Although faults traditionally have been modelled as membrane-like surfaces, the flow pattern through a fault is affected in a volumetric region. The physical properties of the fault rock will be different from what they were prior to the faulting process. Defining specific Fault Facies only present in the close vicinity of a fault gives a possibility to model the flow through faults more detailed than by conventional modelling. The Fault Facies will depend on both the pre-faulted facies, and the strain affecting the rocks when faults are created. A workflow has been created which demonstrates that the concept can be utilized in realistic reservoir modelling, starting from a conventional reservoir model where the fault is defined as a surface. A fault zone is defined in a small volume around the fault surface. It has a finer grid than the original model. First, all Fault Facies are modelled, followed by petrophysical modelling accounting for the fact that the greatest deformation occurs near the centre of the fault zone. The suggested concept produces direct modelling of vertical flow in the faults, making the unphysical non-neighbouring connections obsolete.

Traditionally fault impact on fluid flow is included by assigning transmissibility multipliers to flow simulation grid cell faces co-located with the fault plane (Manzocchi et al. 1999). A new method, called Fault Facies modelling (Tveranger et al. 2004, 2005), captures fault impact by considering faults as deformed rock volumes rather than simple planes. Architectures and petrophysical properties of these deformed volumes (i.e. fault zones) are linked to a range of factors such as lithology, host rock petrophysical properties, tectonic regime, orientation, magnitude, and distribution of stress, as well as the burial depth at the time of faulting. By understanding these links and identifying bounding values for distributions and parameters, fault zone architectures and properties, as well as uncertainties attached to these, can be forecasted. The fault facies approach allows 3D features such as anisotropic permeability fields, capillarity effects and tortuosity of flow paths inside the fault zone to be explicitly represented in the reservoir models. Furthermore, on the simulation grid scale, flow between cells on opposite sides of faults, as well as any uncertainty attached to this, can be estimated a priori rather than set deterministically a posteriori using history matching. The paper compares fluid flow behaviour of conventional transmissibility multiplier-type fault property models and fault facies type models through a series of simple tests. The study demonstrates that the fault facies concept is a technically feasible methodology that represents an alternative or supplement to standard industrial fault modelling methods.

A typical workflow for generating flow simulation grids goes through a facies modelling step. This typically involves setting up a stochastic model that is supposed to capture the important properties of the facies bodies in the reservoir volume in question and their uncertainties. This may be difficult or impossible within a particular modelling framework. Either we end up with a model too simple to be able to reproduce the characteristics of the reservoir, or the model parametres become too many and too difficult to specify. Hence users ask for methods able to reproduce the properties of a training image automatically. In any case one would like objective measures of similarity of facies realizations so that one is able to determine if a set of realizations have the properties that one wants. Here we discuss possible components in a metric on a space of facies realizations and present an implementation of a facies realization analyzer program. The algorithm simply scans a number of realizations and computes the global volume fractions of each facies and the number of facies bodies of each type. Then it computes the surface areas, volumes, and extensions in each directions for the bodies and performs simple statistical analysis of the realizations and compare it with properties of a training image. We present results of applying this software on facies realizations produced with variogram based methods, multipoint methods, and sequential Markov random fields. The analyzer algorithm is fast, applicable in 2D and 3D, and the results are in excellent agreement with the subjective impression of similarity or dissimilarity obtained through visual inspection.

1 A002 STRUCTURAL UNCERTAINTY MODELLING AND THE Abstract REPRESENTATION OF FAULTS AS STAIRCASES HARALD H. SOLENG ∗ JAN C. RIVENÆS ‡ JON GJERDE ∗ KNUT HOLLUND ∗ and LARS HOLDEN ∗ ∗ Norwegian Computing Center P.O. Box 114 Blindern NO-0314 Oslo Norway ‡ Norsk Hydro P.O. Box 7100 NO-5020 Bergen Norway Quite often structural uncertainty is the dominating uncertainty of the oil production of a field. The structural geology of an oil field is a large-scale property with non-linear effects on flow. Hence its effect on production can’t be quantified simply by varying a few parametres and as a consequence

We consider optimization of computational grids for petroleum reservoir flow simulations. In this context grid quality is determined by two independent error sources. On the one hand there is a loss of precision caused by upscaling of geological data from the fine geological grid to the coarser computational grid, and on the other hand there are numerical errors induced by a non-regular computational grid. In this paper we discuss gridding methods addressing these problems within the restrictions of industry-standard flow simulators. Grid problems in reservoir simulations The ability to predict the performance of a petroleum reservoir is of immense importance for the petroleum industry. For obvious reasons one would like to be able to know as much as possible about production rates and total production resulting from different production strategies. To this end, numerical reservoir simulation has gained wide acceptance as an important decision-making tool. By reservoir simulation we mean the process of inferring the behaviour of a real reservoir from the performance of a mathematical model of that physical system. For our purposes, the model is a set of partial differential equations with an appropriate set of boundary conditions, which describes the significant physical processes taking place in the system. The processes of interest in petroleum reservoirs are basically fluid flow and chemical mass transfer. The model equations must take into account gravitational, capillary, and viscous forces, as well as a reservoir description with respect to permeability heterogeneity and overall geometry. This paper is concerned with the problem of generating good computational grids for reservoir simulations. Here we face the particular problems connected to heterogeneity and upscaling. The problem of upscaling One of the inputs to a full field numerical reservoir performance simulator is a reservoir description. This is a model describing a possible three-dimensional map of the geology of the field. Such geological models are often generated by geostatistical methods. The geology is modelled using stochastic simulation conditioned on well observations and other available data. The reservoir description is usually generated on a fine scale, in part reflecting the scale of the input information such as core data. This is done in the belief that the geological model should capture as much as possible of the heterogeneities for accurate predictions of fluid flow. However, to enable a manageable computation, the reservoir performance simulator has to work on a much coarser grid. As a result, one must bring the fine scale permeability data over to a coarser representation. This involves an upscaling or averaging process. This is very problematic, since permeability is a non-additive property. a Fine grid b Coarse grid Figure 1: Reservoir divided into four disjunct sections by impermeable barriers. It is intuitively clear that in problems involving flow in porous media, averaging could lead to severe errors. An example is depicted in Figure 1: Imagine a reservoir divided into four disjunct regions by impermeable walls (Fig. 1a). A simple averaging process into a coarser grid would lead to a smeared-out picture where the no-flow barriers have become low-permeable layers (Fig. 1b). Thus, in this case, the coarse model is qualitatively different from the fine scale model. The challenge is to upscale with a minimal loss of precision in the predicted reservoir performance. Several ideas have been conceived to solve this problem, ranging from the direct pressure solver method of Warren and Price [1] to renormalization group techniques [2]. Different methods yield different results, and while one method is good for one type of problems another method can be better for another. However, by recognizing that upscaling involves both the choice of a coarse