Oil reservoirs hosted in deep-water slope channel deposits are a challenge to manage and model. A six-level hierarchical arrangement of depositional elements within slope channel deposits has been widely recognized, and dimensional (width and thickness) and stacking (amalgamation ratio and volume fraction) data have been acquired from published studies to establish parameters for a representative slope channel system. A new static modelling workflow has been developed for building models of channel complexes based on a simplified hierarchical scheme using industry-standard object-based modelling methods and a new plugin applying the compression algorithm. Object-based modelling using the compression algorithm allows for independent input of volume fractions and amalgamation ratios for channel and sheet objects within a hierarchical modelling workflow. A base-case channel complex model is built at the resolution of individual sandstone beds, conditioned to representative dimensional and stacking characteristics of natural systems. Inclusion of explicit channel axis and margin regions within the channels governs bed placement and controls inter-channel connectivity where channels are amalgamated. The distribution of porosity and permeability within these beds mimics grain-size trends of fining in the vertical and lateral directions. The influence of various geological parameters and modelling choices on reservoir performance have been assessed through water-flood flow simulation modelling. Omission of the compression method in the modelling workflow results in a three-fold increase in oil recovery at water-breakthrough, because the resultant unnaturally high amalgamation ratios result in overly-connected flow units at all hierarchical levels. Omission in the modelling of either the bed-scale hierarchical level, or of the axial and marginal constraints on the bed placement in models that do include this level, results in a two-fold increase in oil recovery at water-breakthrough relative to the base-case, because in these cases the channel-channel connections are too permissive.

In object- or pixel-based modelling, facies connectivity is tied to facies proportion as an inevitable consequence of the modelling process. However, natural geological systems (and rule-based models) have a wider range of connectivity behaviour and therefore are ill-served by simple modelling methods in which connectivity is an unconstrained output property rather than a user-defined input property. The compression-based modelling method decouples facies proportions from facies connectivity in the modelling process and allows models to be generated in which both are defined independently. The two-step method exploits the link between the connectivity and net:gross ratio of the conventional (pixel- or object-based) method applied. In Step 1 a model with the correct connectivity but incorrect facies proportions is generated. Step 2 applies a geometrical transform which scales the model to the correct facies proportions while maintaining the connectivity of the original model. The method is described and illustrated using examples representative of a poorly connected deep-water depositional system and a well-connected fluid-driven vein system.

Simple object- or pixel-based facies models use facies proportions as the constraining input parameter to be honored in the output model. The resultant interconnectivity of the facies bodies is an unconstrained output property of the modelling, and if the objects being modelled are geometrically representative in three dimensions, commonly-available methods will produce well-connected facies when the model net:gross ratio exceeds about 30%. Geological processes have more degrees of freedom, and facies in high net:gross natural systems often have much lower connectivity than can be achieved by object-based or common implementations of pixel-based forward modelling. The compression method decouples facies proportion from facies connectivity in the modelling process and allows systems to be generated in which both are defined independently at input. The two-step method first generates a model with the correct connectivity but incorrect facies proportions using a conventional method, and then applies a geometrical transform to scale the model to the correct facies proportions while retaining the connectivity of the original model. The method, and underlying parameters, are described and illustrated using examples representative of low and high connectivity geological systems.

<p>Irrespective of the specific technique (variogram-based, object-based or training image-based) applied, geostatistical facies models usually use facies proportions as the constraining input parameter to be honoured in the output model. The three-dimensional interconnectivity of the facies bodies in these models increases as the facies proportion increases, and the universal percolation thresholds that define the onset of macroscopic connectivity in idealized statistical physics models define also the connectivity of these facies models. Put simply, the bodies are well connected when the model net:gross ratio exceeds about 30%, and because of the similar behaviour of different geostatistical approaches, some researchers have concluded that the same threshold applies to geological systems.</p><p>In this contribution we contend that connectivity in geological systems has more degrees of freedom than it does in conventional geostatistical facies models, and hence that geostatistical facies modelling should be constrained at input by a facies connectivity parameter as well as a facies proportion parameter. We have developed a method that decouples facies proportion from facies connectivity in the modelling process, and which allows systems to be generated in which both are defined independently at input. This so-called compression-based modelling approach applies the universal link between the connectivity and volume fraction in geostatistical modelling to first generate a model with the correct connectivity but incorrect volume fraction using a conventional geostatistical approach, and then applies a geometrical transform which scales the model to the correct facies proportions while maintaining the connectivity of the original model. The method is described and illustrated using examples representative of different geological systems. These include situations in which connectivity is both higher (e.g. fluid-driven injectite or karst networks) and lower (e.g. many depositional systems) than can be achieved in conventional geostatistical facies models.</p>