Th Volume Based Modeling - Automated Construction of Complex Structural Models

Transcription

1 Th Volume Based Modeling - Automated Construction of Complex Structural Models L. Souche* (Schlumberger), F. Lepage (Schlumberger) & G. Iskenova (Schlumberger) SUMMARY A new technology for creating, reliably and automatically, structural models from interpretation data is presented. The main idea behind this technique is to model directly volumes (the geological layers) rather than surfaces (horizons that are bounding these layers). In order to enforce the geological consistency of the created models another key element is built into this technology: it guarantees that the variations of dip and thickness of the created geological layers are minimized, while all seismic and well data are properly honored. The proposed method enables the construction of very complex structural models, independently from the geological settings, and even when such models have to be built from sparse or noisy data. The full automation of the model construction process allows to rapidly update the model, to efficiently identify the most uncertain parameters, to understand their impact, and to iteratively optimize the model until it fits all available data. To demonstrate the advantages of this technique the construction of a complex exploration-scale structural model of a prospect located offshore Australia is detailed.

2 Introduction Structural modeling and reservoir gridding are the centerpieces of the reservoir characterization workflow. They enable the construction of accurate reservoir models while leveraging all the information extracted from seismic interpretation. In this area - which is still the most disputed among reservoir modeling packages - two main challenges remain: (1) the construction of very complex structural models, especially when such models have to be built from sparse data, and (2) the full automation of the model construction process, that would enable saving weeks of manual edition and greatly reduce the risk of errors. The prize of such automation is the ability to rapidly update the model, to efficiently identify the most uncertain parameters, to understand their impact, and to iteratively optimize the model until it fits all available data, static and dynamic - an objective the industry has been striving to reach for decades. This paper describes a new, inherently robust, methodology for creating faulted structural models that is being included into an integrated reservoir modeling platform. The main idea behind this technique is to model directly volumes (the geological layers) rather than surfaces (horizons that are bounding these layers). In order to enforce the geological consistency of the created models another key element is built into this technology: it guarantees that the variations of dip and thickness of the created geological layers are minimized, while all seismic and well data are properly honored. In this paper, we first describe the inner workings of the Volume Based Modeling technique, we then illustrate the advantages it brings when building automatically complex reservoir models and finally we demonstrate how it was used to build a complex exploration-scale structural model of a prospect located offshore Australia. Related work The various approaches that have been described for creating a structural model of the subsurface can be classified in two categories: surface-based and volume-based modeling. The first stream of technology, by far the most widespread in the industry, attempts to create a numerical representation of the surface network formed by faults and horizon surfaces. A popular approach [1] consists in creating first a smooth, un-faulted surface that is later cut by the fault surfaces before being attracted by the interpretation points. In practice, this only works (1) when it is possible to create properly the initial smooth surface and (2) when the topology of the intersections between the faults and the initial surface does not differ from the one of the final fault polygons. Both assumptions tend to fail when dealing with complex compressional models or with a large number of X, Y or λ faults. Another surface-based approach [2] relies on the partition of the volume of interest into several closed volumes, delimited by the fault network, so that fault blocks can be modeled independently from each other. This requires the fault surfaces to be artificially extrapolated so that they fully cross volume of interest, an inherently non-robust process. The second family of techniques revolves around the concept of implicit modeling. Implicit modeling relies on the representation of surfaces as isovalues of a volume attribute the implicit function. It used to be associated with the construction of multi-z geobody surfaces (i.e. salt bodies) [3]. The horizon modeling technique underlying the pillar-gridding technology also belongs to this category, the volume attribute being a thickness proportion, interpolated onto a 2.75D support (the grid pillars). Methodology In the technique described in this paper, the implicit function corresponds to the stratigraphic age of the formations. It is embedded and interpolated in an unstructured tetrahedral mesh. The first step of the construction of the structural framework consists in building a tetrahedral mesh for carrying and interpolating the implicit function (Figure 1a-b). To do so, a 3D Boundary-

3 Constrained Delaunay mesh generator is used, with several constraints: firstly, faults affecting the considered horizons are taken into account as internal boundaries during the mesh generation, in such a way that some border faces of tetrahedra match the fault geometries in the produced mesh. Using unstructured grids as interpolation support allows controlling the density of the mesh, for maximizing the degree of freedom of the interpolation where it matters (i.e. close to the data), and adapting its anisotropy, in order to better capture the thickness variations in the layering. Note that such a flexibility and adaptability is extremely difficult or impossible to achieve using a structured mesh (like Cartesian grids, pillar grids, etc). The second step consists in interpolating the values of the implicit function on the nodes of the tetrahedral mesh (Figure 1c). This interpolation is done using a linear least squares formulation, which will tend to minimize (1) the misfit between the interpretation data and the interpolated surfaces and (2) the variations of dip and thickness of the layers. The computational cost is typically around 20 seconds to a few minutes depending on the required resolution. The third step is to generate surfaces representing every implicitly modeled horizon (Figure 1d). Since the specific value of the implicit function associated to each of them is known, this is simply done using any iso-surfacing algorithm. Finally, it is possible to take advantage of this Volume Based Modeling approach to generate a consistent zone model (Figure 1e). Every geological layer of a model can actually be seen as an interval of values of the implicit function. It is thus very simple, given its value of the implicit function, to know to which layer an arbitrary point belongs to. a b c d e Figure 1 Step by step construction of a volume model: (a) fault model and horizon interpretation, (b) tetrahedral solid, (c) implicit stratigraphy function (with periodic colorscale), (d) horizon surfaces, (e) layers Advantages Being fully 3D, this method is practically insensitive to the complexity of the fault network (Figure 2). It also benefits from many other desirable features: all conformable horizons belonging to a same conformable sequence being modeled simultaneously, as several isovalues of the same implicit attribute, they cannot cross each other [4]. Another advantage is that each and every conformable horizon constrains the geometry of all other conformable horizons that belong to the same sequence, and is itself constrained by their geometry. This practically removes the need for isochore or isopach maps when modeling some horizons from sparse data (i.e. well tops or 2D sections). Finally, this

4 technique does not only output the geometry of the horizon but also the volume attribute, defined everywhere in the volume of interest, representing the stratigraphic age of the formations (Figure 5). Figure 2 Model built from a transtensive sandbox experiment. Left: Fault model. Right: Volume model and interpretation of the top horizon.sandbox data courtesy pf Pr. K. McClay, Royal Holloway, Univ. of London. Case study The benefits of Volume Based Modeling will be illustrated using an example case study, in which the 3D geological modeling workflow implemented for an exploration project is described. The area under study is located offshore Western Australia nearby Gorgon Gas Field area. The main objective of this study is to deliver quickly a comprehensive, geologically consistent 3D structural and stratigraphic model that can be used for property population, quantification of hydrocarbon volumes and reservoir simulation. The survey covers an area of approximately 500km 2 and a thickness of about 3534ms, corresponding to approximately 4400m depth. The main reservoir formation in this area (Cretaceous formation) is composed of deltaic to shallow marine sandstone deposits. A total of 161 major faults with significant displacements are interpreted (Figure 6). The stratigraphy of the study area is complicated by the presence of a Jurassic unconformity, underlying a Cretaceous deltaic complex (clinoforms). The thick regional shale forms a seal and unconformably caps the Cretaceous complex. In total 10 major horizons (including unconformity surfaces) were interpreted on the 3D seismic. To demonstrate the capabilities of Volume Based Modeling, 4 additional horizons were added, based on sparse well information. In order to build an initial representation of the 3D structure, a very coarse (every 2000 m inlines and crosslines) seismic horizon interpre-tation was performed and fed into the Volume Based Modeler. This preliminary 3D model was then iteratively refined by comparing the geometry of reconstructed horizon surfaces with the location of the corresponding reflectors on the seismic image, and adding interpretation data in places where inconsistencies were detected. This allowed producing an accurate geological model while minimizing the effort and time spent on interpreting seismic. The next step consisted in incorporating the well data to control the construction of intermediate reservoir zones. Thanks to the Volume Based Modeling technology, it was not necessary to compute any isochore maps. A fully stair-stepped 3D corner point grid was then directly generated from the structural framework. The created grid can now be used for property population, volume computation and reservoir simulation. Conclusions A new technology for creating, reliably and automatically, structural models from interpretation data independently from the structural complexity has been presented. It has also been shown, using an example case-study, how this new method can help building large-scale, complex models from

5 exploration data. This new approach enables a significant speed-up in the model building and gridding workflow often one of the most time consuming step when creating a reservoir model. Beyond the structural framework, this new methods yields other valuable outputs: the stratigraphy attribute, which can be used by consuming applications, and a faulted unstructured mesh, which can be used as a support for geomechanical applications. Figure 3 Seismic data courtesy of Geoscience Australia. Top: some of the data used to build the Volume Based Model. Bottom: built australian model, including horizons based on well tops. Acknowledgements The authors would like to thank all their colleagues from the Petrel Structural Framework team that contributed to the integration of this technology in Petrel: David Desmarest, Sebastien Roret, Dustin Lister, Thomas Thrams, Michael Palomas and Mohamed Benaichouche. Dr Leigh Truelove is also warmly acknowledged for providing and interpreting sandbox models shown in this article. References 1. J-L. Mallet, 2001, Geomodeling, Oxford University Press 2. J.W. Neave, Analysis and characterization of fault networks, US Patent T. Frank, A-L. Tertois, J-L Mallet, 2007, 3D-reconstruction of complex geological interfaces from irregularly distributed and noisy point data, Computers and Geosciences, 33, 7 4. F. Lepage, L. Souche, 2012, Method for modeling faulted geological structures containing unconformities, Provisional Patent Application