3D Grid Types

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JIM THOM, JOA Oil and Gas Houston LLC, Houston Texas USA,CHRISTIAN HCKER, JOA Oil and Gas BV, Delft, Netherlands

3-D Grid Types in Geomodeling and Simulation How the Choice of the

Model Container Determines Modeling Results

Requirements for subsurface modeling have changed substantially over the past years: the perception that limitsof hydrocarbon availability are in sight has moved attention in exploration and development to more complexreservoirs; more data has become available for predicting and monitoring performance that now need to beintegrated in subsurface models. Targets for (re)development have become more sophisticated and depend morecritically on accurate models of geometry and actual properties. This paper attempts to analyze the requirementsof static modeling at reservoir to basin scales and simulation of dynamic subsurface behavior, covering fluidflow as well as geomechanical response to man-made changes in the subsurface.

A quick look back into methods for describing subsurface reservoirs tells us that over the past 15 years using 2-D maps as carrier of geometry and property information have been gradually replaced by 3-D reservoir modelsusually built for single reservoir intervals. Maps are still an important means of documentation for securing

funding and getting well plans certified, but in general are a derivative of 3-D reservoir models. The latter arenow the main mechanism by which a thorough understanding of subsurface processes and their impact onhydrocarbon availability is created. As understanding of processes grew, it has become apparent that staticreservoir properties are not static but time-variant, being influenced by phenomena at scales greatly differentfrom that of reservoirs. Production is affected by mechanical processes at foot scale as well as by full-fieldcompaction responding to underburden and overburden up to surface. Large-scale models are also requiredwhen using 4D seismic data as a constraint in reservoir modeling and simulation. Effectively the phenomena tobe considered for a balanced solution occur at 5 orders of magnitude. Complex or just very mature assets cannotbe optimized based on single reservoir models. What is required are multi-scale, 3-D consistent representationsof the subsurface - in other words we still need Shared Earth Models even though the term has become lessfashionable.

Another learning has been that production data are often not accurate enough for optimizing mature fieldsshifting the focus from detailed history matching to real-time measurements of the current performance andusing this information for continuous optimization of asset performance. To do so one needs an evergreenmodel. Therefore our subsurface models should not only be comprehensive but also easily updatable.

Current reality is different though and practice in maturation teams is frequently pitched at lower levels ofsophistication and integration. Sometimes for a good reason - there are indeed cases where re-development canbe done well on the basis of decline curve analysis and where models or tools are of secondary importance toexperience and skill. However, simple assets can become complex when aging and the number of experiencedengineers capable of running a field by decline curves is getting smaller. While integration is required, practice

often shows workflows where optimization occurs in single expertise areas. So what is causing thisunderperformance of all past integration attempts? In our opinion a significant blocker to integration has so farbeen overlooked; it is actually the heart of all modeling packages, the so-called 3-D gridder that determineshow comprehensive models can be and how easily they can be updated.

Grid Types in Geological ModelingDifferent approaches have been developed to subdivide the subsurface into cells. The simplest of all grids is thevoxel grid or sugar cube grid; well known from storage of seismic data a matrix of uniformly sized cellswith horizontal tops and vertical sides. For capturing geological shape into such grids one would need to use


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In the Faulted s-Grid or Jewel Grid the behavior off faults is identical to that of s-Grids. At faults

however, cells are split exactly at the local position of the fault plane (Figure 3). This way all fault-to-faultand fault-horizon geometries can be represented in the 3-D model without compromises in detail or numberof faults. Due to the absence of blockers in structural modeling, models can be horizontally and verticallyextensive (full-field models from surface to deepest target).

Figure 3: Faulted s-Grid or Jewel Grid

Recently also so-called SKUA Grid has entered the market in which lateral cell boundaries can be normal totop model or be conformable to faults. This grid type is probably the only one capable of handling steep andoverturned strata.

In summary, the discussed grid types all have specific strengths and weaknesses. When judged solely oncapabilities with geometry handling, Pillar Grids show most fundamental limitations while s-Grids tend to beheavy, at least when it is attempted to limit inaccuracies at faults by using small cells. Faulted s-Grids are mostflexible in capturing geometry: there is no need to align the grid with faults or dip, cell size can be changed faults will always split cells exactly where they occur, even if it means splitting cells multiple times when usingcoarser grids. The only fundamental limitation of Faulted s-Grids lies in the representation of very steep tooverturning strata.

Capabilities with Property ModelingIn more complex structures, the large variation in cell size in Pillar Grids is problematic it may become

necessary to use finer grids to assure decent property sampling in zones where pillars fan out. Faulted andunfaulted s-Grids are less suitable for storing property distributions in strata dipping more than 45 degrees whilepillars can eventually be tilted to compensate the dip effect. Only SKUA grids support properties in very steepand overturning strata well.

Upscaling into Simulation Grids and DownscalingMost grid types mentioned above are also suitable to serve as simulation grids as far as information on SKUAGrids is available it seems that they are re-sampled into s-Grids. It must be reiterated here that simulators do nodirectly use these simulation grids but execute finite difference flow simulations on a so-called simulationmatrix, in which geometrical knowledge is reduced to cell center depth, node volume and, to some degree


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transmissibility's as calculated fromtransmissibility's works best if modelinother in terms of integers (e.g. 3 cells incase for all grid types except for SKUreservoir models, in order to close the l

Performance of Grid Types in S

Through the integration of (3rd

party)communicate complex structure and griThis process allows for properly handlivolumes and generating the non-neighbmethod is consistent X and Y permeabreservoir simulation results.

A great number of tests have been execdistributions that are stored in differentspeed and gave identical results to s-(unfaulted) SPE1 and SPE10 models.

challenging model where the effects ofextensively. The two most common dyGrids were evaluated for flow across awith two producing wells on the otherpermeability's. The fault throw for two f

Figure 4: Kz in the 3 different ty

permeability is displayed on the arbit

planes are highlighted in green, 2 o

injector is denoted by the blue well wi

The results show that water breakthrouFaulted s-Grids (see Figure 5). Moreprobably yield significantly different de

permeability and cell contact area. Upscalg grid and simulation matrix are similar andthe geological grid to 1 cell in simulation grid

Grids. The same holds for downscaling simoop between geological modeling and reservoi

mulation

reservoir simulators such as provided by Cid properties directly to the to the reservoir sig the polyhedral cells at the fault locations byr connections across the fault surfaces. An im

ility values, resulting in more precise transmi

uted that compare simulation results in identicgrid types. The Faulted s-Grids perform in sirids when benchmarked in CMG's IMEX si

A more interesting benchmark was conducted

grid sampling on transmissibility across faultsnamic simulation grids (s-Grid, Pillar Grid) alodel using one water injector in dead oil and kide of the faults. The model comprises of 10 iaults on the left is 540 feet, while single fault o

pes of simulation grids of faulted hetero

ary cross-section (blue layers ~ 1 Darcy, red

which define a fault zone with low perm

th the producers shown by the yellow derric

h times are different by 50% in Pillar Gridsimportantly, visualization and evaluation ofelopment and in-fill drilling planning (see Figu

ing and derivation ofcommunicate with eachnd matrix), which is the

ulation results back intor simulation.

G, Faulted s-Grids canulation deck generatorcomputing the cell poreortant advantage of thissibility calculations and

l structure and propertyulation with equivalent

mulation engine on theon a structurally more

ould be evaluated moreong with the Faulted s-eping the BHP constant

ntervals with alternatingthe right is 150 ft.

eneous field; vertical

layers ~ 1 mD). 3 fault

ability's inside. Water

.

ersus the Stair Step andthe results would mostre 6).


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Figure 5: IMEX Simulation results

model. Arrival time of water front dif

Figure 6: Water Saturations after 760

and near faults being handled differe

In general it can be stated that simulatGiven that s-Grids are most similar inbe correct if they are fine enough to reprunderstand the differences in simulation

Grid Types and Integrated WorIn integrated workflows, the overall perreservoir simulation is by far more critito utilize, analyze and interpret all offacilitated by newer gridding technoloassociated with all data contributing timplemented in computing environmeapplication engineering techniques in cgridding capabilities that can more effModeling (IRM). Through the realizaefficiently and effectively managed in a

showing water breakthrough at Well 3 pr

ers by approximately 50%.

9 days differ substantially in Pillar Grid due

tly.

ion tests show results from s-Grids and Faulteometry to simulation matrices results from s-esent geology properly. Additional testing is cubehavior.

flowsormance of the Grid in iterative loops betweenal rather than its performance in individual disthe subsurface data available in hydrocarbo

gies and approaches that can properly dealreservoir performance. Older gridding techn

nts that were limited in processing and visuonjunction with 32 and 64 bit desktop operatctively manage the scaling challenges associ

tion of the benefits of IRM, hydrocarbon aproactive manner saving both time and cost.

diction across faulted

to transmissibility's at

d s-Grids to be similarrids can be assumed to

rrently ongoing to better

geological modeling andipline areas. The ability

n assets can be greatlyith the range of scales

iques were devised andalization power. Newering systems enable newted Integrated reservoirsets can now be more

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3D Grid Types