july 2011 the geomodeling network newsletter

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

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

[email protected] 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 ([email protected])

mailto:[email protected]

mailto:[email protected]

http://www.blueback-reservoir.com/






Table of Contents

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 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

Sign that you may be a geologist #1

You have ever had to respond "yes" to the

question, "What have you got

in here, rocks?"



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

-Description of QC Issue

When we build a reservoir model, we sample the well logs into the grid cells and we get a set of upscaled logs. How do we make sure, that these up-scaled logs are a good 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!

-How do we do the QC Today?

A geologist was accused for

throwing a lava rock at a

tourist.

He's been charged with "basalt

and battery."

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?

http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&gid=111596&discussionID=1212699&sik=1237994455856&trk=ug_qa_q&goback=%2Eana_111596_1237994455855_3_1%2Eana_111596_1237994455856_3_1







There are two ways we try to do the QC of upscaled logs today, by comparing statistics from logs and upscaled logs...and sometimes we try to calculate vertical columns in wells from the properties.....but is this possible in Petrel?

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

"If I could remember the names of all these particles, I'd be a botanist."

— Albert Einstein



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.

... 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

http://www.energybandgap.com/power-generation/oil-environmental-geographical-and-political-issues/attachment/oilworld/



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

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

Sample average

property values into

well tops spreadsheet

and calculate vertical

columns


#3

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



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:

....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


#4

You have never been on a

field trip that didn‟t include

scheduled stops at a

gravel pit and/or a liquor

store



-QC of upscaled logs – solution

Can this QC be done fast inside Petrel minimizing the mouse clicking and minimizing the risk of manual errors (mistypes, hitting wrong buttons etc)

Petrel Plug-in QC Tool

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

“Reality is merely an illusion,

albeit a very persistent one.”

Albert Einstein

If A equals success, then the

formula is: A = X + Y + Z, X

is work. Y is play. Z is keep

your mouth shut.

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)

“Everything that can be

counted does not necessarily

count; everything that counts

cannot necessarily be

counted.”

Albert Einstein



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

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

Evan Esar

http://upload.wikimedia.org/wikipedia/commons/d/d8/Lucas_gusher.jpg



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

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

“The guy who invented the

first wheel was an idiot. The

guy who invented the other

three, he was a genius.”

Sid Caesar

http://en.wikipedia.org/wiki/File:Blow_out_preventor-italy.JPG



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

“Don‟t worry head. The

computer will do all the

thinking from now on.”

Homer Simpson

http://www.guy-sports.com/funny/funny_church_signs.htm



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 [email protected]. Thanks

Trisatya Nugraha • Jose, to export well properties from model to excel you should do "make log" on well folder operations, choose which properties, then you will have upscalled properties on wells in input data tabs, then export it to ASCII, dont forget to 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

“To start, press any key.

Where‟s the „any‟ key?”

Homer Simpson

http://www.linkedin.com/groups?viewMemberFeed=&gid=111596&memberID=55133950&goback=%2Egna_111596%2Egmp_111596%2Egde_111596_member_54809126%2Egmp_111596





http://en.wikipedia.org/wiki/File:Wellhead-dual_completion.jpg

http://www.guy-sports.com/jokes/funny_pictures_cars.htm



that can you please provide an example work template to me of course if you don't have any inconvenience?

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 '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

If you think there is safety in

numbers, try playing roulette.

Evan Esar







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



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

Albert Einstein



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

Albert Einstein



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

Albert Einstein



“Education is what remains after one

has forgotten everything he learned in

school.”

Anon




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



http://www.soton.ac.uk/~imw/jpg-Bridport/11BRP-East-Cliff-Low-Tide.jpg



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

http://www.soton.ac.uk/~imw/jpg-Petroleum-Geology/10PTS-Wessex-Succession-Simple.jpg



“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



“Make crime pay – become a

lawyer.”

Will Rogers

http://upload.wikimedia.org/wikipedia/en/8/8f/102_329_nobel_oilwells.jpg



4. My model is good your model is bad. (Taken from the Geomodeling Network

discussion page)

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 complementary and independent one from the other. Your two criteria (purpose ; volumes) fall into my third angle. 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 tools to the maximum of their strengths and weaknesses, but if the resulting model doesn't "talk" to the team, then it is a bad model. Of course, the vision of team can be 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.

“An optimist is just a guy who

has never had much experience”

Don Marquis

“That‟s the spirit, George. If nothing else works, then a total pig-headed unwillingness to look facts in the face will see us through.”

– General Sir Anthony Cecil Hogmanay Melchett, ‘Blackadder Goes Forth’

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* ... 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 fundamental, then it is better to QC three scenarios rather than a base case static model. 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

The goal of science and

engineering is to build better

mousetraps. The goal of

nature is to build better mice.



http://www.iris.edu/hq/gallery/photo/9156

http://www.iris.edu/hq/gallery/photo/9150



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. History match might also be a criteria for determining if a model is good or not. A checklist is always nice, because it quantifies stuff. Seems to me that most companies 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

On the sixth day, God created the platypus. And God said: let‟s see the evolutionists try and figure this one out.



http://en.wikipedia.org/wiki/File:Oil_platform_Norway.jpg



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.




http://www.decadevolcano.net/photos/etna1106_7.htm


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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

Steven Trueman • I think the validation and verification theme is also a good starting point. Some quick look suggestions: - VALIDATION - verify concept, structure and properties are valid and physically reasonable Conceptual model; consistent with regional and analogue information etc? Faults – eg is fault type reasonable in tectonic setting? Maps - do they look sensible, geological? Well Test comparison e.g. perm thickness and/or HCPV ft height

Scientists have shown that the moon is moving away at a tiny yet measurable distance from the earth every year. If you do the math, you can calculate that 85 million years ago the moon was orbiting the earth at a distance of about 35 feet from the earth‟s surface. This would explain the death of the dinosaurs. The tallest ones, anyway.




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. BTW: It is the latter that JewelSuite is really good in …

Per Olav Eide Svendsen • But if a model is not identified as "bad", is it then "good"? I think the Popper-based approach to determining if a model is a "good" or a "bad one is an interesting approach. Following the same research principles as any scientific theory, 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

“Scientists travel into jungles to study cannibals, crawl into active volcanoes, play with dinosaur bones, and blow things up! How can that be boring?”

– published comeback to a 6th grader who says, ‘Science is boring’





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.


http://www.linkedin.com/redirect?url=http%3A%2F%2Fhistory%2Eucsd%2Eedu%2F_files%2Ffaculty%2Foreskes-naomi%2FPhilIssuesModelAssessOreskes%2Epdf&urlhash=69D8&_t=tracking_disc




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 even with good seismic and well control. 4d geological models are normally under constrained and it is possible that there is more 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 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.

“You can no more win a war

that you can win an

earthquake”

Jeannette Rankin



http://thinkexist.com/quotation/you_can_no_more_win_a_war_that_you_can_win_an/187946.html



http://annainmexico.com/wp-content/uploads/2011/02/Richter_scale.jpg

http://en.wikipedia.org/wiki/File:Quake_epicenters_1963-98.png

http://en.wikipedia.org/wiki/File:Global_plate_motion_2008-04-17.jpg



5. Blueback Reservoir releases the Reservoir Engineering Toolbox

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

“Political convulsions, like

geological upheavings usher in

new epochs of the world's

progress”

Wendell Phillips

http://thinkexist.com/quotation/political_convulsions-like_geological_upheavings/184616.html




http://en.wikipedia.org/wiki/File:Witchs_Finger_Carlsbad_Caverns.jpg



FUNCTIONALITY HIGHLIGHTS

The Sw modeling calculator is a much needed tool for Petrel users. It is designed as its

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.

“We are like a judge

confronted by a

defendant who declines

to answer, and we must

determine the truth from

the circumstantial

evidence.”

-Alfred Wegener

http://www.soton.ac.uk/~imw/jpg-Kimmeridge/10KM-Burning-Blocks-Oil-Shale.jpg



Fin

july 2011 the geomodeling network newsletter

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