Make sure your model captures the essential features of the reservoir. You don't need a fine details model to verify the correctness and accuracy of your reservoir characterization. Fine details take a lot of effort and time, and won't fix the model if key features were not properly caught. Check if your model does not miss key physical concepts.

Not all geo-modeling software allows you to easily model the Net to Gross value for each 3D cell, GeolOil does. Unless you have an extremely high vertical resolution 3D grid, your Net to Gross for each cell should be a number between 0.0 and 1.0, rather than exactly zero or one.

By keeping an accurate Net to Gross model, you can easily verify storage petrophysics properties (porosity and water saturation) over the reservoir rocks instead the cell's upscaled whole value. For instance, if you are working with clastic heavy-oil or bitumen reservoirs, it is typical to have porosity values around 0.25-0.32, but if the cell contains some proportion of very shaly sands, the upscaled whole cell porosity might be reduced to 0.15-0.24 depending upon the whole cell Net to Gross.

It is far easier and understandable to handle a joint porosity and net-to-gross pair with values of 0.30 and 0.70 than to handle a single upscaled porosity cell value of 0.21 with an implicit net-to-gross of 1.0. Furthermore, if you a have a client request to change the petrophysical VCL threshold cutoff, you will see immediately how the net-to-gross ratio is affected.

You don't have to reproduce legacy values of former reservoir volumetrics, but in many cases those old volumetrics reports, provide a valuable guide to understand reservoir storage. If the legacy volumetrics report and your current static model were built on the same lease or polygon outline, values might be close. If they don't agree, you should ask yourself why. Is it the porosity?, the water saturation?, the net-to-gross?, the gross thickness?, the threshold cutoff values?

This is very typical. Even after a carefully built 3D geomodel, the simulation forecasts too much water compared to production history. Why?, you have build several shaly SW water saturation models, Simanduoux, Fert, etc., and all yield high water saturation.

There are limitations to the shaly sand water saturation models. If that if your case, you could try to adjust a cation-exchange water saturation model instead, but then you can not estimate its parameters with log data alone. In other cases, even if special carbon-oxygen logs are ordered, they report also a lot of water.

If you don't have reliable core SW water saturation lab measurements to calibrate your logs, and all petrophysical analysis, Rw and sensibilities point to a high water saturation, consider to review the relative permeability curve to the water (as one client from Thailand have suggested us).

Where did you get the relative permeability curves your are using in the simulator?. Are they reliable?. Did you get them from a reputable laboratory or published literature?. If you don't have a trusted source for the relative perm curves, then you could try to perform sensibilities on then. For instance, you can keep the same endpoints, and specifically the same irreducible water saturation and yet achieve different water production forecasts by just keeping the water relative perm curve close to the origin while honoring a fixed SWirr.

Other approach could also be to handle several simulation rock type zones include file (different relative perm indexes) for different lithologies, instead of using a single, unique relative perm set of curves throughout the reservoir. Compute your own rock types, based on combinations of petrophysical properties. You may for example, classify the reservoir rock into a factorial combination of two levels of Vclay and two levels of porosities, to define a set of four rock types, each one with a relative perm curve, then GeolOil will yield you a 3D grid include file with the four rock types 1,2,3 and 4.

Your geo-modeling team should work in close interaction with the simulation engineers team throughout the project's life. It is no longer valid to have a team of geologists and petrophysicists working on a project for serveral months, only to discover there is no way the geomodel passes even a basic simulation history match.

If you have to outsource the reservoir modeling studies, consider to hire a solid consulting company that offers you a real integration of the static and the dynamic modeling stages.

Avoid early optimization of the static modeling. Deliver a quick, preliminary version of the geo-cellular model to the simulation team and quickly assess its fluids production profile. Then iterate again until a stable and consistent model is achieved.

It happens all the time. You used the latest structural model provided by a geo-phycisist, but its surface top does not pass through the current well tops. That does not necessarily mean that the structure model is inherently wrong.

In most of the cases, the structural model was build simply before current wells were drilled. In other cases, the structural model serves only as a general guide for major low res trends of the geometry. Whatever be the case, the 3D grid has to have surfaces honoring well tops. If you use a legacy structural model, make sure you apply smooth, gentle surface deformations so the structural model surface passes perfectly through the well tops. Check also for spikes, ripples and abnormal behaviours.

It is not enough to rely on a high resolution 3D vertical model to capture vertical barriers to flow and pressure communication. Check the cap rock and its integrity well by well. If you have a horizontally continuous thin shale or a very compacted carbonate, it could behave as a vertical seal, yet it was bypassed and ignored by the 3D geo-cellular model.

You should verify carefully the vertical seals to fluid flow and pressure, and if needed, apply correct transmissibility factors between vertically adjacent cells to ensure a sound simulation model.

The GeolOil petrophysical summaries module allows you to compute and interpolate coalescence and vertically continuous shale thickness between reservoir beds to specify transmissbilities. If you are simulating a coupled thermal-geomechanical process, like SAGD (Steam Assisted Gravity Drainage), take into account that shales up to 50 cm thickness (or even more), could be dehydrated and broken under the normal thermal process operations, allowing vertical communication.

When you study which exploitation technologies are suitable to exploit a reservoir, you not only have to answer if a particular technology is applicable to the reservoir, but also where in the reservoir you can apply such technology.

Make sure you got the right property. For instance, if you are screening if a SAGD process is applicable, the key property to study is the continuous-pay thickness, not the regular net-pay. Some packages allows to build maps of net-pay, but GeolOil petrophysical summaries module allows you to compute a continuous-pay thickness.

All reservoirs are complex. The impression that a reservoir is simple is probably because it has not been studied in full detail. A common example is the use of synthetic relative permeability curves and capillary pressure curves. If someone is performing a simulation history match production, odds are that laboratory curves are not available.

In those cases, make sure that if synthetic curves are used as replacement, they represent the correct physics wettability. While usually many clastic sandstone reservoir rocks are water-wet, carbonate reservoirs can be oil-wet based. Hence the right wettability physics can improve dramatically water history match.