Introduction
As with most
things, when you learn something you tend to automatically rely on established approaches
and workflows. You’re taught, or read,
how to do things and tend to accept these are the best practice and therefore
should be utilised. This isn’t always
the case.
Petrophysical
evaluation is a case in point, where there are a few legacies which, when
scrutinised, do not have very solid technical foundations. The Effective Porosity System (EPS) is one of
these; however, the vast majority of evaluations of conventional clastic
reservoirs utilises this particular system.
As most
experienced Petrophysicists know, the first fundamental flaw in the EPS is the
estimate of Shale Volume (VSH), from which Effective Porosity (PHIE) and
Effective Water Saturation (SWE) are subsequently derived. If (as is almost always the case) VSH is inaccurate;
then PHIE and SWE are also inaccurate.
This paper is not going to expand on, or discuss in great detail, the
technical pros and cons of the EPS; this can be found in the literature. The Total Porosity System (TPS) is
technically more robust than the EPS and would be preferred so long as the necessary
data has been acquired.
This paper
summarises a workflow that derives technically defensible Low-Mid-High
estimates of VSH, which can then feed into Low-Mid-High estimates of PHIE, SWE
and permeability. The workflows for
PHIE, SWE and permeability will be addressed in separate papers; however, in
the meantime the experienced Petrophysicist should be able to adapt the approach,
described below, to derive a range of PHIE, SWE and permeability estimates.
This “EPS approach”
can be adapted to derive three sets of results via the TPS.
The vast
majority of Petrophysicists’ “clients” tend not to specify a requirement for a
range of petrophysical results. However,
all subsurface technical disciplines should, by default, be deriving a
technically substantiated range of estimates, in order to account for
uncertainty. Sadly, this is not
routinely undertaken. Of course, to do
this requires more time and more understanding.
We should not be producing single, so-called “Base Case” or “Best
Technical Estimate” results, without properly describing the upside & downside
scenarios.
If your client
asks you for a petrophysical evaluation without specifying the porosity system,
or the scenarios required; the likelihood is that the client doesn’t understand
petrophysics or uncertainty; or maybe they do, but their prepared to wing it,
which isn’t really top decile professionalism.
In this situation, it’s incumbent on the Petrophysicist to discuss the
options with the client (and, if necessary, diplomatically with the client’s
Technical Manager); to ensure there’s full understanding of the best porosity
system(s) to use, their implications and the need for a range of estimates. If the client (and TM) choose not to follow
the Petrophysicist’s recommendations, ensure you’ve made your views clear (for
example in an email) and summarise what you’ve been asked to undertake,
together with any limitations these give rise to.
Paul
Worthington has an excellent summary of using the TPS and EPS together in order
to derive what he calls “ground-truthed” results (Conjunctive Interpretation of Core & Log Data Through Association
of the Effective & Total Porosity Models, 1998). His approach is highly recommended; however, a
range of estimates, is still required.
Shale Volume Estimation
As mentioned
above, the estimation of a VSH log is fraught with issues plus, whichever VSH model
is adopted, there’s significant uncertainty around this estimate. This uncertainty is “inherited” into the
estimate of PHIE and then again into SWE, because VSH and PHIE are inputs to
SWE. So, you can see why undertaking a
single EPS evaluation is a risky business.
In order to
mitigate the issues with estimating a VSH log; below is a relatively simple
workflow designed to derive Low-Mid-High estimates of VSH using the Gamma-Ray
(GR) as the input log.
This approach
is not restricted to solely VSH from the GR; the approach can be adapted to
other input logs used to estimate VSH, as well as the input parameters to
porosity, permeability and saturation estimates.
The key
aspect is identifying realistic Low-Mid-High input values for the Shale Volume
model(s), plus Porosity & Saturation model(s).
VSH-GR Workflow
1. Ensure all borehole environmental corrections
have been undertaken and bad log & core data either removed or replaced (I
prefer to null bad data rather than replace it).
2. The available GR data controls the best
technical approach. The best quality wireline
GR data is acquired at the slowest logging speed, which tends to be with the
(pad-based) bulk density tool. So, identify the best GR data over the Zone Of Interest
(ZOI). If Spectral GR is available,
investigate the CGR and SGR over the ZOI.
The Thorium or Potassium logs may be useful, for example, if there are
Feldspars, or not (see Crain for more details: https://www.spec2000.net/11-vshgr.htm
and leave a donation).
3. For the reasons above, ideally evaluate the ZOI
on a well by well basis, but if this is not feasible, for example, due to the
large number of wells; generate normalised input log(s). Typically the GR
database will be a mixture of standard wireline (acquired at different logging
speeds), spectral GR (often over limited intervals) plus LWD data. These data can be quite disparate and not
really suitable for normalisation or multi-well evaluation; hence keeping
evaluation to a well by well basis is recommended.
4. In conjunction with other logs, core analyses,
sidewall core information, cuttings, formation pressure plus DST data;
interrogate the GR response over the ZOI to understand what the radio-activity
represents. As part of this, do other
quick-look VSH estimates using logs other than the GR.
5. Once you have selected the ZOI(s) generate a
histogram and cumulative frequency plot of the best GR log, over the ZOI, such
as the one shown below:
6. Decide on what cumulative frequency percentiles
to use to describe the “Clean,” low GR intervals and the “Shaly”, high GR
intervals. Illustrative values, taken from the distribution above, are shown
in the table below:
Cumulative %
|
GR
Value
|
Class
|
P05
|
20
|
"Clean"
|
P10
|
22
|
"Clean"
|
P15
|
24
|
"Clean"
|
P85
|
81
|
"Shaly"
|
P90
|
92
|
"Shaly"
|
P95
|
125
|
"Shaly"
|
7. The selection of the ZOI, plus the cumulative
percentage values, has a direct impact on the resulting estimates of VSH; so
some iteration is recommended before selecting the final parameters. This is discussed in more detail later on.
8. For each ZOI derive raw VSH GR-Hi, VSH GR-Mid and VSH GR-Lo, using the equations below:
9. Create final constrained
VSH GR Hi, Mid and Lo logs, from the raw logs.
10. Plot the constrained VSH GR logs in a layout
together with the input log(s) and check for suitability. It may be necessary to iterate on the ZOI
and/or the “Clean” plus “Shaly” end-points. The three VSH GR logs, shown below, were
derived from the GR data in the histogram shown above. The Net-To-Gross (NTG) flags and NTG percentages,
based on application of VSH GR<50%, are also shown. The Gross and “Net” interval thicknesses, for
each VSH GR scenario, after application of the VSH GR<50% cut-off are also
summarised:
Illustration of VSH GR-Hi, -Mid & -Lo Estimates, Together With “Net”
Flags, Plus Tabulated Gross & “Net” Thicknesses
VSH GR-HI
|
VSH GR-MID
|
VSH GR-LO
|
|
Gross
Interval (metres)
|
106.38
|
106.38
|
106.38
|
"Net"
Interval (metres)
|
81.24
|
84.13
|
88.92
|
Net-to-Gross (%)
|
76%
|
79%
|
84%
|
Summary
The range in
“Net” thickness from 81.24 m to 88.92 m will tend to outweigh the uncertainty
range attributed to porosity plus, where valid calibrating data for saturations
exist and vertical fluid distribution(s) are well-constrained; NTG uncertainty will
outweigh the saturation uncertainty range.
But, if fluid
contacts and/or fluid types are poorly constrained, then saturations and/or the
definition of Net Pay, can have significant uncertainty ranges; potentially a similar
order of magnitude as NTG. This is why
it’s essential to incorporate fluid contact uncertainty into all petrophysical
evaluations.
The example
illustrated above highlights how sensitive the inputs to the VSH GR estimate
are, in terms of the resulting NTG. Looking
at the input GR log, you could make an argument that the ZOI could be changed;
introducing three zones for example: 3080-3095m, 3095-3155m and 3155-3190m. If you have other data to support such a zonal
breakdown, then subdivision should be done.
This also
demonstrates how inappropriate it is to undertake a single deterministic
estimate.
With a range
of approximately 8m of “Net” reservoir resulting from the same input GR log but
different “Clean” and “Shaly” end-points; it’s essential to derive a range of
VSH estimates.
Discussion
I am not
recommending the workflow above is the be-all and end-all of EPS petrophysics;
it’s not. Worthington’s 1998 paper,
referenced earlier, provides an excellent all-encompassing EPS and TPS
approach, albeit leading to a single deterministic estimate in each system.
I am
recommending that all petrophysics moves away from the single estimate scenario,
to an industry-wide default of deriving: Low, Mid (or Most-Likely, if this can
be achieved) and High estimates of lithology, porosity, permeability and water
saturation. All technical professionals
agree with this, but then don’t tend to implement it!
The workflow,
described above, provides a good starting point for VSH. The approach can be adapted and expanded to
estimate three scenarios of PHIE and SWE, linking with the three estimates of
VSH. The approach can be adapted to
estimate three cases of VSH from other input logs such as the SP, RHOB, NPHI,
etc.
As I’ve
presented elsewhere, I recommend you combine, deterministically, all the
pessimistic estimates of VSH, PHIE and SWE to provide what may be close to a P99
scenario; where there’s a 99% chance the actual values are larger. This pessimistic
scenario is very useful in terms of estimating a “downside” case, which
geomodellers, reservoir engineers and Subsurface Managers really ought to be
considering before investing millions of Dollars in appraisal or development.
It’s
recognised that combining all pessimistic values is unlikely to occur in
reality; however, this is deliberate in order to extend the range beyond the
incomplete data actually used to establish the Low, Mid and High scenarios.
The same
argument applies to the combination of all optimistic VSH, PHIE and SWE
parameters to provide what may be close to a P01 scenario, where there’s a 99%
chance the actual values are smaller. This can be considered an “upside” case,
which should also be modelled for in-place and recoverable volumes.
Lastly, the
long legacy of the EPS and its adoption as the petrophysical model of choice,
probably means the industry will continue using it, even though it is
technically unsupportable!
Instead of
following the petrophysical flock, think about alternative ways of describing
potential reservoir intervals, the inter-connected pore volume and permeability
within the reservoir intervals, plus the fluid saturations.
Is a single
VSH log a good enough parameter to start this process?
No.
Is a range of
VSH estimates good enough?
Not really,
but it’s significantly better than a single estimate.
Can I
estimate (connected) porosity and permeability with reasonable accuracy,
without using a VSH?
If so, then
you can define reservoir and non-reservoir using these parameters and VSH
becomes redundant; wonderful!
The TPS is
technically more robust than the EPS; so planning data acquisition for the TPS
is highly recommended, whereas reliance on the EPS, is not recommended.
You are
welcome to contact me for additional clarification or to share your own
applications, or adaptations, of this approach.
If you’d like
professional input you can find out more and enter your project requirements to
get a cost estimate, at:
Jeremy Daines
Chief Petrophysicist
Oleum
Khaos Ltd
T: +44 (0)1252 416396
M: +44 (0)7852 554496
E: info@oleumkhaos.com
© Oleum Khaos Limited (July 2017)
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