Demands for improved oil recovery prompted the Norwegian oil company, Statoil, to evaluate geologically complex oil and gas-condensate fields offshore Norway with a new approach. Here, major sections of producing reservoirs are heterolithic tidal units of interlayered mud and sand.

Using conventional modeling technology, Statoil geologists could not capture the fine-scale interlayering that would later impact their reservoir property simulations and reserve predictions. A unique multi-scale reservoir modeling tool developed by Geomodeling enabled Statoil to better understand its reservoir assets and choose the right strategies for optimized recovery.

Compared to homogeneous reservoirs with few structural and sedimentary complexities, heterogeneous reservoirs have relatively low recovery factors, Fig. 2. This is due to the effects of compartmentalization, multi-phase flow and pressure development within the reservoir. These effects complicate reservoir predictions, drainage strategies and improved oil recovery (IOR) measures.

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Statoil geologists recognized that the key to improving reservoir predictions was to model the observed heterogeneities in detail and evaluate their collective effect on flow properties. The reservoir properties that affect fluid flow and distribution--such as porosity, permeability and mud/sand volumes--are governed by small-scale (cm-to-m scale) geometries occurring below conventional well log or seismic resolution. For example, grain size contrasts between mm-to-cm scale sand and mud lamina set up strong permeability anisotropy. This forces fluids to move along pathways controlled by bedding structures and mud/sand ratios.

A NEW APPROACH

Today's demands for faster reservoir cycle times, coupled with limited CPU capacities, have made it impractical or unfeasible to model small-scale bedforms with conventional techniques. Using SBED proprietary geological modeling software, Statoil successfully modeled the observed heterogeneities in Halten Terrace tidal units. These detailed models were then used to generate effective porosity and permeability values for input to large-scale reservoir simulations and reserve estimations. Results represent the real distribution of porosity and permeability in reservoir intervals, and provide more accurate reserve calculations and production profiles.

Process-oriented modeling. The new modeling method follows a process-oriented approach. Process-oriented modeling of sedimentary structures mimics the products of sedimentary processes, such as bedform migration, erosion and deposition, without actually calculating the physics of grain movement or fluid flow. The method combines a vector-based movement of geometric surfaces through space and time with Gaussian simulation to create stochastic models of sedimentary structures and their associated petrophysical properties. (2,3) This approach considers the stochastic nature of sedimentation and the spatial distribution of reservoir properties. (4) The resulting models are highly realistic, Fig. 3.

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Upscaling. Upscaling is the process of extrapolating fine-scale reservoir data (e.g., cm-scale core plug and m-scale well log data) to coarser scales, to populate reservoir grid cells up to hundreds of meters in size. Conventional modeling approaches can upscale the data statistically (e.g., by arithmetic, geometric or harmonic averaging) without considering the effects of fine-scale heterogeneities or data bias. When the upscaled data and associated errors are used to populate large-scale reservoir grids, the resulting reservoir simulations have a high degree of uncertainty.

In the modeling workflow, sedimentary environments, such as fluvial, shoreface or deepwater facies, can be reproduced by creating stacked bedding models from over 100 bedding templates. These small-scale models are populated with petrophysical data (such as porosity and permeability) derived from core plug and well log measurements.

The petrophysical models are upscaled by a range of methods, including averaging and flow simulation, to obtain effective properties for a given bedding structure. The resulting models incorporate the effects of sedimentary structure on flow properties. (5) This approach enables upscaling without overloading the models with data or increasing model sizes. By comparing the effective property relationships derived from multiple SBED scenarios, a user can better discriminate between reservoir and nonreservoir intervals.