Bruce Nielsen
Josh Bolling
Semester Project
In order to construct the models, we first had to use the given data to create possible
adjusted values for permeability for some of the wells. In order to do this we created cross plots
of permeability and porosity for wells 2 and 4, since those wells had all the data given. From
these cross plots we created cloud equations that allowed us to create the adjusted permeability
values based on porosity for the wells without permeability data. We took the average porosity
value for each well by facies type and compared it to the average values of both well 2 and well
4 in order to use the cloud equation that fit the data best. This gave us much better results. Then
we took all of the data for all wells and determined that 30% of the data points were facies 1, and
70% facies 0. We used this information when determining values for each grid block in the
“well_info.dat” file, in order to keep the proportions the same as the raw data. After determining
the values for porosity, permeability, and facies type for each grid block, we imported the data
file into SGeMS.
For part 2, we created a file that contained the top and bottom depths of the formation for
every block in the x-y direction. These values were used to find the thickness of each gridblock
in the reservoir. In order to obtain the top and bottom depths for the formation we had to use
ordinary kriging. In SGeMS we set up the kriging using the inputs given, and used conditioning
data with a minimum of 0 and a maximum of 20. Our search ellipsoid had a maximum of 200,
median of 200, and a minimum of 50, with 0 degrees of angle adjustment.
For part 3 we used a training image to create a realistic facies model of the reservoir. This
model was conditioned to our well information data, and the seismic data for the reservoir.
For part 4 we used sgsim to find the porosity values for the sand facies (facies 1) and the
shale facies (facies 0). To do this we had to separate our well data into two separate files, one for
sand and one for shale. Then we created two porosity maps, one for each facies at each grid
point. We then used the “cookie cut” method to combine the two porosity maps into a final
single porosity map with the porosity of each gridblock based on the facies of that gridblock. The
cookie cut method consists of placing the values at each gridblock for each facies into one excel
file then choosing the correct value for each individual gridblock based on which facies type the
gridblock is. This gave us a final data file with porosity of each individual gridblock.
For part 5 we used the same process as in part 4, except we did it for permeability and
based the permeability values on the final porosity values for each gridblock found in part 4.
This gave us a final data file with permeability of each individual gridblock.
There are multiple sources of uncertainty in almost every step of the model. In part 1, we
estimated permeability values based only on porosity values. The cloud equation we used does a
decent job, but it is definitely not 100% accurate. Throughout the project we used kriging to
estimate different parameters. It is well known that kriging under estimates high values and over
estimates small values. We are trying to simulate an entire reservoir model with limited data so it
is impossible to get a perfectly accurate model of what is actually going on. By running multiple
realizations of our different properties, we could try to refine our estimations and create more
accurate models. Overall, we believe our model does a good job of simulating the reservoir
based on the limited data we were given.