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Facies Modeling

An accurate model of the reservoir geology is crucial input in the field development planning process. Without it, costly decisions such as the placement of wells and future predictions about production volumes will be unreliable. RMS™ offers industry-leading tools for geological modeling. The facies modeling modules offers a range of methods for modeling geological facies, enabling the characterization of all geological environments. No other solution offers such a comprehensive and reliable set of tools.

When new well data in the form of a new well or new facies interpretation arrives, it is possible to update the existing facies model with Local Update to preserve the areas not affected by the new data. Local Update is available for Facies Composite, Facies Channels, Facies Indicators, and Facies Multipoint. 

Facies Modeling Benefits

  • A uniquely comprehensive set of facies modeling tools helps users describe the geological model, to improve the prediction of future reservoir behavior with less uncertainty.
  • Intrabody trends from the best object modelling tools in the market help better guide the distribution of petrophysical properties, ensuring higher confidence in the predictive power of the model.
  • A parallelized Indicators (SIS) algorithm generates models quickly, making it easier to create multiple models during uncertainty analysis, to mitigate risk.

Facies Modeling Features

An accurate geological model is the foundation upon which optimal hydrocarbon recovery is built. RMS offers two different approaches to facies modeling: Object-based and Pixel-based.

OBJECT-BASED FACIES MODELING

RMS provides a range of object-based facies modeling methods, refined for a range of different geological environments. All benefit from RMS’s advanced proprietary technology, including the unique ability to accurately honor well data and multi-well correlations. The geometry and distribution of the facies bodies can be controlled with seismic data and geological trends. Flexible, intra-body trends also ensure accurate modeling of all scales of geological heterogeneity.

Channelized facies deposits. From left to right: Depth intrabody trend, facies and proximal to distal intrabody trend.

Four different facies modeling methods use the Object-based approach:

  • Channels NGOM provides the ability to model meandering fluvial channels or channel belts and their associated levee and crevasse facies, in a single tool. It provides a high degree of flexibility, where a multitude of trends, well data, and geological knowledge is combined into a single facies model, allowing intrabody trends to be used in subsequent petrophysical modeling. It can address both meandering channel bodies and straighter channel belts.
  • The Facies Composite algorithm is an object-based, facies modeling technique that can be used for a wide range of heterogeneities and depositional environments. The facies bodies are simulated as geometrical objects that have a defined shape. The modeling algorithm can use well data, trend maps or seismic attributes to position the objects within the reservoir. The shape sizes are drawn from a user-specified distribution that can differ locally within the reservoir.
  • Facies Sedseis allows interpreted facies bodies to be used in constraining the modeling of true 3D facies objects. These interpretations typically come from high-resolution seismic data, where individual geological bodies can be identified. The modeled objects offer all the benefits of RMS object modeling, including accurate well conditioning and intra-body trends. Azimuth trends can be included to enable large facies objects to accurately follow detailed depositional trends.
  • The Facies Channels method has been designed to describe channel reservoirs, and has been applied in fluvial, delta-plain and deep-marine settings. This method has been used in the reservoir management of many giant fluvial fields across the globe. The Facies Channels tool models channels, channel margin facies (crevasses and levees) and intra-channel heterogeneities (gravel-lag thief zones, shale barriers).  The method includes a channel belt mode and an option for modeling fan geometries.

PIXEL-BASED FACIES MODELING

Pixel-based facies modeling does not reproduce a specific geometry (i.e., objects), but reproduces volume fractions, trends, and continuity defined by variograms or training images.

These statistical parameters can be used to specify the spatial correlation between observations. For example, in a carbonate reservoir model, the geometry of the facies objects is usually quite difficult to define; it is therefore much easier to generate a reservoir model based on statistical parameters. In some cases, it is even possible to connect the estimated size of the facies bodies to the spatial statistical parameters. Three different facies modeling methods use the Pixel-based approach: Facies Indicators, Facies Belts and Facies Multipoint.

  • Facies Indicators is a parallelized Sequential Indicator Simulation (SIS) algorithm which includes a wide range of options for trend control of the model, including vertical proportion curves and seismic attribute data. A unique feature of this version of indicator modeling is its extremely robust volume fraction control, ensuring an accurate distribution of facies.  
  • Facies Belts is a truncated gaussian stochastic facies modeling tool, designed to model various transitional geological environments, including stacking of facies belts in progradatioal and retro-gradational depositional systems.  Additionally, it includes a proportions mode that allows easy modeling of facies environments where the facies’ volume proportions vary vertically, laterally, or both. A unique option is the ability to generate trends which can be used to control further facies modeling, for more complex hierarchical facies environments.
  • Facies Multipoint is a set of methods and sequential simulation algorithms that uses a pixel-based approach to building stochastic facies realizations based on training images/pattern recognition. Multipoint in RMS is an improved “SneSim” type algorithm that includes improved well conditioning, and re-simulation of previously simulated pixels, helping the final realization avoid artifacts that conflict with the training image.

The RMS Facies Modeling modules are offered through different licenses:

  • Facies Modeling License
    • Channels NGOM
  • Facies License
    • Facies Composite
    • Facies Sedseis
    • Facies Channels
    • Facies Belts
    • Facies Multipoint
  • Indicators License
    • Facies Indicators