Jan 2009 The Geomodeling Network Newsletter

The Geomodeling Network Newsletter January 2009

Welcome to the third installment of the Geomodeling Network newsletter
and a very Happy New Year to you all. The more eagle eyed of you will have
realized that there has been a bit of a gap since our last release – everyone was
busy in Q4 2008 so it’s only now that I have had the time to chase up and collate
the articles. Anyway we are now back on track with the shiny January 2009
newsletter.

One of the major changes since our last newsletter has been a LinkedIn
technology upgrade. This upgrade has given our group members the opportunity
to post discussions or questions on this site (which a number of you have
already done). I have posted a couple of example threads in this newsletter to
give those that have not seen or used these discussion boards what is going on.

We also have the ability to upload presentations. For those of you that have not
received our backdated presentations you will find them by using the SlideShare
feature on LinkedIn. It’s also a very convenient tool if you want to avoid opening
up the newsletter on your email system which in some cases can take a while to
download.

At the Geomodeling Network we are trying to be as ambitious as we possibly
can and when one of our members proposed that we should think about
running a Geomodeling conference for one or two days I was of course very
interested.

The merits of this were discussed very loosely over a few pints and it is still on
the table for Q4 2009. The base idea was to come up with an event that was
primarily made up of Geomodeling case studies, workarounds, success stories or
glorious failure which would hopefully be of interest to our members as well as a
wider audience. If this is the kind of event that appeals to you please let me
know and whether or not you may be interested in either attending or indeed
presenting.

Other than that I hope you all enjoy reading this newsletter and are looking
forward to an exciting and prosperous 2009!

“Lang may yer lum reek, Wi' ither folks coal!”

Mitch Sutherland
mitch.sutherland@blueback-reservoir.com

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


Table of Contents

Member Articles, Reviews & Questions

1. Probabilistic geological modelling: the “gun control” issue of
geomodeling.
Dan O'Meara
Chief Advisor, Reserves at Landmark and Owner, O'Meara Consulting, Inc
(Thread taken from the Geomodeling Network discussion page on LinkedIn)
Page 3

2. Whilst quantifying subsurface uncertainty is recognized as
important in field development planning, it is often not directly
accounted for in the 3D model” – this article outlines an
approach and discusses the advantages of dealing with your
uncertainties directly in the 3D model.”
Alister MacDonald
Technical Advisor at Roxar Page 12

3. Effective porosity vs. total porosity in 3D-models?
Laurence Bellenfant
Lead Geologist at Senergy Ltd Page 21

Career Networking

Subsurface Global Page 28
Blueback Reservoir Page 29

Requests for newsletter No4 Page 30



Member Articles, Reviews & Questions

1. Probabilistic geological modelling: the “gun control”
issue of geomodeling.
Red: In 1966, Andy Dan O'Meara
Dufresne escaped from Chief Advisor, Reserves at Landmark and Owner, O'Meara Consulting, Inc
Shawshank prison. All
they found of him was a
muddy set of prison If ever you are attending a party in Houston, bring up the issue of gun control
clothes, a bar of soap, and and step back to enjoy the fireworks. You are sure to hear vociferous
proponents on both sides of the issue. Well, probabilistic geological modeling is
an old rock hammer, damn
a “gun control” issue for us. In a related discussion, Mike Hardwicke has stated a
near worn down to the nub.
commonly held perception that 3D modelling software...seems to have gained
I used to think it would favour as the definitive technology for capturing the range of uncertainty in
take six-hundred years to static models. Let’s have some fun by opening a discussion that challenges this
tunnel under the wall with Sort of thinking.
it. Old Andy did it in less
From my experience, the image that I have of probabilistic geological modelling
than twenty. Oh, Andy is of people playing dice in a small room. Too often, the resulting models ignore
loved geology. I guess it high impact possibilities that do not fit into the boxes that the software
appealed to his meticulous providers have provided for you to play in. Consequently, those who play only
inside the box are deluded into thinking they understand uncertainties related
nature. An ice age here,
to their reservoir when all they have done is to understand uncertainties related
million years of mountain to the box that they have chosen to play in.
building there. Geology is
the study of pressure and Let me give you an example of what I mean. On one field I worked on recently,
we could construct a model that was in the box. It had multiple rock types and
time. That's all it takes
a single reservoir compartment. This was the type of model that was expected,
really, pressure, and time. ready-made for running multiple realizations. On the other hand, we
That, and a big god- constructed another model that had fewer rock types but that had as many as
damned poster. fifteen reservoir compartments. Both models were entirely consistent with all of
the known data. The second model was definitely out of the box but it fit the
Narrated by Red, The data just as well as the first model. You can think of the second model as actually
a number of models, consisting of consistent interpretations of anything
Shawshank Redemption
between two and fifteen compartments. Now, if you were planning a waterflood



on this field, the second model of a compartmentalized reservoir would have
high impact.

What probability would I assign to the second model? I don't know. In fact, I
think it's very difficult to assign probabilities. In fact, discussions of Gaussian
probability distributions seem completely irrelevant to the problem at hand.
However, it's very important to know that the second model is both reasonable
and consistent with all of the data. In this case, possibility is more important
than probability. Identifying such a possibility is certainly important for
understanding uncertainty in your field. But the job of identifying such
possibilities seems to be relegated to a secondary role because it's a lot easier to
play dice in the box than it is to imagine high impact possibilities.

You might suggest that an Ockham’s razor approach would argue for the first
model to be the one worth spending time on because it is the simplest model
that explains all the data. Well, it depends on who is defining “simple”. For me, it
is simpler to think of compartments than it is to think of rock types. And, in the
field under discussion, the geological model had thirty-five faults that seemed to
cry out for inclusion a simple explanation of compartmentalization based on
fault blocks.

Robert Smallshire
Geoscience Software Development and Structural Geology

I'm much inclined to agree with Dan that the uncertainty modelling techniques
available out-of-the-box fail to capture the full range of uncertainty present in
our understanding of reservoir properties and predicted performance. This is
because of the difficulty of programmatically simulating conceptual
uncertainties - current software systems are good at providing sparsely sampled
distributions of realizations within a single concept (playing dice in a small
room) with the positions of channels or other sedimentary features, but cannot
begin to generate alternative concepts which also fit the available data
(compartmentalized versus continuous, thrust faults versus inverted extension).

Concept generation is left to us humans, who are dogmatic, often insufficiently
well informed of the possibilities or simply too busy to invest significant time in
creating alternatives. What is more, we tend to believe in our current concept,
in which we have a large emotional investment, even beyond the point of it
being untenable in the face of the data: Witness the the discussion over whether
the Silverpit structure in the North Sea is impact or salt related! [1] . I'm aware
of some work that has been done into analysing concept uncertainty and its
impact on seismic interpretation [2], but none of that addresses the topic in
relation to the specifics of static or dynamic geomodels.

Its was once said by my colleague Dave Hardy that uncertainty isn’t commonly
considered part of the 3-D modeling process, even though everyone knows they
should be doing something about it [3]. This is hopefully changing, but



uncertainty modelling continues to be applied largely within the within the
confines of what the software tools easily allow - within a single geological
concept. I predict that until software can impartially explore concept-space for
us, multiple scenario assessment with a worthwhile number of scenarios won't
be commonly done, simply because it's too expensive [4].

[1] UK impact crater debate heats up
http://news.bbc.co.uk/2/hi/science/nature/6503543.stm >
[2] Bond C.E., Gibbs A.D., Shipton Z.K. Jones S, (2007) What do you think this is?
“Conceptual uncertainty” in geoscience interpretation. GSA Today 17 <
http://www.gsajournals.org/archive/1052-5173/17/11/pdf/i1052-5173-17-11-
4.pdf >
[3] Top Down or Bottoms Up? Getting a Grip On Reservoirs <
http://www.aapg.org/explorer/2006/10oct/uncertainty.cfm >
[4] I've been wrong before.

Yannick Boisseau
Senior production geologist- Chief Reservoir Modeller

embedded only sample a space of uncertainty as defined by the geologist or
geomodeller. A commun misconception is that by generating multiple
realisations, one gets to know what are the uncertainties and what is the true
P50. There is no true P50. there is reality one one side and models on the
other side. Depending on concepts you apply during your modelling and
uncertainties you assume on input parameters, you get a range of possible
outcomes. The main biais (but may be not the main risk) is probably the choice
of a concept as Dan is presenting in is case study. The same rules applies with
probabilisitc modelling as would apply with deterministic modelling.

- trash in, trash out

- what you don't put in, you don't get out.

I also hear often the sentence let's keep it simple initially, or simple is better.
I tend to approach this a different way. First try to estimate how complicated it
might be, and then simplify to what really matters for your case.
Dealing with heavy oil or gas will make a huge difference on how the same rock
type will behave to flow and what net pay you might what to consider.

The main benefit for me in uncertainty modelling tools is that assuming a
particular concept for the reservoir architecture, you can assess how your pay
criteria, STOIIP or recoverable oil is sensible to different ranges of input
parameters (different porosity mean & std deviation), rocktypes proportions...
This should be applied on as many concepts as you see relevant
(compartimentalisation or not, as an example). This helps you decide where
your risk lies and where you need to get more data.



Mark Whelan
Development Geologist

Concept design as expressed here is what Design of Experiments is about. I think
the modelling community really needs to do a better job of convincing senior
management and peers that a set of concepts or experiments do a much better
job at capturing ranges of uncertainty than a host of multiple realizations do.

Start off by defining the realistic end members of the scenario/concept and work
towards generating that all elusive P50 and the rest will follow.

The dependent variable in an experimental design whether it be In-Place or
Recoverable variables is arrived at by making realistic assumptions about the
A man goes into a restaruant, constituent variables and that should not be forgotten, so geological knowledge
sits down and starts reading the and empirical values/analogues are a great way to start and can always be
menu. The menu says: defended.

Broiled Accountant $5.95 per Gun control? Let's have more of it!
plate
Dan O’Meara
Fried Engineer $7.95 per
plate
What is the best way to expend money and effort in assessing uncertainty in our
Toasted Teacher $7.95 per reservoir models? Isn’t this the crucial question? For those of us who are
plate mathematically inclined, we find solace in probability theory, geostatistics,
experimental design, and other such constructions. But at the risk of appearing a
Grilled Geologist $25.95 per troglodyte, I would like to explore what they really buy us in terms of addressing
plate true uncertainties. As Yannick points out, our current tools help us to assess the
uncertainties related to varying relevant parameters for a given conceptual
The man calls a waiter over model. But that’s what I mean by “playing dice in a small room”. The real action
seems to be in conceptual models. So, isn’t money best spent on exploring a
and asks Hey, why does the
wide range of conceptual models than it is in exhaustively quantifying
Grilled Geologist cost so much
uncertainties associated within a “small room”?
more?
Ah, but who would be responsible for exploring these concepts? Let’s imagine
The waiter says, Are you that you are attending a conference on uncertainty in reservoir models. Who
kidding? Do you know how would you expect to see in the room? Who would be giving the presentations?
hard it is to clean one of Can you see their faces? Do you have their names in your head? Okay, well how
many of them are mathematical types – well known geostatisticians,
them?!?!
experimental designers, or simulation experts? I imagine most of them.
..............i’ll get my coat!
Now imagine that you are put in charge of understanding uncertainties on a
mega-development project that can make or break your company. What kinds



of people would you want on your team? From my experience with real
reservoirs, I tend to think of marshalling together an interdisciplinary team
where the team members knock around lots of concepts about what might
explain all that is known about the reservoir of interest. The uncertainties come
from analyzing various conceptual models that honor both physics and data,
with special attention to outliers. Instead of investigating multiple, equiprobable
realizations, I think of the teams as investigating multiple scenarios that span, as
Mark says, “realistic end members”. In other words, I don’t think of a team of
uncertainty experts but a team that stimulates right-brained activity aimed at
generating a range of concepts.

Now, once you think of teams and batting around concepts, you’ve got to think
of the psychology of it all, as Robert mentions. When I think of the purported
uncertainty experts, I think of left-brained thinkers who are highly analytical and
highly structured. But when I think of the folks that I’d like on a team that is
looking at uncertainty, I’d like to see a balance of out-of-the-box thinkers as a
better job of convincing senior management of the benefits of a “set of
concepts” rather than running multiple realizations. He is right. But I doubt that
he will make much headway with a frontal assault. Think for a moment of the
managers you know. Would you characterize them as highly analytical and
structured or as out-of-the-box thinkers? I suggest that most middle managers
are the former. So, when middle managers get together with mathematical
folks, it is a marriage made in heaven. Any talk of a trial separation or divorce is
likely to get very messy. Fortunately, studies have shown that as you go up the
management tree, you will find more right-brained thinking.

Please join the discussion and tell me whether I am missing the boat on this.
Because if I am not, then one has to wonder whether the resources expended
on addressing uncertainty issues are misplaced.

Dan O'Meara

Let’s go back to the concrete example that I raised earlier. I postulated a very
reasonable model that is consistent with all data but that has fifteen reservoir
compartments as opposed to one. Experimental design would certainly help us
to narrow down the number of variations of models that need to be studied in
order to get a handle on the response surfaces. But, we hear the term
“probabilistic reservoir modeling”. Well, help me out. How do we assign
probabilities? In understatement, Mark refers to “that all elusive P50”. Yannick is
more direct when he says, “There is no true P50”. Can we assign a P50 in this
case? Are the various possibilities of anywhere between one and fifteen
compartments equally probable? Do we want to exclude the fifteen
compartment case from consideration?



If there is really a consensus amongst practicing geomodelers that “there is no
true P50”, then management does not seem to be hearing it.

Steve Flew
Technical Advisor at Schlumberger

To add some 'customer' perspective to this too, as a reservoir engineer with a
geomodelling background, its galling to see the lengths subsurface teams go to
force fitting observations to a single model, deluding themselves that 'this is the
one'. There is often an apparent breakdown of inter-discipline communication
when it comes to possible scenarios, and often this is made worse in fields with
production history, somewhat ironically.

Dan's comments about right- and left-brainisms matches my experience, if you
get an enlightened asset manager, etc, then they both understand and support
the type of data gathering - try justifying drilling a well off structure or downdip
in many companies and you'll be met with a brick wall. However, find a way to
explain the true range of OIP/reserves and the impact that observation will have
”I think there’s a world through some form of VOI exercise - this requires a few things to be in place:
Realistic input ranges (and no, those points aren't outliers and should be
marker for about 5 discarded) Realistic alternative structural/depositional scenarios
computers.” Acceptance of (some) production/pressure trends that don't fit the
'conventional' model
Thomas Watson (founder of
An ability of the geoscientist/team to convey these issues in an unbiased, and
IBM)
straight forward manner for the 'in-the-box' thinkers, highlighting the impacts
these could have on any development planning decision.

Sadly, whilst we can bemoan in-the-box managers, I suspect much of the issue
lies with our own disciplines - constrained by what some computer scientist has
coded up in software, or by their own background/limited experience,
organisation.

So, to comment on Dan's P50 consensus statement, in my experience even if we
portray a P50, there are others who MISUNDERSTAND what that actually means,
thinking that this is actually a far higher confidence number. I've started to
present ranges in meetings, focusing on the high confidence number, but have
found more often than not that the number people (and not just mgmt)
remember, is the mythical P50, despite insistance that it means half the time we
won't achieve this!

Be interested to hear others experiences!

Guillaume Caumon
Associate Prof at the School of Geology, Nancy Universite



We have to be very careful with some software vendors and some so-called
geostatisticians who claim uncertainty is assessed just by running a bunch of
stochastic simulations. To that respect, yes, we need to go beyond this simple
dice in a small room.

However, the point is not to decide for some objective automatic uncertainty
assessment versus some scenario-based subjective uncertainty assessment.

The uncertainty is all about the lack of information, so trying to assess
subsurface uncertainties just amounts to tell what you don't know from what
you know. Therefore, uncertainty assessment is ALWAYS subjective, and this
subjectivity may originate from the scenario, from the underlying assumptions
of some mathematical / statistical framework, from the particular
software/algorithms your are using, or from all of these. In one case, the
subjectivity is yours, in another case, it comes from others (e.g. data
independence, multivariate normal assumption or iid in stats methods, two-
point statistics in classical geostats, etc.). The problem with software is that the
underlying assumptions are not always explicitly stated by software vendors,
and are not always properly understood by software users, hence the
impression that just experience and one's own subjectivity is preferable, and
easier to discuss (or defend) in an asset team.

Still, software and modeling has proved extremely valuable at integrating data in
a consistent manner from a deterministic standpoint, and is an invaluable
companion of the human brain when it comes to processing large amounts of
information. Then, why should we drop software when we want to consider
uncertainty ? Or should we just make one deterministic model for each
scenario? I don't think so.

The problem of dimensionality and cost of exploring the space of uncertainty
(Robert's point) can hardly be addressed by just a few scenarios. Huge biases
may appear in such a method. This hold especially if the model is simplified from
the start, see Yannick's point (e.g., if we have reduced the number of faults to
generate a grid more easily). In my opinion, the only approach to sample this
space is, as for deterministic modeling, to use software, and understand the
underlying parameters and uncertainty modeling rationale behind them: the oil
and gas industry is high tech and relies on skilled engineers & geoscientists;
hopefully they can spend some time learning new technology.

For now, we can already benefit from a mixture of discrete deterministic models
and automatic perturbations of these models to sample the space of uncertainty
and make relevant decisions from these samples, see for instance refs 1-6.
For tomorrow:

- researchers and software vendors should urgently explore new ways to
stochastically generate realistic models other than by perturbing some reference
model. Ideally, these models should help answer the question at hand and



depend on the subsurface complexity, and should be updated in some inversion
loops (e.g., history matching).

- We will not avoid biiiig supercomputers to tackle this, because our subsurface
modeling problem is ill-posed, and a huge number of models should be
considered.

Guillaume Caumon
Associate Prof at the School of Geology, Nancy Universite

Refs:

1- Massonnat, G. J. “Can we sample the complete geological uncertainty space in
reservoir-modeling uncertainty estimates?” paper SPE 59801, SPE Journal (2000)
5(1):46–59.

2- Hollund, K., Mostad, P., Nielsen, B.F., Holden, L., Gjerde, J., Contursi, M.G.,
McCann, A. J., Townsend, C. and Sverdrup, E. [2002] Havana — a fault modeling
tool, A. G. Koestler and R. Hunsdale (eds.), Hydrocarbon Seal Quantification,
Norwegian Petroleum Society Conference, vol. 11 NPF Spec. Pub., Elsevier
Science, Amsterdam.

3- G. Caumon, S. Strebelle, J. K. Caers, and A. G. Journel, 2004. Assessment of
Global Uncertainty for Early Appraisal of Hydrocarbon Fields. SPE Annual
technical Conference and Exhibition (Houston). (SPE 89943).,

4-G. Caumon, A.-L. Tertois and L. Zhang, 2007. Elements for Stochastic Structural
Perturbation of Stratigraphic Models. Proc. Petroleum Geostatistics 2007, EAGE,
A02, 4p.

5- S. Suzuki, G. Caumon and J. Caers, 2008. Dynamic data integration for
structural modeling: model screening approach using a distance-based model
parameterization. Computational Geosciences 12(1):
105—119.

6- Scheidt, C. and Caers, J. (2008). Representing Spatial Uncertainty Using
Distances and Kernels, Math. Geosciences, in press.
Posted 1 month ago | Delete comment

Dan O'Meara

I appreciate Guillaume Caumon’s remarks, especially about the subjectivity of
uncertainty analysis. Let me begin exploring two of his statements concerning
the “huge biases” that are inherent with a scenario approach and his hope for

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


the future, the industry should learn to “stochastically generate realistic models”
that are not merely perturbations on a “reference model”.

No doubt, you’ve heard (especially in this election year), people complain about
the biases of the media. Well, I am always surprised to think that anyone would
expect to find unbiased reporting anywhere. That’s not a complaint. It’s just a
matter of recognizing that human beings are inherently biased. We bring our
prejudices to the analysis of politics, religion, and (indeed) reservoir modeling –
three topics, by the way, that a gentleman or lady would never deign to bring up
in polite company over dinner. That’s why I began this discussion by saying that
probabilistic geological modeling is an issue like “gun control”. Discussions of it
can become quite heated, especially when basic tenets of the faith are
questioned.

Have you ever talked with a conspiracy theorist? There are people who believe
that the world is controlled by a vast right-wing or left-wing conspiracy. Talking
with them is like talking to a guy who thinks he is the King of England. No doubt,
you can come up with a lot of counterarguments. But, ultimately, all that you say
will be used as proof of the great international conspiracy that exists to prevent
the poor fellow from taking his rightful place on the throne. Well, I find that
discussions about geostatistics can have this flavor about them. There are those
who see the world through Gaussian distribution functions, variograms, and
equiprobable realizations. And, there are those of us who are the great
unwashed that do not share the faith, who are not believers in the one true
religion.

Biases are part and parcel of the human condition. Just as with television or the
newspapers, I expect biases and, in fact, embrace them. When it comes to
understanding uncertainty in reservoirs, I admit that I am biased. I am biased
towards accepting recommendations of teams that have a balance of right-
brained as well as left-brained members, who think “out of the box” when it
comes to understanding uncertainty in reservoirs. Now, I would argue that
people who put a lot of faith in stochastic models also betray “huge biases” even
though many of them are reluctant to think they do so. So, when I read Steve
Flew’s comments that “it’s galling to see the lengths subsurface teams go to
force fitting observations to a single model”, I thought “Amen, brother”. I think
of multiple, equiprobable realizations on the same geological model as being “a
single model”. Guillaume decries the use of “just a few scenarios”. Well, I would
argue that staying in the typical geostatistical modeling box constitutes little
more than one scenario.

Go back to the situation that I posed where we have two reservoir models that
are consistent with all existing data, with one model having fifteen
compartments and the other having one. As I’ve argued before, it’s not a matter
of probability but possibility that is important. If the fifteen compartment case is
possible (it fits the data and is physically realistic), then it ought to be considered
within an uncertainty analysis of the reservoir. And, if its possibility has costly

11


consequences, then we should be driven in the direction of seeking information
such as well test data or fault seal analysis that might foreclose or make highly
unlikely its possibility. How would you stochastically deal with these models in
order to assign probabilities? How would you come up with that “all elusive
P50”? How would you “stochastically generate realistic models” like this?

2. U3D Reservoir Uncertainty Modeling –
workflows, products & benefits

In recent years the quantification, understanding and management of
subsurface uncertainties has become increasingly important for oil and
gas companies as they strive to optimise reserve portfolios, make better
field development decisions and improve day-to-day technical operations
such as well planning

Although the use of realistic 3D models in reservoir management is
becoming standard practice, 3D modelling is seldom used directly for
uncertainty management. This is partly related to a lack of procedures for
implementation of such studies and partly related to a lack of high quality
software to support the work processes necessary to model and explore
subsurface uncertainty in 3D.

The uncertainty module in Roxar‟s IRAP RMS reservoir modelling
software has been developed to fill this gap and allow the application of
3D modelling tools in uncertainty quantification and management.

12


Seed Variation Parameter Variation

Figure 1: Seed variation & parameter variation. Realisations
of channel architecture generated using a stochastic (object)
model. The “Seed variation” realisations have the same
geometrical input parameters. The “Parameter variation”
realisations have variable channel volume fractions, channel
widths and azimuths

The central concept in the IRAP RMS uncertainty module is to provide
users with a tool to set up 3D modelling workflows and analyse the results
where the input parameter values to the component modeling jobs
(operations) are varied in a controlled manner. This is in contrast to
working with multiple realisations where input parameters are kept
constant and random seeds are changed to generate a variety of different
reservoir models. This difference is illustrated in Figure 1, using a sand-
filled fluvial channel facies model. The geometrical input parameters are
kept constant and the random seed is varied the resultant realisations are
locally different, but are all characterized by a similar overall architecture.

From well data and general paleogeographic knowledge it is not possible
to know the true channel facies volume fraction, average channel

13


sandbody width, or average azimuth precisely. It is therefore important to
include this lack of precise knowledge in the uncertainty analysis by
varying the input parameters. This produces a much wider range of
geometries which more closely represents the true uncertainty in the
channel architecture.

It is important to incorporate this lack of precise knowledge throughout the
modelling process, from velocity modeling to flow simulation, to estimate
realistically uncertainty in reservoir volumes and reserves.

3D Uncertainty Modelling Workflow

Figure 2 (left) shows the 3D
uncertainty modelling
workflow. It is a standard
„structure to simulation‟ 3D
modelling workflow, but
includes uncertainty
distributions for the most
important input parameters.

14


The workflow involves:

1. Setting up input distributions for the 3D model components

2. Generation of multiple realisation of 3D models based on sampled values
from the input distributions

3. Volumetric and analysis based on multiple 3D models

The uncertainty workflow includes 3 main stages. The GRV uncertainty
is controlled by the structure and the contacts. The HCPV uncertainty is
controlled by the internal rock properties (facies and porosity) and fluid
properties (SW, Bo, Bg). Reserves uncertainty is defined by
connectivity and the dynamic behaviour of the reservoir.

GRV Uncertainty – Structure & Fluid Contacts
Gross rock volume is to a large extent controlled by the structure of the
horizons and faults and the fluid contacts. GRV uncertainty is often the
most significant uncertainty for in place hydrocarbon volumes and the
correct handling of the structure and contacts is often the key to a realistic
uncertainty assessment and asset evaluation.

An uncertainty model for velocities can be used to generate multiple
realisations of the depth structure. Relatively low velocity gradients will
produce flatter structures and high velocity gradients will produce steeper
dips (Figure 3). This is important for capturing realistic uncertainty in the
field volumes as flat structures are generally associated with larger
closures and higher volumes than steep structures.

15


The structural depth uncertainty
also needs to be linked to the
fault model. Uncertainty in
velocities and depth conversion
need to be handled in a
consistent manner for both
depth horizons and the fault
surfaces. The modern fault
modelling algorithms in Irap
RMS allow the fault model to be
rebuilt automatically using
velocity models with varying
input parameters.

The result is multiple
realisations of the reservoir
structure with consistent depth
surfaces, isochores and
Figure 3: 4 depth structure map realizations resulting from
faults.These consistent
uncertainty parameters for velocity modeling. The cross
structure models are used to
section underneath shows multiple surface outcomes
generate the 3D grids which are
using the same well data, but different velocity gradients
used for internal property
modelling, volumetric
calculations and flow
Uncertainty in fluid contact definition is often the key uncertainty in
simulations.
reserves estimation. Government organisations and oil companies use
very specific definitions of contacts for reserves accounting based on
criteria such as Lowest Known Hydrocarbons (LKH),

Oil Down To (ODT), Water Up To (WUT), half way depths, etc. When
working with realistic uncertainty analysis, distributions need to be defined
for the contact (or Free Water Level, FWL) depths.

16


Figure 4 (left) illustrates two examples of
input distributions for contact uncertainty.
Depth is on the Y-axis (increasing
downwards) and probability on the X-axis.

Example (A) is from a structure with a single
discovery well. A uniform probability
distribution between the LKH and the
structural spill point is used to define contact
uncertainty.

The second example (B) is from a structure
with two wells; a discovery well with a full
hydrocarbon column and a dry offset
appraisal well. A triangular distribution has
been used to define contact uncertainty. The
minimum depth value is defined by the LKH
depth in the discovery well, the maximum
value at the WUT depth in the appraisal well
and the mode at the MDT pressure derived
contact using data from both wells.

Other distributions could be used for the examples outlined above. The
key is for the asset team (geoscientists, petrophysicists and engineers) to
define input distributions which describe realistically the uncertainty in the
contact location based on the available data.

One of the main benefits of working in 3D is that intrinsic geological
dependencies are incorporated in the uncertainty analysis. Structure and
contacts need to be accounted for together. A combination of steep
structures (high velocities) and shallow contacts will lead to low GRV
whereas flat structures (low velocities) and deep contacts will lead to high
GRV.

17


HCPV Uncertainty – Rock & Fluid
Properties

Rock and fluid property uncertainties control the HCPV, STOOIP and GIIP
within the structural framework (Figure 2). The rock and fluid properties
are many and include: facies, porosity, permeability, SW, net cut-off, Bo
and Bg. Facies uncertainty includes the volume fraction, geometry and
stacking relationships of different facies types.

Volume fractions are important for the in place volumes, where as the
geometrical parameters are generally more important for connectivity and
recoverable hydrocarbon volumes. The implications of the facies
geometries can be analysed in 3D using static connectivity measures,
streamline calculations or full flow simulation calculations.

Figure 6 (left) illustrates an example of facies distribution
uncertainty in deltaic reservoir penetrated by three
exploration wells. The regional paleogeography includes a
continental high to the NE and a seaway to the SW. The
facies distribution in the wells supports the general
paleogeographic picture with higher proportions of proximal
facies to the NE and distal facies to the SW. The
progradation direction however cannot be known precisely
from this information and a distribution is used to capture this
uncertainty. This leads to a set of realisations with different
facies and architectures and anisotropy.

There are two important sources of petrophysical uncertainty.
The first source is related to logging tool measurement,
processing and interpretation. The second source is related
to the sampling of the wells. With only a few wells it is difficult
to know the “average petrophysical” values within the field
precisely. This uncertainty is particularly significant if there
significant trends within the field.

18


Standard spreadsheet-based uncertainty evaluations use distributions for
average petrophysical parameters as part of volumetric uncertainty
quantification. It is however very difficult to know, for example, the
average SW in a structure. Average water saturation in a reservoir is
dependent on many other parameters including the structure, FWL depth
and reservoir properties (porosity and permeability). These dependencies
are very difficult to estimate using petrophysical analysis alone, but are
automatically incorporated in the uncertainty analysis when working in 3D.

Another main advantage of 3D modelling is that the uncertainties are
defined at the individual input level rather than using some arbitrary
amalgamated average. Water saturation is typically modelled using some
form of saturation height function where the key parameters are the SWirr
values and the parameters of height functions within transition zones, e.g.
the “a” and “b” constants in a power-function (Figure 7).

Figure 7:
saturation
height
functions in the
3D model

Recovery Factors & Reserves

Conventional reserves accounting used „analogue‟ fields and basic
general engineering considerations to estimate recovery factors. When
working with 3D modelling, these considerations can be supplemented by
more realistic flow simulation analysis. The IRAP RMS uncertainty

19


module links the geomodel directly to a flow simulator (RMS flowsim) so
that realistic flow simulations can be carried out on multiple geomodels.
The 3D grids which have been generated from the structure model and
used in the property modelling can be used directly in flow simulations or
can be upscaled and used in lower resolution simulations. The grids can
be used for the whole variety of simulation purposes including:
quantification of realistic recovery factors, design robust development
solutions, and identification of bypassed oil and infill well targets.

There are a number of parameters used in flow simulations which can
have a significant impact on production profiles and recovery factors.
These include typical “simulation” parameters such as the Corey
exponents and the relative permeability end points. Additional parameters
such as fault sealing and aquifer dimensions should also be evaluated in
realistic reservoir simulation uncertainty evaluations.

Summary

Advances in 3D reservoir modelling technology allow for uncertainty
quantification workflows to be implemented in 3D instead of being based
solely on the use of speadsheets and direct sampling of input
distributions. This brings a wide range of benefits including:

• The input distributions are defined directly at the level of the modelling
components (velocity model, saturation model) instead of amalgamated
averages
• The input and output are not restricted to functions and histograms.
• Dependencies between input parameters can be treated in a realistic
manner
• 3D grids which can be used directly in reservoir simulation and
connectivity analyses are created.
• A wide variety of maps and 3D uncertainty cubes can be generated to
quantify spatially varying uncertainty
• The 3D models and derivatives can be used directly for well planning and
geosteering.

A full version Alister MacDonald‟s white paper on uncertainty management can
be downloaded from Roxar‟s website where you will also find a range of other
useful references and background material.

http://www.roxar.com/reduceuncertainty
20


3. Effective porosity vs. total porosity in 3D-models?
Laurence Bellenfant
Lead Geologist at Senergy Ltd

I have only been building 3D models for the last 5 years applying the same
methodology for petrophysical properties distribution. My experience is not as
Statistics: The only science
broad as most people in this group of dicussion so I thought I would take the
that enables different experts
opportunity to ask some questions.
using the same figures to
draw different conclusions.
I normally distribute effective porosity unless total porosity is only available, in
— Evan Esar what case I would apply a NTG cut-off. Only recently I realised that effetive
porosity can be calculated in different ways which means it doesn't always
represent the same thing.

When we distribute petrophysical properties in a 3D model, we make
assumptions about the petrophysical data we are using. I always thought
effective porosity was the way to go as I thought effective meant effective to
flow. I have recently realised that the term effective means different things
depending on your speciality. This is why some modellers prefer to distribute
total porosity and apply NTG. By applying the cut-off at the end of the process,
errors on what is really the effective porosity would be avoided. I guess the
term effective will have different meanings wether you are a petrophysicist, a
geologist or a reservoir engineer, but still, we pass on that information via the
3D model. My question is then, what should we apply as a methodology?

Mohit Khanna
Chief Geological Advisor at BG-India

Effective porosity or PHIE, as commonly known, is usually defined as Total
Porosity minus the porosity due the the shale component in the rock. Now the
definition of shale varies between a petropysicist & a geologist! That is why it is
important to talk to the petrophysicists right upfront and challenge them on
their approach so that we know what we are modelling. If the PHIE is calculated
as mentioned before then there is no NTG used there, hence, some sort of NTG
will be required for volumes etc. either as a poro-perm cut-off or a facues cut-
off.

Therefore, have a chat with the guys and then proceed.. you will realise that in

21


most cases what you have been doing i.e distributing PHIE is best and then use a
NTG.

Simon Haworth
Geologist at Nexen Petroleum
Laurence! Ca va?

Its a very good question. I'll tell you my workflow (purely petrophysical model
not facies based) so you can get even more confused.

We use effective porosities at work- so called because 'effective porosity'
excludes clay bound water. Clay bound water affects the neutron tool
measurements and tends to over predict porosity in shale layers when in actual
fact it is very low. Effective porosity therefore has a correction applied during
the porosity log calculation to take into account the underlying effects of shale.
The 'effective' term therefore means rocks which actually has porosity and
which is connected. So you can have very low (0-10%) porosities in an effective
porosity log but these porosities may not necessarily flow hence our
requirement at this stage to apply a cut-off (NTG or Facies based). I am only
really interested in modelling reservoir rock so a cut-off helps me to discriminate
between net and non-net facies. My engineer/petrophysicist will normally tell
me what he expects the minimum permeability for fluid flow (normally 1mD)
and this can be related back to a porosity value (roughly 10% but highly
dependent on facies). This can be validated via core analysis and a trip to the
core store!

So in my models, I model effective 'net' porosities. I make all values less than my
cut-off undefined at the log scale and then scale-up or block my log to the
resolution of my grid. My minimum porosity in my grid will be set to my cut-off (I
have to set this manually). I also generate a net flag at the log scale (1,0) and
then scale this up to the resolution of my grid so that my NTG flag becomes a
continuous parameter. I model NTG stochastically using trend maps etc so that
people can visualise the distribution of reservoir rock. Its not perfect and there
are alternatives to this methods.I'd go with whichever one you can get to grips
with and more importantly explain to the people who eventually use your
models.

Your not alone!

ingrid Demaerschalk
Principal Geologist at BG Group

Not sure i can agree with Simon's method... I certainly wouldn't go around
blanking curves. I try to leave the decision of what cut-off to use as late as
possible in the modelling process... saves a lot of work when you want to change

22


the cut-off value afterwards or do some sensitivities.
Ideally you want to model at a scale where a cell is either completely net or non
net (using effective porosity). You can then model porosity through out the
model (e.g. stochastically) and apply your net cut-off to the model resulting in a
net flag. This flag can then be upscaled for simulation (if necessary) and used as
a bias to upscale net porosity to maintain volumes correctly.

Net to gross is a non geological parameter that we impose and in my opinion
should fall out of the model rather than being modelled itself. A trend applied to
the porosity model will result in a N/G trend if done correcty.

Noelia Vera
Reservoir Engineer at TAQA Energy
Bonjour Laurence

I agree with Ingrid, NTG is a 2D deterministic concept that we are trying to use in
3D modelling wher is not really needed. I prefer to consider a whole cell either
net or non net when using effective porosity (which is by classical definition to
correct the effect of shales in the neutron tool measurements). After that you
can leave the cutoffs for later in the modelling, for upscaling or preserve the
flow units in your simulation grid.

Simon Haworth

Wow! That generated some feedback. I agree that NTG is deterministic but we
are limited- in essence - to computing limitations. In theory it is possible to not
use a cut-off at all in a geomodel (and therefore move away from the concept of
NTG) and let the simulator decide what will flow and what will not. I'd suggest
two things 1) you read the newsletter that was sent out some time ago
regarding modelling NTG 2) you find me a simulator that can simulate a fine
scale multimillion cell model in a reasonable time frame.

Cheers.

Simon Haworth

To add a bit more- even when you think your fine scale model is fine enough-
you will never ever capture the true distribution of net vs non net until you grid
cells are the same resolution as your logs. Thats why NTG is used as a substitute
for the scale of investigation we are working at.

ingrid Demaerschalk
Principal Geologist at BG Group

23


I did start my sentence with ideally and of course we have to compromise in a
lot of situations. But it comes down to getting get the upscaling right (whether
you do it in 2 steps i.e. blocking your logs + upscaling again to a simulation
model, or in 1 step i.e. blocking straight into a simulation model)
Also simulators can handle a lot more cells than you average RE will admit to
and in fact I am modelling some models at log scale (15cm cells) which then get
upscaled to a range of 1.5 to 5m cells. It works, the whole team understands and
upscaling is simple and teh models only gets slow when trying to do sensitivity
runs. Those need to be set running overnight.

I have no problem with applying a net cut-off providing it's done correctly and
volume calculation is still consistent.

Ian Taggart
PE at RISC Pty Ltd

Nice simple question ..... without a simple answer.

There is another dimension to this question not yet discussed - and that is choice
of saturation basis, SWE or SWT.

For a hydrocarbon in-place and moveable hydrocarbon in-place viewpoint you
can make either a PHIE or PHIT system work as long as you recognise the
saturation basis is different for each case. If you beleive in Dual water or simple
Archie models then, in general, you are in a total porosity space. Expressed
another way, combining PHIE, SWT or PHIT, SWE will result in inconsistencies.
(eg Indonesia equation & PHIT)

There is another aspect - that can sometimes generate debate .... and that is ;
oven-dried helium porosity is (close to) PHIT - so if you want to calibrate to core
- then PHIT has some advantages. As noted there is NO universal definition of
PHIE. The definition of PHIT as conencted pore space to helium after oven drying
is failry workable - and relates closely to PHID (density porosity). (Ian Juhasz
from Shell was an early advocate of this basis)

PHIT (vs the many PHIE alternatives) tends to have a smoother distribution and
is less affected by normal-score transforms that seek to correlate data behind
the scences in some geostat packages (eg cokriging perm from PHIT tends to
behave a little better than perm from PHIE under such conditions)

Bottom line - if you are careful - you can make either system work (and should

24


get the same HC volumes in either method) - but you cant choose your porosity
basis independently of saturation.

ie PHIE. (1-SWE) = PHIT.(1-SWT)

Oh & BTW - virtually all lab measured Sw, krw Pc's are done on a total porosity
basis. ( In good qualty rock where Vcl ->0 then the difference is moot)

Upscaling should work in either basis if done correctly.

As noted by others PHIT doesnt distinguish facies particularly well - so use of
facies and/or cutoffs is needed so hydrocarbons are placed in the wrong place.
Just takes a little more care.

Time to put on my flame-proof suit.

Mark Whelan
Development Geologist

Blimey! Tricky subject.....As Ingrid touched upon, it depends who the customer
is of your model. If it's the nice RE in your organisation, he'll/she'll take care of it
with a N2G flag. I have been modeling PHIT for a while now and my engineers
are still talking to me - which is nice!

Effective to flow is nice to define so longs as you have core and rel perm data to
work with.

The perfect scenario would be having core and PHIT/Krw to work with so that
you could define a cut-off or N2G flag based on permeability, and in this case
PHIT would be the way to go. If you don't have core then I would still use PHIT
and use analog data from a field nearby to help you decide on a cut-off based on
Permeability.

Nice question and one that will have as many answers as there are modellers in
the world - or at least this forum.

Roger Kimber
Senior Development Geologist at Centrica Energy

Good thing to come into work to - get the grey cells working. I've tried different
workflows - at the same time trying to take into account the views of the RE in

25


particular as he/she is the customer. For me, I have followed a workflow similar
to Simon but it is also important to consider different cut-offs as this is in itself a
deterministic interpretation. It also brings into question the whole debate about
calculating static volumes as gross and leaving the facies model to take out the
non-net. Personally, I take a pragmatic view and like to use a NTG parameter as
people understand the concept (or at least we assume they do), particularly
managers. The petrophysicist will compute a net:gross, it is a standard
parameter for 2D models and I don't see why we should not model it. At least it
can give us some flexibilty when we present static volumes as we can then
measure the impact of non net v net and the influence of facies. I also agree that
net cut-off should be permeability based. And this comes to the debate about
core - we don't have enough of it and to often make decisions based on a log
evaluation derived from a suite of logs which may or may not have taken the
core fully into account. A perfect example is my current project where I have the
luxury of >3000 ft of core where it is very evident that if you actually touch the
core you end up with a toatlly different mind-set - the logs tell you that it is net
but in reality the heterogeneity is on a fine scale with much argillacous content
“The world was created in
introduced by burrowing.
4004 BC.”
James Ussher (1581-1656
– Archbishop of Armagh, Laurence Bellenfant
Lead Geologist at Senergy Ltd
who worked out a long-
accepted chronology of
Thank you very much for your very useful answers. The NTG is one part of the
scripture)
problem/solution but understanding the origin of my petrophysical data is going
to help a lot. Thank you particularly to Ian Taggart for his detailed comment. I
actually went to see the cores of my field this week and I feel more confident
about the way to takle the model.

I hope you enjoyed the discussion and that it helped ... at least it helped me!
Laurence

PS: Salut Noelia et Simon, hope all is well.

Lawrence Itakpe
Geoscientist - Horizon Energy Partners

That’s a very important question and issue for discussion regarding reservoir
modelling.

Ideally I think both PhiT and Phie are important information/parameters in
models, which is more important than the other depends on what you’re looking
to achieve, but the best practice is model PhiE.

Phie would define the amount of effective connected pore space in the matrix,

26


while the total porosity – PhiT would define porosity of the total pore space –
connected pores, isolated pores in the grain matrix. As a result – if you model
PhiT you might just be over estimating volumes, hydrocarbon pore volumes
space in the rock and also perm if you apply log Perm vs. Poro transform to
model perm.

Most times both PhiT and Phie are generated by the Petrophysicists and would
always provide both to the geologist.

Ideally one would want to normalize its data and understand their relationship
in space with each other before modelling in a 3D scale/resolution – data
analysis.

But if the data are just model and cut-offs applied later, I don’t think the data set
modelled would have been normalized.

My views about net to gross – I see it has an important parameter in 3D
reservoir model. Net to gross shows a fraction of the reservoir that contains
Hydrocarbon – Net pay sand – Net sand – these if shown in the form of a Map
provide an idea about - sand distribution, development and source into the
reservoir/Basin - this is important in reservoir development and could be applied
same way – seismic amplitude attributes is applied.

Net to gross is also an important parameter in volumetric computations.
Because the volumes considered is the volumes in the Net sand of the reservoir
and not the total sand reservoir package.

I agree with Roger Kimber regarding the importance of Net to gross.

Ian Taggart raised yet another important issue about water saturation – SWE or
SWT as similar to PhiT or PhiE; I think in either case the effective is important but
keeping in mind the total.

Yes Ian Taggart you are right trying to model saturation using Porosity vs. water
saturation transform – this is workable if there is a transform Law linking
Porosity and water saturation, most times the result using this transform have
oil saturation below the contact or in the aquifer. The question is how this could
be possible.

I think the best practice for saturation modelling is depth or thickness vs water
saturation transform. This I think would be more realistic and consistent.

27


I terms of Phie or Phit modelling methodology – the clients would always prefer
their in house methodology. But in ideal practice I think modelling Phie is best
practice but keep in mind the phit as well.

Good Luck

Career Networking

Subsurface Global 2008/2009 –

2008 certainly has been a year for change in the oil and gas industry. Oil prices
almost reaching $150/barrel before falling back to close to $40. The demand for
energy has not, and will not go away. Subsurface Global plans for 2009 are to
develop upon our relationships to provide excellence at delivering ideal
candidates to organisations and the ultimate jobs for candidates. If you find
yourself wanting to take on a new challenge or considering a move across the
world please contact one of our specialists ( future@subsurfaceglobal.com ). Our
expertise lie in Geosciences, Petroleum and Senior E&P appointments. May I take
this time to wish you all a Happy Christmas and a prosperous 2009.

28


29


Requests for the newsletter No4

The next newsletter is planned for a March 2009 release, so please send any
articles to me at the following email address for inclusion
(mitch.sutherland@blueback-reservoir.com).

Also, please take advantage of the Geomodeling Network discussion board on
LinkedIn to initiate comments on any Geomodeling subject of interest to you –
all I ask is that you respect other people’s opinions – even if you think they are
talking mince!

Finally: Ever wonder why you studied Geology?..........Click below to find out

http://s65.photobucket.com/albums/h223/fishmato/?action=view&current=Am
ericanDad.flv

Fin

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