OK - have had a quick/rough look at the data, and uploaded a sheet at the following address *updated*
[link Point to another website Only the registered members can access]
Assumptions:
- Oil/Brine centrifuge test
- Oil/Brine reservoir
- Reservoir oil at 0.825 SG in-situ
- Reservoir brine 1.02 SG in-situ
Notes:
You will never get perfect fits across entire population. Need to critically decide which areas are more important to get better fit it. If your column is only 100ft high, then if the 500ft+ data does not fit so well, remove it from the fit criteria. If you have 99% of your reservoir with Sw<0.7, then don't worry so much if your relationship has a poorer fit with high water saturations. Of course we should strive to get the best fit we can, but in the end you will in all likelihood need to make compromises to better address those areas that are most important
With J-Function, I assumed max hydrocarbon column of 600ft, so did not give any weight to errors in the 1,500ft points
All samples seem to be exhibiting 'normal' type trends - insofar as I'd probably lump them as one saturation function family unless there was some other data to tell me otherwise
Be careful working with Ka (perm to air). Often in situ perm can be an order of magnitude less through overburden correction and rel perm to the primary reservoir fluid flowing. Ensure that you are consistent with how you test/populate/relate all the various perms (ie lab test perm, property model perm, rel perm) so that the final property perm x rel perm reflects in situ reservoir deliverability for your fluid and that your saturation function is used against the appropriate perm value. I'm not saying you need to always work with Ka, or always with Perm to hydrocarbon or water - what I am saying is be consistent through your workflow.
Fit Results (updated):
J = 0.4778 * Sw^-2.356, using Perm exponent of 0.529 and Poro exponent of 0.608 (J = Pc x (Perm^0.529)/(Poro^0.608)/26 )
or if solving for Sw given a calculated J;
Sw = 10^( (-log(J) + log(0.4778))/2.356)
Lambda constants are;
a1 39.08174161
a2 -14.76172542
l1 0.726524565
l2 0.072382779
b1 0.17321564
b2 -0.148686704
For 'no name' relationship;
Choosing 675ft as Swir -
Swir = 0.2581 - 0.06106 x Log(K)
Alpha 1324253.612
Beta 33922.95145
Comparing the results, it appears that both the J-function as well as Lambda functions give very good matches based upon the data furnished to date. This being the case, I'd probably elect to use J-function due to ease of implementation in Eclipse.
With regards to the petrophysics equation - yes, go ask them. If I had to guess, I would translate it as follows;
Swi = .44/(.00766*(Height*.21)*(SQRT(PERM/PHIT))^.212, but not allowed to go below 0.15 or above 1.0
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