The result will be an excellent approximation of the high-resolution calculations performed in the original model within the simulation model, more accurate predictions of reservoir behaviour and fl uid fl ow, and ultimately improved fi eld performance.

The Challenge of Upscaling

Geologists today often generate highly heterogeneous descriptions of reservoirs, describing complex structures in considerable detail to build the geological model. The result is that the detailed reservoir models tend to contain too many grid cells which must be reduced through upscaling due to the limitation of computing hardware in rela-tion to computing speed and time required to carry out the reservoir simulation.

Many detailed reservoir models, however, contain too many grid cells for effective reservoir simulation and must be reduced through upscal-ing. The challenge today is how to upscale these reservoir models without sacrifi cing the reservoir’s heterogeneity and without losing the original structure of the geological model.

This is crucial to ensuring that the geological data affecting fl uid fl ow is accurately transferred to the simulation model. Rock properties, such as porosity, absolute and relative permeability, all need to be upscaled accurately.

It is also essential that the models prepared for simulation are able to ease the process of history matching; predictions and simultaneously meet reservoir management needs in regard to defi ning new drilling well locations, sidetracks and workovers, as well as including simula-tion layers detailed enough to defi ne vertical barriers, high permeabil-ity streaks and changes in fl ow or storage capacity.

New Simulation Grid Design within Roxar RMSTM Better Captures Heterogeneities and Strengthens Simulation Process for Improved Field Predictions

Better Decisons. Increased Recovery

Roxar - The Modelling Company

Case Study

SUMMARY

Customer

Middle East and Abu Dhabi Marine Operating Company

(ADMA-OPCO).

The Field

The Thamama Complex Carbonate Reservoir, Offshore Abu

Dhabi

Challenge

To upscale a large geological model of 20 million cells into a

simulation grid of 2.5 to 3 million cells, without losing the

reservoir heterogeneities within the model.

Solution

A new methodology and algorithm within Roxar RMS,

Emerson’s industry leading reservoir modelling solution, that

takes a non-uniform layering approach to simulation grid

design. The formula and script developed identifi ed the most

similar layers with the smallest variation changes so that

maximum heterogeneity can be kept if the two layers

merged. The result is an irregular single layer simulation grid

which still contains the geological structure of the original

model.

Results

Extensive validation and quality control took place through

comparing the original geological model to the upscaled

simulation model using comparison methods, such as

histograms, scatter plots and streamline simulation. All tests

confi rmed that the Upscaled Model captured a high degree

of heterogeneity characterised by the fi ne scale Geological

Model.

This case study examines how the design of a simulation grid can be optimised and the reservoir model upscaled through a new methodology which takes place within Emerson’s industry leading reservoir modelling software, Roxar RMS.

The Field & Geological Model

In the case study presented in this paper, the challenge was to upscale a 20 million cell size fi ne geological grid up to a 2.5 million cell size simulation grid. Figure 1 shows a 3D view of the fi eld description.

Figure 1 - 3D Picture of the Field Description

The model was developed from the Lower Cretaceous Thamama Group Reservoir, part of one of the world’s largest oil reservoirs which underlies most of the Arabian Gulf from Oman through the United Arab Emirates and Saudi Arabia. The operator in question was the Abu Dhabi Marine Operating Company (ADMA-OPCO), a major producer of oil and gas from the offshore areas of the Emirate of Abu Dhabi and established in 1977.

The reservoir consists of highly heterogeneous porous and dense layers, including lithology limestone and dolomite with high permeability streaks. Figure 2 illustrates the number of layers.

The geological model developed, through Roxar RMS, consisted of the following:

• Number of Columns – 455

• Number of Rows – 256

• Number of Zones – 32

• Number of Layers – 170

• Number of Cells – 19.8 Million

• Number of Wells – 300

• Number of Faults – 75

Reservoir properties that were modelled included the stochastic distribution of the rock type and stochastic distribution of rock porosity; the stochastic distribution of permeability; and the saturation conditioned to RT (Rock Type).

About Roxar RMS

Roxar RMS is Emerson’s fl agship reservoir modelling product and provides an integrated software solution, including mapping, seismic inversion, seismic attribute analysis, reservoir modelling, well planning, reservoir simulation and uncertainty modelling tools.

Features include structural modelling tools which can reduce the time required to build a structural framework from months to weeks, high quality grids for reservoir modelling and simulation, and simulation-friendly grids suitable for accurate production predictions. Roxar RMS also provides the necessary upscaling tools to coarsen large reservoirs to sizes acceptable for commercial fl uid simulators.

RMS 2010, the version of RMS used in this case study, also comes with a number of signifi cant new features including enhanced structural modelling tools for generating high quality models from any reservoir; new property modelling tools; and a RMS 3D Gridder.

Developing an Optimal Simulation Grid Design

The ‘optimal simulation grid design’ method applied to this model is a recursive algorithm which works by merging two adjacent layers with minimal variation changes, until a single layer model is generated. The algorithm is based on an algorithm presented at SPE by BP and Texas A&M University (2006, SPE-95759-PA, Optimal Coarsening of 3D Reservoir Models for Flow Simulation).

In the words of the authors, “the approach has been validated for a number of oil and gas projects, where fl ow simulation through the coarsened model is shown to provide an excellent approximation to high-resolution calculations performed in the original model.”

The algorithm uses model properties, such as bulk volume, porosity, permeability and rock type, as input for quantifying the heterogeneity. The algorithm is then implemented as programming script within Roxar RMS, based on Internal Programming Language (IPL) scripts.

The initial stage of developing an optimal simulation grid design was through layer coarsening. This was achieved through a layer coarsening script where quantitative parameters were generated to measure variation changes and determine where two cells could be merged.

The formula and script identifi ed the most similar layers with the smallest variation changes, so that maximum heterogeneity could be kept if the two layers merged. At each stage of merging layers, the remaining heterogeneity was then calculated and converted to a percentage (Hcurrent / Horiginal).

Figure 2 illustrates how the heterogeneity is plotted versus model layers with the point of infl exion on the curve being the optimal point where the minimised model layers combine with the maximum retained heterogeneity.

Case Study

Figure 2 - Heterogeneitry Plotted Versus Model Layers

At each stage, the layer coarsening script reports the total number of layers, the proportion of each layer, and the combination sequence based on the original layer index, as illustrated in fi gure 3 with a small spreadsheet generated with an index of only 100 and 1 to 16 layers. The algorithm can also be applied to determine the optimal number of columns and rows.

At each stage, the layer coarsening script will report the total number of layers, the proportion of

each layer, and the combination sequence based on the original layer index.

Figure 3 - Spreadsheet Reporting the Total Number of Layers, Proportion of Each

Layer, and Combination Sequence Based on the Original Layer Index.

As one can see, the layers are not uniform with heterogeneity captured within each layer. The result is an irregular single layer simulation grid with control lines created through IPL scripts and irregular layering through a Global Grid Refi nement operation.

The result is a statistical measurement of the heterogeneity of a fi ne-scale model.

Implementation on the Thamma Geological Model

The Thamma geological model was inputted into the simulation grid design described above.

Figure 4 illustrates the Vertical (Z) Heterogeneity Optimisation for each zone in the model.

Figure 4 - Vertical (Z) Heterogeneity Optimisation for Each Zone in the Model

The control lines, created through the IPL script, also helped to defi ne the columns and rows in the grid building process. As can be seen in fi gure 5 when evaluating lateral heterogeneity, the distribution of controls lines shows that the bigger cell sizes are on the fl anks and the smaller cell sizes on the crest.

Figure 5 - Evaluation of the Lateral Heterogeneity

Figure 6 shows comparisons between the original geological grid and the simulation grids and fi gure 7 the upscaling process within RMS in relation to RT, Porosity, Permeability, and Saturation.

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Figure 6 - Comparisons Between the Original Geological Grid and the Simulation

Grids.

Figure 7 - The Upscaling Process Within RMS in Relation to RT, Porosity,

Permeability, and Saturation.

Validating the Results

The upscaling results were validated primarily through comparing the geological grid fi ne scale model with the upscaled grid model with irregular cell sizes to determine how much of the reservoir’s heterogeneities were captured.

Figure 8 shows the porosity distribution of a Geological and Simulation grid and Figure 9 shows the facies distribution of a Geological and Simulation grid. While the upscaled, irregular cell-sized grid model loses a little bit of detail, this is negligible. The high quality and geological heterogeneity of the simulation grid was later confi rmed by the reservoir engineers.

Figure 8 - Comparison of Porosity distribution of a Geological and

Simulation grid.

Figure 9 - Comparison of Facies distribution of a Geological and Simulation grid.

PLT and other logs, for example, indicated lower heterogeneity in the upper zone and higher heterogeneity in the lower zone (as one can see in fi gure 10, the top layers are thicker and layers below are thinner). This was also backed up through the non uniform layering scheme method.

Figure 10 - The Figure Illustrates Lower Heterogeneity in the Upper Zone and

Higher Heterogeneity in the Lower Zone.

With facies and properties upscaled to the simulation grid, various methods, such as histograms, scatter plots and streamline simulations, were all undertaken to compare properties between the geological and simualtion grid. The upscaled and geological models’ properties were compared, for example, in relation to

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rock type, permeability, and porosity (see fi gure 11,12,13), with histograms, scatter plots and streamlines showing very close correlations.

Figure 11 - The Histograms Showed Very Close Correlations to Rock Type,

Permeability and Porosity.

Figure 12 - QC of the Geological and Upscale Grid

Finally, we applied a streamline simulator with the streamline pattern in the geological model very similar to the streamline pattern in the upscale model (see fi gure 13), again providing validation of the upscaling methodology.

Figure 13 - When Applied Through a Streamline Simulator, the Streamline Pattern is

Very Similar to the Streamline Pattern in the Upscale Model.

Summary & Conclusions

In this case, a geological model of 19.8 million cells was upscaled to a simulation grid of 2.8 million cells within Roxar RMS, with the heterogeneity preserved by using a non uniform vertical layering scheme and non uniform lateral cell size methodology. The methodology was successfully applied in the Thamama reservoir.

Signifi cant advances in upscaling, such as this approach, are playing a crucial role in enabling detailed and complex reservoir models to go forward for simulation with the maximum amount of heterogeneity still captured within the model. This will enable engineers and geoscientists to work through the fl uid fl ow simulation process with an excellent approximation of the high-resolution calculations performed in the original model within the simulation model.

The result will be more accurate predictions of reservoir behaviour and fl uid fl ow, and ultimately improved fi eld performance.

Emerson Process Management would like to thank ADMA for permission to publish this case study.

To learn more please visit www.roxarsoftware.com or email us on rss@roxar.com.

Case Study