Geostatistical modeling of reservoir facies and petrophysical properties (e.g. porosity and permeability) must be performed in a pre-faulted, deposition space in order to reproduce the true spatial correlations of these properties. A transformation function is therefore required to bring the data from the current position of the reservoir into the modeling space where experimental variograms are computed; reservoir properties are then stochastically simulated in the deposition space and mapped back to the real space. Current reservoir modeling practice uses a stratigraphic grid to conform to the reservoirs structure (bounding horizons and faults). The (i, j, k)-indexing of the nodes of the cells is used as a discretization of a curvilinear coordinate system which acts as the transfer function to the deposition space. This leads to a very strong underlying assumption: the geological distances (in the deposition space) are a function of the (i, j, k)-indexing. In the presence of non-vertical faults, the cells of the stratigraphic grids are either stretched or squeezed, violating this assumption. Moreover, in the presence of complex structural geology, these grids simply cannot be constructed without tremendous simplifications. The new proposed approach uses a 3D parameterization of the subsurface yielding a grid that minimizes the distortions of distances imposed by geostatistical simulation algorithms. These new Geologic Grids allow the construction of robust reservoir models whatever the structural complexity of the reservoir. Additionally, they guarantee the accurate mapping and upscaling of reservoir properties into either structured or unstructured Flow Simulation Grids. They also enable the creation of structured Flow Simulation Grids in which faults are defined as stair-steps allowing representation of the complete reservoir structure and ensuring orthogonality of cells. Introduction Today, reservoir modelers and engineers use the same 3D reservoir grid definition to construct their respective reservoir models. These grids are "structured" in the sense that each row contains the same number of cells; the same is true for each column which must have the same number of layers. The geoscientist should align the grids with the principal directions of deposition. The engineer should align them to preferential flow directions. However, in practice, the same grid is often used by both disciplines; only the resolution of the grids will differ. Depending on the oil and gas companies preferred approach, the Flow Simulation Grid is either down-gridded to a finer resolution, or the geological model is up-gridded to a coarser one. Both of these approaches can lead to erroneous results. As we show in this paper, the use of these types of grids has two major shortcomings. Firstly, it is extremely difficult and often impossible to accurately represent many structurally complex reservoirs - models have to be simplified; the geometry of the fault network is modified and some faults are even ignored. Secondly, once the reservoir model is constructed, cells are (i, j, k) indexed with implications that are often ignored. The indexing is commonly used to provide a transformation of the reservoir geometry from its current faulted and folded structure to an un-faulted, unfolded environment assumed to represent the reservoir geometry at the time of sediment deposition. Petrophysical properties such as net-to-gross, porosity and permeability, are stochastically distributed in this deposition space and mapped back onto the reservoir model. As described below, the structured nature of the reservoir grid leads to large volume variations from cell to cell which, if not properly taken into account, can lead to erroneous reservoir volume and reserve estimations.

After intense collaboration among operators, service companies, and software vendors (all members of an Energistics Special Interest Group (SIG)) Version 1.0 of the RESQML data exchange standard has been released. Prototypes implemented by both vendors and operators have been tested and have proved the efficiency of the concepts. RESQML has been designed to support: Interaction with real-time production and drilling domains;Transfer of giga-cell reservoir simulation models, which are currently in use in some areas of the world, and with static reservoir models, which may be significantly larger;Loss-less data transfer for complex grids, especially for non-standard connectivity;Retention of the geologic and geophysical meta-data associated with 3D grids;Data exchange for flexible and iterative multi-vendor subsurface workflowsacross geology, geophysics and engineering. A demonstration will illustrate how different components of a shared earth model can be exchanged between major commercial applications. Additionally, based on the Alwyn North Field dataset, a typical validation loop involving operator in-house and vendor applications will be demonstrated. The objective is to transfer in-house interpretation results (e.g., horizons and faults) as RESQML features to diverse structural, stratigraphic, and reservoir vendor applications, then re-import the RESQML features (modelled horizon and faults, reservoir grid geometry) obtained by these applications into the in-house application to ensure, at each step, an overall consistency with the original interpretation.

Subsurface modeling workflows are complex, data intensive, and iterative, requiring many different software packages to repeatedly exchange data. Improving the speed and accuracy of data exchange in model development can improve the speed, accuracy, and reliability of analyzing these models, ultimately supporting better decisions for asset development and economics over the life of a field. RESQML is an XML/HDF5-based, data-exchange specification that allows data to be transferred efficiently between the many different software packages used in subsurface modeling workflows. Key Version 2 enhancements include: a richer, more detailed RESQML data model with more domain data-objects (e.g., wells), and the ability to group related data-objects (e.g., faults, horizons, grids, etc.) and exchange them as a complete model. While these enhancements support more accurate and reliable models, they also presented challenges that were solved using new technological approaches new to RESQML. To design the richer yet more complex data model, the RESQML Special Interest Group (SIG) has moved from a hierarchical view of the data model to an entity-relationship view. The SIG is now using UML modeling tools to visualize the data model and produce both class and instance diagrams. The class diagrams are then used to generate XML schema definitions in a more automated and consistent manner. The Open Packaging Conventions_a container-file technology that stores a combination of files and their relationships to form a single entity for transfer in one compressed (ZIP) document format_are being used to group together independent data-objects (files) as a complete model. This paper presents an overview of RESQML Version 2 enhancements and capabilities, and explains how these new technology solutions are being used and their impact on subsurface modeling, the design process, future maintenance, and in helping software developers integrate RESQML into their own products.

In the framework of a 3D tomography project, we propose to build a geometrical modeling tool based on parametric representation of horizons. We decided to use the Gocad modeler because it has numerous features oriented toward the manipulation and the visualization of 3D objects. Since in the Gocad project the horizons are modelised as triangulated surfaces, we have built an interface which allows the user to map a triangulated surface into its parametric piecewise counterpart. The efficiency of the transformation of a triangulated surface into a parametric representation depends on the capabilities of the discrete smooth interpolation involved in the Gocad project. Indeed, we show that solving this mapping problem changes the variable topology of the triangulated network into a regular one based on quadrangles.

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