minimum support (MS), minimum volume support (MVS), and minimum gradient support (MGS). As expected when no a priori model is used, the stacked reservoir horizons are recovered as a single resistive structure. However, as shown in Figures 3 and 4, the anticline structure defining the Shtokman field was recovered quite well when focusing stabilizers were used. Figure 3. 3D image of the Shtokman resistivity model obtained from 3D inversion of the inline electric field data using the minimum support stabilizer. The vertical and horizontal cross-sections correspond to those shown in Figure 2. A vertical exaggeration of 6 was used in this image. Figure 4 presents the results for the different inversion scenarios after 26 iterations. Though the actual resistivity models are 3D, we only show vertical cross-sections through each model for ease of visual inspection of model quality. Figures 4 shows that inversion with the Occam stabilizer converged to produce very smooth resistivity models bearing the least resemblance to the actual resistivity model shown in Figure 2. Inversion with the minimum norm stabilizer also produced smooth resistivity

4 models, though not as smooth or under-estimating as ones produced with the Occam stabilizer. More compact models with sharper geoelectric contrasts were obtained using the focusing stabilizers. Next, we compared the convergence of the misfit, which we define as the norm of difference between the normalized observed and predicted data (Figure 5). The focusing stabilizers had similar nearquadratic convergence to lower misfits. We notice that inversion with the Occam stabilizer had the slowest convergence. In other words, focusing stabilizers produce better results in less time compared to smooth stabilizers. Though not shown here, our results also show that there is noticeable improvement in the quality of the recovered resistivity models as the transverse magnetic and then vertical electric fields are added to the CSEM data prepared for inversion. It follows that as the industry moves towards acquiring 3D surveys with the intent of defining 3D structures such as the Shtokman field, the ability to invert all components of data along multiple lines for 3D resistivity models will prove to be essential. Conclusions We have developed an effective method and workflow for the 3D quantitative interpretation of CSEM data. This is based on 3D inversion and we have implemented this in a parallelized code. As we have demonstrated with our feasibility study for the Shtokman field, we can effectively invert entire 3D CSEM surveys to models with millions of cells. Our implementation makes it practical to run multiple inversion scenarios using different a priori models, data combinations, and stabilizers so as to build confidence in the robustness of features in the recovered models. This workflow can also discriminate artifacts that arise from the interpretation of a single model. Figure 4. Vertical cross-sections of the Shtokman resistivity model obtained from the inversion of the inline electric field data using the following stabilizers: (a) Occam, (b) minimum norm, (c) minimum support, and (d) minimum gradient support.

5 Figure 5. Convergence of the misfit for the following stabilizers: Occam (OC), minimum norm (MN), minimum support (MS) and minimum gradient support (MGS). These convergence curves are shown for inversion of the inline electric field data. Acknowledgements The authors acknowledge TechnoImaging for support of this research and permission to publish. The Russian Research Center Kurchatov Institute is acknowledged for assistance in preparing the Shtokman geoelectric model. Zhdanov, Čuma and Gribenko acknowledge the support of The University of Utah s Consortium for Electromagnetic Modeling and Inversion (CEMI) and Center for High Performance Computing (CHPC). References Endo, M., Čuma, M. and Zhdanov, M.S. [2009] Multiple domain integral equation method for 3D electromagnetic modelling in complex geoelectric structures: presented at SEG International Exposition and 79 th Annual Meeting, Houston. Gazprom [2009] Accessed 19 December Hesthammer, J., Stefatos, A., Boulaenko, M., Fanavoll, S. and Danielsen, J. [2010] CSEM performance in light of well results. The Leading Edge, 29(1), Zakharov, Ye.V. and Yunov, A.Yu. [1994] Direction of exploration for hydrocarbons in Jurassic sediments on the Russian shelf of the Barents Sea (in Russian). Geologiya Nefti i Gaza, 2, Zhdanov, M.S. [2009] Geophysical Inverse Problems and Regularization Theory. Elsevier. Zhdanov, M.S. [2009] Geophysical Electromagnetic Theory and Methods. Elsevier.