July 2011 The Geomodeling Network Newsletter

The Geomodeling Network Newsletter July 2011

Great to be back……!

The Geomodeling Network newsletter has been conspicuous by its
absence over the last year. There are lots of reasons for this but in
particular it’s been down to a lack of time on my part and a lack of input
from our members. That said I am planning to resurrect this bi-monthly
amateur publication and who knows, maybe some of you would like to
contribute some articles….come on, you know you want to!

For those of you that are interested, the 2,000th Geomodeling Network
member was Steve Wilson, a Principal Geoscientist who works with
Gaffney, Cline & Associates. Steve will receive his coveted ‘prize’ through
the mail shortly.

In order to encourage you to submit articles I have decided to award a
‘mystery prize’ to the best entry received for the September 2011
newsletter (Steve, no point in you submitting unless you’d like a 2nd
‘coveted prize’ without the mystery). So if you have any
thoughts/articles/rants you would like to include as part of the next
newsletter, please feel free to email them to me at
mitch.sutherland@blueback-reservoir.com and I’ll do my best to ensure
they are included.

This month’s articles are based around new workflows and new
geomodeling technology as well as some recent debates taken from our
discussion page – hope you enjoy them.

Cheers for now,

Mitch Sutherland (mitch.sutherland@blueback-reservoir.com)

Page 1 The Geomodeling Network – Sponsored by Blueback Reservoir www.blueback-reservoir.com


Table of Contents

Sign that you may be a geologist #1

You have ever had to respond "yes" to the 1. QC of upscaled logs – how accurate is your Petrel model?
question, "What have you got A workflow to ensure that the logs we upscale are a good representation of the
in here, rocks?" original well-log data.
René Dam Pedersen – Senior Geomodeler at Blueback Reservoir Page 3

2. What is the best way to generate Sw grid in absence of a J
function? (article taken from the Geomodeling Network discussion board) Page 13

3. Petrel Corner – handy tips & tricks
A quick and easy tutorial to carry out some regularly used functionality in the
Well Section window and the Well Section template
Isabel von Steinaecker – Geomodeler at Blueback Reservoir Page 14

4. My model is good your model is bad. (Article taken from the
Geomodeling Network discussion page) Page 26

5. Blueback releases the Reservoir Engineering Toolbox for
Petrel
The fourth Toolbox created by Blueback Reservoir is a great set of tools that
complement Petrel RE. Page 34



Member Articles, Reviews & Questions

1. QC of upscaled logs – how accurate is your Petrel model?
A workflow to ensure that the logs we upscale are a good representation of the original
well-log data.
René Dam Pedersen – Senior Geomodeler at Blueback Reservoir

QC of upscaled logs
-Description of QC issue
-How do we do the QC today?
-A QC solution

A geologist was accused for
throwing a lava rock at a -Description of QC Issue
tourist.
When we build a reservoir model, we sample the well logs into the grid cells and we get
He's been charged with "basalt
a set of upscaled logs. How do we make sure, that these up-scaled logs are a good
and battery."
representation of the logs we up-scales from? Since we are populating reservoir
properties using the up-scaled logs as a basis, it is very important, that the basis is right!

Up-scaled logs (Blocked logs) are the basis
of property modelling, so it is very
important that the basis is right to have
confidence in the populated reservoir
properties!

How do you make sure that what comes
out of the model is the same as what
went into the model?

-How do we do the QC Today?



There are two ways we try to do the QC of upscaled logs today, by comparing statistics
"If I could remember the
names of all these particles,
from logs and upscaled logs...and sometimes we try to calculate vertical columns in wells
I'd be a botanist." from the properties.....but is this possible in Petrel?
— Albert Einstein
Comparison of Statistics:

After an upscaling of a log, we look at the statistics of all wells in a batch and compare
log mean with upscaled log mean, and if the numbers is not too different we are ok....or
are we? We do it for each property, but do we do it well by well by well and zone by
zone? If we do, it is a very labour intensive process, so most often we look at statics for
all wells in a batch and do not have control of the quality by individual wells.

...and when we have calculated properties (calculated in property calculator) e.g. Sw
calculated from sat-height function, we do not get the statistics of the well logs and the
comparison is very difficult



... the brain is no stronger than
its weakest think ... Evan Esar

Sign that you may be a geologist
#2

Your rock garden is located
inside your house

Calculation of Vertical Columns:

often calculation and comparison of HCPO columns from logs and properties is a
requirement in peer reviews......but can we actually do the calculation in Petrel?

Peer reviewer: "Did you calculate the vertical HCPV column in the wells from the model
and compare them to the petrophysicists calculation from logs?"

You: "well....yes I tried, but calculations are not accurate..... however the numbers are
close" or "No, it is not possible to do the calculation in Petrel"

We try to calculate the vertical column by calculating HCPV metres in cells
(cell_height*N/G*PHI*(1-Sw)) and do a "vertical sum" operation and read of the colunm
heught by clocking on a cell penetrated by the well....there are two problems in this
method, the cell_height calculated in Petrel is the cell height through the cell midpoint
and not the the height of the well penetration of the cell.....and the "sum vertically" is
not vertical, but along IJ columns, so if the IJ columns are not vertical it is not a vertical
sum, and it is definitely not the sum along the well trajectory.



#3

Your photos include people
only for scale and you have
more pictures of
your rock hammer and lens cap
than of your family

...we have tried to sample grid values into the well tops spreadsheet........does
not work!

Sample average
property values into
well tops spreadsheet
and calculate vertical
columns

An accurate column height calculation and comparison can be done outside Petrel by
exporting well tops, synthetic well tops from the grid, logs and synthetic logs from the
grid to excel, where the calculation can be set up and differences can be calculated



Generate synthetic logs and well tops from the model, export logs, synthetic logs, well
tops and synthetic well tops to excel and do the calculation here:
#4

You have never been on a
field trip that didn‟t include
scheduled stops at a
gravel pit and/or a liquor
store

....but it is a very labour intensive process and the method is very prone to human errors
like typos or accidentally hitting the wrong button....but it can be done if you are an
experienced excel user



“Reality is merely an illusion,
albeit a very persistent one.”

Albert Einstein

-QC of upscaled logs – solution
If A equals success, then the Can this QC be done fast inside Petrel minimizing the mouse clicking and minimizing the
formula is: A = X + Y + Z, X risk of manual errors (mistypes, hitting wrong buttons etc)
is work. Y is play. Z is keep
your mouth shut. Petrel Plug-in QC Tool

Albert Einstein Blueback has developed a plugin, that does the columns height calculation and
comparison between logs and properties, it is easy, fast and accurate...here is the user
interface



“Everything that can be
counted does not necessarily
count; everything that counts
cannot necessarily be
counted.”

Albert Einstein

the well is dropped in, well tops to define zones for the logs, the logs (net flag, phi, sw
and perm) are dropped in, the grid and corresponding properties are dropped in, and
you hit apply (option to drop in FWL if well is cut by a FWL)



"Statistics: The only science
that enables different
experts using the same
figures to draw different
conclusions."
Evan Esar

and here is the result spreadsheet with height calculations from logs zone by zone and
total, height calculations from properties zone by zone and total, absolute differences
and percentage differences and issues are easily spotted

Page The Geomodeling Network – Sponsored by Blueback Reservoir www.blueback-reservoir.com
10


I don’t want to go into details of the calculations, but I want to mention, that for the log
calculation we’ve tried to mirror the calculation in Geolog, where the log sample value is
assigned to the TVT interval below the sample.

The TVT calculation of the up-scaled logs is done using the correct well intersection of
the grid cells, and the summation is done along the well trajectory

“The guy who invented the
first wheel was an idiot. The
guy who invented the other
three, he was a genius.”

Sid Caesar

If the following wells have the same logs, you just need to drop in the well, and the user
interface will detect the logs......hit apply and you get the result sheet for the next well.
Results are saved in the input window

11


“Don‟t worry head. The
computer will do all the
thinking from now on.”

Homer Simpson

Summary
• QC of up-scaled logs used to be difficult and tedious and inaccurate
• With the new plug-in, the QC is fast and accurate
• Calculation of vertical columns from both logs and grid properties is easy, fast
and accurate
• This QC is a standard QC for peer reviews, and now it is actually possible to do it

12


2. What is the best way to generate Sw grid in absence of a J
function? (article taken from the Geomodeling Network discussion board)

I have generated Sw grid through petrophysical modelling process and corrected it for
oil and water leg. But I found that some of the high N/G areas are showing high Sw in oil
leg. How can I correct this?
Thanks and regards

Paramita Agarwala

Oliver Torres • Hi Paramita,

What kind of algorithm and process are you using for the gridding? Neither kriging or
simulations are suited for Sw. I would suggest you to try to find a polynomial correlation
between Sw and Por - height above contact. Choose wells that are at different height
then the transition could be characterized. You can export all log_uspcaled properties (
Por, Sw and h) into excel and perform the multivariable correlation. It is important you
have a chat with your petrophysicists.

Jose A. Villasmil M. • Hi Colleagues, I agree with @Oliver Torres in using the physical
relationship between height over contact and the petrophysical properties to estimate
Sw. By doing just a SGS procedure we are neglecting the real distribution which is an
equlibrium between capillarity and gravitacional forces. But Oliver one question, how
can I export log_uspcaled properties ( Por, Sw and h) into excel in Petrel for example?
Thanks

Javaid Afsar • Hi Oliver, your suggestion seems me a good way to model Sw. I have
never done it before. Appreciate if you please provide an example work templete of
your project. This can make it easier for me. My email is geologistjavaid@yahoo.com.
Thanks

Trisatya Nugraha • Jose, to export well properties from model to excel you should do
“To start, press any key. "make log" on well folder operations, choose which properties, then you will have
Where‟s the „any‟ key?” upscalled properties on wells in input data tabs, then export it to ASCII, dont forget to
Homer Simpson
consider layering thickness as an increment of upscalled properties log. cmiiiw.

Oliver, so that your suggestion will resulting an equation and simply apply it to model?
i ever do trial using "assign value" operation in modeling, and the value it self is a simple
vertical function that considering Sw (assumed as initial) to height above contact, so that
transition zone is captured, but i havent do detail recheck on result.

Paramita Agarwala Kumar • Hi all thanks a lot for your help. Oliver I tried to follow your
suggestion but I could not generate a proper function because the sequence is quite
heterogeneous or may be I could not do it properly. I would also request you as Javaid

13


that can you please provide an example work template to me of course if you don't have
any inconvenience?
If you think there is safety in
numbers, try playing roulette. Oliver Torres • Trisatya, Yes it will be a multivariable regression that you can set up in
the property calculator of the same grid where the properties were exported using
Evan Esar
'Make Logs' as you mentioned. It is important to keep the consistency of the support or
cell volume. Once you generate the 3D Sw you need to QC the results. Regards.

Patrick Wong • Oliver's original suggestion on "chat with your petrophysicists" is
definitely a good start before entering into the Petrel realm. I would also add "chat with
your reservoir engineers" and get an agreement on the SW-H function and saturation
end-points to be used.

John Kunka • Whatever method you use make sure you QC the function against Sw
generated from logs e.g. Archie or similar. If the sequence is highly heterogeneous it
would help to classify Sw by rock type based on porosity/permeability classes. It maybe
that you have an field nearby with a similar reservoir that you could use as an analogue.
However, best to use the data from the field you are working on to generate the
function.

3. Petrel Corner – handy hints & tips?
A workflow to ensure that the logs we upscale are a good representation of the original
well-log data. Isabel Von Steinaecker – Geomodeler at Blueback Reservoir

14



You have ever taken a 22-passenger
van over "roads" that were really
intended only for cattle

15


“If you can't explain it simply, you
don't understand it well enough”

Albert Einstein

16


“Technological progress is like an axe
in the hands of a pathological
criminal."
Albert Einstein

17


“Logic will get you from A to B.
Imagination will take you
everywhere.”

Albert Einstein

18


“Education is what remains after one
has forgotten everything he learned in
school.”

Anon

19



You have ever found yourself trying
to explain to airport security that
a rock hammer isn't really a weapon

20


21


22


I‟m a philosophy major. That means I
can think deep thoughts about being
unemployed.

Bruce Lee

“Science is not a sacred cow. Science
is a horse. Don‟t worship it. Feed it.”
– Aubrey Eben

23


“The Romans would never have
conquered the world if they had to
learn Latin first.”

Heinrich Heine

I am not sure how clouds get formed.
But the clouds know how to do it,
and that is the important thing.

– 6th grade science student

24


“Make crime pay – become a
lawyer.”

Will Rogers

25


“An optimist is just a guy who 4. My model is good your model is bad. (Taken from the Geomodeling Network
has never had much experience” discussion page)
Don Marquis
3D static models come in a large variety of shapes and sizes and are built for numerous
reasons with many different types of software. Models are often used to quantify
geological knowledge but assessing whether a model is “good” seems to be a subjective
assessment. Yes there are various ways to QC a model to determine if it honors the well
data and yes each model is only one possible point in the range of possible outcomes.
However I am often asked if a particular model is a “good” model or “bad".
With the software available today it is difficult to build a model that does not honor the
faults, depth surfaces and well data. But I don’t believe that all the resulting models are
“good”. So I’m trying to establish some basic criteria for declaring a model good /
acceptable . So far I have only settled on 2 criteria, both of which are subjective.
1. Is the model fit for purpose?
2. Would the resulting volumes be acceptable to a SPE certification auditor?
There are a lot more criteria that can be applied specifically for development models,
but I’m trying to establish a general set of criteria so will park the specifics for now.
My question is “can models be judged to be good or bad” by an agreed set of criteria?

Patrick Wong • Since the model is fit-for-purpose, criteria cannot be easily generalized....
If the model is good for reserves booking, it doesn't mean that it is a good model for
performance prediction. If the model is used for performance prediction, the very first
thing that comes to my mind is... Can the model be initialized in the flow simulator??
(just too often to see geologically realistic models that have fluids moving up and down
at time zero!!)

Thomas Jerome • Hi Andrew,

When I create a 3D static model, I analyze it following three angles that are both
“That‟s the spirit, George. If complementary and independent one from the other. Your two criteria (purpose ;
nothing else works, then a total volumes) fall into my third angle.
pig-headed unwillingness to
look facts in the face will see us
through.” Firstly, the model must reflect the conceptual geological model that the team has. At the
end of the day, a geomodeling package is only a set of tools: you can have pushed the
– General Sir Anthony Cecil tools to the maximum of their strengths and weaknesses, but if the resulting model
Hogmanay Melchett, doesn't "talk" to the team, then it is a bad model. Of course, the vision of team can be
‘Blackadder Goes Forth’
bad, but that's then beyond the scope of your question I believe.

Secondly, while I use the tools, I have to make sure I use them right. This means I have
to understand them, knowing what are the input, what are the output, what I should
QC, what are the known artifacts of the algo. Examples:
* When I create a surface with an interpolator that works by convergence, I make sure
that the final geometry is not an artifact due to a convergence problem.

* If I use geostat (SGS for example), I check that my histograms are indeed respected.

26


* ...

Thirdly, the purpose of the model is indeed a key factor (I completely agree with
Patrick). This implies parameters such as relevant cell size, grid orientation, export
format, "did I oversimplify the model?", "is it overly complex?", ...

Transversal to all of this is the idea that you have to keep control of your tools. If you
have the impression that you analyze the results from a tool through the angle of "the
software gives me this result ; I don't understand it but that's ok because the tool did it
so it is correct and I'm the problem", then you are heading for troubles and quite
certainly the result will be a bad model.

Steven Trueman • Easier said than done, but i have seen and occasionally use a detailed
100 point questionaire which tackles the main issues around model design, uncertainty,
competency, audit and technical rigour. Each point is then flagged using the traffic light
system and too many reds obviously flags a bad model. Unfortunately it is not available
publicly but it at least gives you an idea what other companies may use.

Oliver Torres • Andrew,
Very interesting theme.
From my point of view a model can be checked quantitative and qualitative.

Quantitative quality controls can help you to check the reliability of a model.
Read by model a 'simplification of the structural and geological setting by using
deterministic and/or probabilistic methods. Appart of the simplifications (not enough
available information or time resources), the reliability of the conceptual model is
The goal of science and fundamental, then it is better to QC three scenarios rather than a base case static
engineering is to build better model.
mousetraps. The goal of
nature is to build better mice. There are several controls within different items of the static model;

- Structural, zonation
- Layering resolution
- Log upscaling
- Histogram
- Data analysis
- Transformations
- Body dimensions, shapes and/or variogram size
- Gridding algorithm

Most of the operators have 'static model checklists'. These checklists sometimes might
help to identify possible flows but it is in function of the experience of the auditor.

In order to perform a 'static model checklist' geoscientist must have experience and
knowledge of the geological setting and have a strong knowledge of the main key
uncertainty factors where the model should integrate the most of the available
information. Combined with the geologist experience, there is a need of knowledge of
27


geomodeling techniques and tools and awareness of the posssibilities of the sofware.

Going through a static model checklist can be very subjective; Two geoscientists in
charge of auditing models can come with very different results i.e

Item Number n-1: OUTPUT HISTOGRAMS

Geoscientist A: Red flag; the petrophysical histos do not honour the well histos.

Geoscientist B: Green flag; petrophysical histos positively diverge from well histos in
accordance with the impact of an input horizontal 2D trend that increases porosities at
proximal areas (proximal areas contain more volume than distas areas).

Moreover output histogrmas are in function of layering scheme, log upscaling method,
size of cells, declustering ...

Another point is that a model might seem in good health after a static model checklist
but sometimes it is forgotten that neither petrophysicists or geophysicists have perform
a health check of thir own outputs that will be used as input for static models.

Steven Trueman • As i said easier said than done. Obviously, the checklist process
requires an interview with the modeller; and if the red flag can be explained then it will
turn green.

Per Olav Eide Svendsen • To a large, I agree with the points put forward by Thomas J.
Mainly, there is the "what" and then there's the "how". Possibly also the "why" should
be taken into account...

I believe that a geomodel must be based on multidisciplinary efforts. I believe that if an
entire subsurface team is able to stand behind the model (techniques, and results) -
then it is probably a good one. Similarly, if a geomodeller is not able to iterate with the
other disciplines during modelling, and if no other disciplines are involved in the
modelling, odds are that the resulting model might not be a "good" one.

On the sixth day, God created History match might also be a criteria for determining if a model is good or not.
the platypus. And God said:
let‟s see the evolutionists try A checklist is always nice, because it quantifies stuff. Seems to me that most companies
and figure this one out. use those. But such checklists (which also in most cases seems to produce traffic light
colours) must take into account not only modelling technical issues, but also the input
data, conceptual understanding and so on.

Example: If no one understands the geology of a specific area, a geological model will
probably not be a good one even though every available best practice is used in the
generation of the model...

Of course, Oliver makes a good point about input data quality. Garbidge in - garbidge
out (but it will still

28


Tim Wynn • This is a great thread and I think l all the main points have been addressed.
My synopsis would be;

Define the modelling objectives as tightly as possible to help determine whether a
model is fit for purpose.

Volumetrics are not a universal criteria for a 'good' model.

The quality and relevance of the implicit assumptions and default practices made during
the modelling process should be reviewed. These are often overlooked but can have a
significant impact on what looks like hard data to match to. A classic one is assuming
that the well is always in the right place...

Models can contain errors of conceptualisation and errors of implementation. The latter
are usually easy to spot and fix if a rigorous checking process is adopted. Because
concepts are subjective, their errors are harder to find and fix but should always be
reviewed.

Well data does not always need to be honoured in the sense of whole model histograms
= well data histograms. If the well sampling is biased and there are robust concepts and
or good quality seismic data control there may be significant divergence,

Iterate as often as possible between all disciplines, The result will nearly always be
better.

Olugbenga Oni • To a large extent the definition of a good or bad model is subjective
and the answers would vary depending on who is consulted.
To ascertain the correctness of a model depends mostly on the integrated objectives of
the model and of course the availability of fit for purpose data.

A model built to illustrate the facies distribution might not neccessarilly honour the
observed fluid flow dynamism of the reservoir.

Therefore an appropraite model will be one that fulfills the objectives

Oscar Rondon • Hi All

This is a very interesting thread.

A colleague recently provided me with an article about global climate change that
discusses the validation of climate models (Edwards P, Global climate science,
uncertainty and politics: Data-laden models, model-filtered data, Science as Culture,
2010)

I think that what it is mentioned in this article is applicable to geoscientists in general
and suits well the topic of this thread, so I would like to briefly share some of these
concepts with you all and know your thoughts on this regard.

29


Oreskes et al (Verification, validation and confirmation of numerical models in the Earth
sciences, Science, 263:641-6) based on results from philosophy of science considered
the followings: 1) Verification, 2) Validation, 3) Confirmation and 4) Evaluation

* Model Verification

In their view verification implies definitive proof of truth and they argue that the fact
that a model agrees – even perfectly - with observations does not guarantee that the
principles it embodies are true. The possibilities always remain either that some other
model could explain the observations equally well, or that future observations will not
agree with the model

* Model Validation

Is less stringent and refers to the demonstration of internal consistency and absence of
detectable flaws. Therefore, the model could be valid without being an accurate
explanation

* Model Confirmation

They argue that models can be at best “confirmed”. This term implies only that the
model results agree with observations. A confirmed model raises the probability that the
model embodies true principles but ca not confer absolute certainty.

* Model Evaluation

Defined as assessment of the degree of correspondence between models and the real
world they represent.

All in all, models can be either “evaluated” or “confirmed” which is consistent with
Popper’s doctrine of falsificationism: Models can be proven false by observations but
cannot be proven true.

Therefore, looks like the easiest part is to identify a "bad" model ...

Regards
Scientists have shown that the
moon is moving away at a tiny
Steven Trueman • I think the validation and verification theme is also a good starting
yet measurable distance from point. Some quick look suggestions: -
the earth every year. If you do
the math, you can calculate VALIDATION - verify concept, structure and properties are valid and physically
that 85 million years ago the reasonable
moon was orbiting the earth at
a distance of about 35 feet
Conceptual model; consistent with regional and analogue information etc?
from the earth‟s surface. This Faults – eg is fault type reasonable in tectonic setting?
would explain the death of the Maps - do they look sensible, geological?
dinosaurs. The tallest ones, Well Test comparison e.g. perm thickness and/or HCPV ft height
anyway.

30


VERIFICATION - verify structure and properties are consistent with available data
Faults - check for rollover and consistency of isochores and fault lines
Check well ties, input and grid horizon consistency.
Maps – visual check (eg HCPV height versus wells)
Ntg, Por, Sw histograms etc compare at raw, block and model scale.
Well perm thickness vs well test analysis and comparison
Confirm Poro/Perm corrections (air to liquid, overburden etc)
Volume consistency – backout averages and box calculation
Well Test Comparison (Perm height)
Dynamic simulation history matching etc

Christian Höcker • Some more food for thought …

There are fit-for-purpose models and over-optimized models. Whether a model fits the
first or the second label is mostly a question of the life expectancy assigned to a model,
and somehow we tend to forget that there might be model life beyond the next
milestone (reserves determination, HC development plan, the next batch of wells …). It
is exactly the same problem CEO’s have at company scale – when maximizing results
which period should be considered?

So, some over-engineering of models is not always that bad as the future tends to hold
surprises; highly optimized models are unlikely to provide answers to these surprises.
And if the plans for next 3 years or so can be considered quite stable then it might do
good to see whether the master - child principle can be applicable, i.e. building a rather
comprehensive model (lateral & vertical coverage, # of faults, amount of properties)
that can serve as the master or reference model for integrating a significant volume of
available information, and then deriving child models from the master, smaller like with
limited vertical extent, with different resolution and orientation, more flexible and very
much for-purpose – but not uninformed.
“Scientists travel into jungles
to study cannibals, crawl into
active volcanoes, play with BTW: It is the latter that JewelSuite is really good in …
dinosaur bones, and blow
things up! How can that be Per Olav Eide Svendsen • But if a model is not identified as "bad", is it then "good"?
boring?”
I think the Popper-based approach to determining if a model is a "good" or a "bad one is
– published comeback to a 6th an interesting approach. Following the same research principles as any scientific theory,
grader who says, ‘Science is
boring’
it would have hypothesis set up to test single scenarios aiming to falsify the
theory/model. However, all models can easily be falsified as pointed out. Because they
are only models. A model can fail to predict details of reality, but still be good models. It
all depends on the purpose of the model.

How many has experienced that a geomodel is built for one purpose, but then used for
another purpose? My point is that a good model quickly turns bad when the purpose
(and initial assumptions) are ignored - is it then still a good model?

Models may break down when new data arrives, hence the model might be called "bad"
(because it failed to predict the new data). But this is only valid if the new data was
assumed existing and missing, and if the model was built for predicting the observations
31


in the new data.

Example: Darwins theory of evolution will probably break down if life on other planets
are encountered. The principles of evolution might not apply to E.T. So Darwin is proved
wrong, theory of evolution is abandoned? No, because the theory was build for life on
Earth, hence it is only valid for describing evolution on Earth. If one were to assume that
the theory would also work on Mars, it might fail. Same thing applies to geomodels, the
understanding of the purpose is essential for making a good, or bad, model.

David F. Machuca-Mory • Here's is an interesting paper on the subject by Naomi
Oreskes:
http://history.ucsd.edu/_files/faculty/oreskes-naomi/PhilIssuesModelAssessOreskes.pdf

Damien Thenin • Cross-validation
----------------------
Cross-validation is a good way of assessing how good (or bad) a model might be.

Assuming the structure and stratigraphy are correclty modeled, one of the most
important part of the model to check is the facies. The petrophysical model is facies
dependent... so a geomodeler needs to make sure his facies model is good.

To cross-validate the facies, you can estimate the facies probabilities predicted by the
model at every well location using a jackknife approach (in which you simulate one well
at the time using the other wells).

Easier said than done, since it can be quite cumbersome to implement it in commercial
software. You will need a good macro/wizard to automate the process.

Reservoir modeling goals
------------------------------------
There are three main reasons why people build reservoir models:
* uncertainty assessment,
* resources assessment,
* flow simulation.

The modeling requirements & workflow will be quite different for each goal. So if you
are using a static model checklist, it could be a good idea to create one checklist for each
type of goal.

For example asking the geomodeler if a spatial bootstrap has been done on the input
parameter is important for uncertainty assessment, but might not be relevant for flow
simulation.

32


Andrew Pitchford • After following the great discussion and reading some of the papers
mentioned I have come to a conclusion of sorts. Inherent in all models are uncertainties,
errors and omissions. The relevance of these can be negated with accurate framing of
model objectives and statements outlining if the offending elements are critical or not
critical to the model. Subjective statements regarding model quality are always going to
be difficult to support. Objective terms such as verified and validated models are useful
if there is a strong consensus about what they mean. Steven’s definitions based on
Oscar’s comments are a good start.
My line of thought has gone down a slightly different, but possibly complementary,
path. Assuming we have clearly stated the objective of the model and we have run
through validation and verification of the model following Steven’s checklist, how should
we now rate the resulting model? I propose 2 more criteria;

Technical divergence:
*High; model results do not agree with significant portions of the observed data
*Mid; model results agrees with majority of observed data with some errors
*Low; model results agrees with majority of data with no significant errors
Conceptual divergence
*High; model does not cover mid case scenario
*Mid; model does not cover high and low alternative scenarios
*Low; model covers all reasonable scenarios adequately

Alan Gibbs • Static models can easily be verified against static data, however the key
issue for geological models is whether the static model is valid relative to kinemaitc
evolution of the geological system.
For this reason a systematic approach to balancing and establishing a valid kinematic
evolution for the model in 2d and 3d is an essential best practice step. Models which
honour the available data are commonly fail the test of geological and kinematic balance
“You can no more win a war even with good seismic and well control.
that you can win an 4d geological models are normally under constrained and it is possible that there is more
earthquake” than one valid scenario and model. A reasonable seach of the scenario or solution space
should be carried out to identify solutions that will potentially change outcome and
Jeannette Rankin decision. By far the biggest element of uncertainty and error in most intepretations at
both regional (exploration) and local (field) scale lies in the geological concepts used to
constrain the interpretation rather than details of model topology.

33


“Political convulsions, like
geological upheavings usher in
new epochs of the world's 5. Blueback Reservoir releases the Reservoir Engineering Toolbox
progress”

Wendell Phillips

Speed up and expand your Petrel RE workflows

The Blueback Toolbox suite of Petrel* plug-ins contains Petrel functionality features not
available in standard Petrel. It has been developed by the Blueback Reservoir
development team using the Petrel development framework called Ocean*. All
functionality has been developed based on requests from Petrel users around the world
and the development is coordinated with the Petrel software teams at Schlumberger.

The Reservoir Engineering (RE) Toolbox contains a number of small plug-ins filling
functionality gaps in Petrel. The focus is to provide Petrel users with functionality
features speeding up and improving the various reservoir engineering workflows. This
also includes functionality for data QC and plotting.

The tools include functionality for dedicated Sw-modeling, 3D grid checks, fast well
track generation, and other tools in the Petrel reservoir engineering domain. The RE
Toolbox gives you a rich set of tools, where our frequent releases provide a steady
increase of functionality based on feedback from Petrel users.

Is there specific missing features in Petrel that you would like to add? Get in touch with
us to discuss how we can help you and your company to optimize your workflows and
Petrel usage.

Quickly create multiple vertical wells with the interactive well picker The 3D grid QC tool provides a range of geometry checks of
the 3D grid cells

34


“We are like a judge
confronted by a
defendant who declines
to answer, and we must
FUNCTIONALITY HIGHLIGHTS
determine the truth from
the circumstantial
evidence.”  The Sw modeling calculator is a much needed tool for Petrel users. It is designed as its
-Alfred Wegener
own process step, and provides functionality for estimating J-curves based on your log
data. Then the Sw calculator uses the J-curves together with your petrophysical
properties to model a Sw property. This tool provides a straightforward and fast solution
to a very common workflow for most reservoir modeling projects
 3D Grid QC is an easy way of investigating the quality of your 3D grid. It features a set of
new geometrical modeling properties all tied into an easy-to-use interface. The user has
the option to create new properties in the model, or to just export the QC results to a
text file. In addition it is possible to include the characteristics of existing grid properties
to a text file report. As irregular grid cells can have an unwanted effect on reservoir
modeling and flow simulation algorithms, this tool is ideal for quality checking your grid
 The Interactive well picking tool lets you quickly create vertical wells in the 3D/2D
windows by clicking at a data object. The well path lengths can be specified, or it can be
limited between surfaces. The tool speeds up the workflows where you want to test
your simulation cases using different well positions for injectors/producers
 Back calculate contacts. Moving fluid contacts can be easily obtained from simulated
saturation distributions. Contacts are represented either as a 3D property model or a 2D
surface
 Auto generate RE plots from multiple cases. Line plots of production data in the active
function window are output in a csv format that is immediately suitable for reporting
purposes of quantities like fluids-in-place and production rates. The output format does
not require additional processing and re-formatting in spreadsheet applications like
Excel but allows for direct comparison between many different simulation cases on any
quantity of interest.
 RelPerm generator. Next to generating standard Corey type relative permeability
curves, the RelPerm generator allows to generate LET-type of relative permeability
curves as well. Furthermore, capillary pressure curves can be generated for water-wet,
oil-wet or mixed-wet rock.
 Rename multiple cases. Save time by renaming multiple cases in one operation.
35


Fin

36

July 2011 The Geomodeling Network Newsletter

Recommended

Recommended

More Related Content

Featured

Featured (20)

July 2011 The Geomodeling Network Newsletter