march 2009 the geomodeling network newsletter

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

The Geomodeling Network Newsletter March 2009

A very warm Spring 2009 welcome to all of our Geomodeling Network

members wherever you are. Earlier on this month it was looking like the

newsletter was going to be delayed through a lack of articles. However I am

pleased to say that after some online harassment, our members rallied and the

articles magically appeared just in the nick of time – so a big thank-you for those

of you who have taken the time to contribute.

I am always open to suggestions regarding our growing network and the shape

and direction in which it takes us. Bearing this in mind, I have had a few

members bending my ear recently who are concerned about the growing

influence of online recruiters using the Geomodeling Network for their own

commercial purposes.

Whilst I am not totally adverse to some forms of commercialism in our group

(you may even have spotted the subtle Blueback advertising throughout our

newsletters); the intention is that this plays a minor role in what our Network is

trying to accomplish. Indeed if you carry on reading this month’s newsletter you

will see that there are articles from a number of software vendors (Halliburton,

Schlumberger etc) which I think are interesting, relevant and provide a lot of

value to the group.

That said, I will keep my beady eye on the group as to what is being posted and

will take great pleasure in removing articles (and members) if they are starting

to become a nuisance.

Mitch Sutherland [email protected]

mailto:[email protected]

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






Table of Contents

Member Articles, Reviews & Questions

1. Property Modeling within modeled objects .... defining that

thalweg! Has anyone successfully modelled rock properties at specific locations within facies objects? Juan Cottier, Subsurface Manager at Blueback Reservoir AS

This has been taken from the Geomodeling Network discussion board and is a good

example of how the board can be utilized to pose questions. Page 3

2. A faster and more accurate Gaussian method for property

modelling in Petrel Colin Daly – Petrel product champion, geological modelling, Schlumberger

Sandra Quental – Petrel product analyst, geological modelling, Schlumberger

There were questions asked recently on the discussion board about the Gaussian method

– this is timely input from Schlumberger!

Page 5

3. Geology & Technology What kind of technology will geologists be using in 2025? An example of technology that today is in its infancy, but which may be more prevalent in the future. Simon Haworth - Geologist at Nexen Page 11

4. High Frequency Characterization of an Outcropping Sinuous

Leveed-Channel Complex, Dad Sandstone Member, Lewis Shale, Wyoming This paper presents the results of data collection, analysis and integration to build a 3D geological model of an outcropping leveed channel complex. Staffan Van Dyke - Geologist at Nexen Page 14

(Abstract only – see end of article on how to access the complete paper)

“Civilization exists by

geological consent,

subject to change

without notice.” -Will Durant

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

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



5. Free Petrel Plugin’s! What idiot said “you get nothing for free in this world?” Check out the latest free downloadable Petrel plugin’s now available from the Blueback Reservoir software development team. Blueback Reservoir Page 14

6. Geo2Flow Reserves Estimation – software that allows you to answer 3 crucial questions: How much? How fast? And How connected? Dan O’Meara – Owner, O’Meara Consulting Page 17

7. Requests for newsletter No5 Page 20

Member Articles, Reviews & Questions

1. Property Modeling within modeled objects .... defining that thalweg! Has anyone successfully modeled rock properties at specific locations within facies objects? Juan Cottier, Blueback Reservoir AS Clearly some further information is required here ..... For example, I want to be able to place certain poroperm values at certain locations within channels. I am working on the UK Forties fairway and I have been provided with some excellent facies work (facies logs, facies associations and very well integrated field analogues). The challenge is that within a distribution of porosity values I want to be able to place the very best poroperms at the centre of the channels and at the top of the channels, where as the poorest poroperms go at the base and sides. there are plenty of ways of trendng/cross correlating/analysing data per zone/layer within PETREL but there does not seem to be any "understanding" of the geometry of the bodies.

“Rocks are records of

events that took place

at the time they formed.

They are books. They

have a different

vocabulary, a different

alphabet, but you learn

how to read them.”

-John McPhee



Schlumberger Support, though very helpful, have no straight forward answer to this question. I have already created a workflow that allows me to define channel edges per K-layer and then use the distance from the channel edge to control levee or channel porosity distributions. It works exactly as I wish it to ... except ... it is cumbersome, ineffcient, requires conformable layering, requires precise set-up and is impractical beyond a certain number of k-layers. Beautiful results but not at all practical ...... rather like an Alfa Romeo. Any ideas? Or solutions? Thanks. Juan.

Dave Hardy Reservoir geologist and reservoir modeller

Juan, Use RMS ;-). It 'knows' about modelled objects. Intrabody trends are very easy to set up in all directions and work really well. In the old days before RMS had this I have used a facies object ID parameter and a script to define a vertical trend (loop trhough the layers and reset the distance every time the code changes). The script approach does not handle erosion terribly well and horizontal trends are tricky unless the objects are aligned with the grid but it was passable. I have no idea if that approach would be possible in PETREL or if it is any better than the solution you already have.

Russell Cooper Geologist at OXY Permian

Juan, Assuming you have a 'center of channel' and 'top of channel' poroperm equation to distinguish these areas from the rest of the model, perhaps respective of facies as well, you could use zone/layer filters in combination with a polygon(s) in geometrical modeling to create a center of channel and top of channel property and use these as references in a nested 'IF' equation to derive the desired permeability property.

http://www.linkedin.com/profile?viewProfile=&key=15076604&authToken=Tp18&authType=name&goback=%2Eana_111596_1237994455855_3_1%2Eana_111596_1237994455856_3_1

http://www.linkedin.com/profile?viewProfile=&key=36886095&authToken=Cjxl&authType=name&goback=%2Eana_111596_1237994455855_3_1%2Eana_111596_1237994455856_3_1



2 A faster and more accurate Gaussian method for property modelling in Petrel. Colin Daly & Sandra Quental – Schlumberger

Petrel 2009.1, released last February, brings a new Gaussian simulation algorithm that will please our modeling community. The so-called Gaussian random function simulation (GRFS) differs substantially from the Sequential Gaussian simulation (SGS) from GSLIB: it is not sequential; it is parallelized and hence typically faster than SGS. Plus it has an option to run a fast collocated co-simulation, with an interactive correlation coefficient slide bar.

The GRFS works using the well known decomposition which states:

CONDITIONAL SIMULATION = KRIGING + UNCONDITIONAL SIMULATION

For the kriging part of the equation, Petrel uses the parallel kriging algorithm

introduced in 2008. This kriging algorithm is substantially faster than the old

kriging algorithm in Petrel, particularly in the case of a lot of well data, and so

makes use of the above decomposition practical and beneficial. (For example, on

a test case with 3 million cells and 500 wells, the new algorithm runs in about 10

seconds compared to about 36 minutes for the old GSLIB based algorithm for

identical results). The unconditional simulation term uses a Fast Fourier

Transform based algorithm which gives good variogram reproduction for a wide

class of variograms.

If using the collocated cosimulation option with GRFS, the user will notice that

there isn’t any systematic bias in the degree of variability of the simulated

variable or in the correlation between the simulated variable and the secondary

variable. For SGS, it is often found that the variance of the simulated primary

variable is systematically different to the desired input variance. In the typical

case where the secondary variable is smoother than the primary (often the

secondary variable is a smooth seismic data volume), then SGS simulations will

generally have higher variability than expected. Furthermore, the calculated

correlation between the simulated primary variable and the secondary variable

is not equal to the input correlation. This is a problem associated to the

sequential nature of SGS. Within Petrel’s GRFS there is a mechanism to

overcome such a bias, called the ‘Variance Reduction Factor’, which can be used

to partly remove the bias. This is no longer necessary with GRFS.

to remember the order of

the geological time

periods: “Cows Often Sit

Down Carefully. Perhaps

Their Joints Creak?

Persistent Early Oiling

Might Prevent Painful

Rheumatism.”



Another nice feature implemented for the GRFS is the fast collocated

cosimulation. This is based on a new algorithm that extends a well known

decomposition from the literature (e.g. Chiles and Delfiner, Geostatistics, Wiley,

1999). This decomposition states that collocated cokriging can be split into a

kriging that is done once and a simple Bayesian cokriging update. We have

further developed this by coupling it with a correctly chosen unconditional

cosimulation of primary and secondary variables. It can be shown that this gives

an exact collocated cosimulation. Updating to try a new correlation between

primary and secondary variables is quite fast, so this has been implemented on a

slider bar within Petrel so that the modeler can interactively see the results

when changing the correlation (Figure 1). For the 3 million cell model mentioned

above, the updating takes about 0.2 seconds so is fast enough to work on the

slider bar.

Secondary property = porosity cc = 0.85

cc = 0.15 Permeability models using

GRFS with cosimulation

Figure 1 – Using the new slider bar available for cosimulation with GRFS, it is

possible to change the correlation coefficient (cc) and see the results on the fly in

visualization windows.



Also new in Petrel 2009 is the ‘layer declustered search’ option, which is used in

the kriging component of the Gaussian simulation (thus also present for the

kriging method). When this is active, it ensures that the when the kriging

algorithm is searching for nearest neighbours of a cell which is to be kriged, it

preferentially searches for neighbours in the current layer and then

progressively for neighbours in nearby layers. If selected, this overrides the

default mechanism which searches for neighbours according to variogram

weighted distance. The primary application of this is when the variogram

exhibits a long correlation in the vertical direction. In this case, the standard

search would tend to find many highly correlated neighbours along vertical, or

near vertical wells. This situation, when many of the data used for kriging are

highly correlated between themselves, is similar to a situation in standard

regression theory called collinearity. A typical solution used for kriging is to

perform a declustering of the data and to choose neighbours which are less well

correlated between themselves. The layer declustered search is a simple but

often effective method to perform such a declustering in the case of near

vertical wells with a long vertical range. The effect is that a better spread of

neighbouring data, with less correlation between one another are used for the

kriging, eliminating common artifacts generated by the traditional search

methods used in the market (Figure 2).

Figure 2 – A very simple example showing a kriged porosity model using three wells.

Left: common kriging artifacts are due to a long vertical variogram range Right: layer declustered search has been applied, eliminating the artifacts.

Keeping long

vertical variogram

range

Applying layer

declustered search

Applying layer

declustered search



Another expert option for kriging and GRFS is the ‘approximate search’, which

provides a search algorithm that can often be substantially faster than the

standard search algorithm. It is however not as accurate and must be used with

some care. A typical application will be when used to create many realizations

for kriging or Gaussian simulation. The user should test that the fast search is

giving results of acceptable quality on a trial realization. If it works well for one

realization then it will work just as well for all realizations using the same

parameters. So when, for a choice of kriging parameters and neighbourhood

size, it is deemed to be working acceptably, this option can be switched on for

the time consuming activity of generation of many realizations.

Finally the ‘factor of simulation extent’ is a variable associated with the

unconditional simulation. When the range of the variogram becomes longer

than the sides of the model, the Fast Fourier Transform based model will not

give a good reproduction of the variogram (due to aliasing). This can be

improved by simulating on a larger volume and then ‘cutting out’ the region of

interest. The factor of simulation extent can be used in such a case. Typically the

value of 1 will be good enough, but if the correlation length becomes long, then

it may need to be increased to a length of 2 or 3. Very high values can cause

memory problems for the machine. It should be noted that there is little reason

for using correlation lengths much longer than the extent of the field as this type

of low frequency variability is usually better treated as a trend.

Schlumberger Information Solutions strongly suggest Petrel geomodelers to

start using the new GRFS in workflows where SGS is usually applied. In large

grids and/or in an uncertainty study context, if running various realizations of

petrophysical properties, a great time gain will be observed, as well as a visible

improvement in achieving the desired distribution statistics.

An example comparing SGS and GRFS results on volume distribution

We now look at an example which shows that GRFS does a better job at

modeling the uncertainty in the total pore volume of the reservoir than SGS.

In a test project with a regular grid of 90x90x200=1.62 million cells of 100 x 100

x 1 meters size, 200 realizations were run on the porosity model, 100 of them

using GRFS and the other 100 using SGS. Only the seed has been changed, all

other parameters were kept the same. The variogram was spherical with ranges



2000, 2000, 5 in the X, Y and Z directions respectively. The mean porosity was

0.15 with standard deviation of 0.05.

Our objective is to calculate the distribution of pore volume for each realization

and then look at the distributions of such volumes for both GRFS and SGS. The

resulting distributions are shown in figure 3. There is clearly a difference

between the GRFS case and SGS case. Which gives the better result? Well, if we

knew the expected standard deviation of the distribution we could just check

and see.

To help us with this, we remember from basic statistics that if we have n

independent points following the same distribution then the variance of the

mean of those values is just variance of a single point divided by the number of

points ( V = 2/N where V is the variance of the mean reservoir porosity, the

variability being from one realization to the next). We have 1.62 million points

and we know that the mean of the porosity distribution is 0.15 with standard

deviation of 0.05. However, we cannot just choose N=1.62M because not all the

data are independent (the resulting simulation is not just a pure nugget effect or

white noise simulation so the values are correlated to one another). Roughly

speaking we can consider points to be independent of one another when they

are separated by a distance equal to the range of the variogram. More

accurately there is a known method in geostatistics for calculating the

approximate number of truly independent points and then we can use that

formula. It is called the method of integral range. We won’t go into the details

here (see Lantuejoul, C., Ergodicity and Integral Range, Journal of Microscopy,

161(3)) but the integral range for a spherical variogram is A = a3 where a is

the range of the variogram and the number of equivalent data is then N = V/A

where V is the bulk rock volume of the reservoir. In this case we find that

N=1600 approximately. We can then use the formula V = 2/N to calculate the

variability we might expect over the reservoir volume. This gives the standard

deviation V=0.00125. In a normal distribution, the size of the 95% confidence

interval is twice the size of the standard deviation. Combining these results we

should get 95% of our realizations having a mean porosity for the total reservoir

of between 0.15 – 2*(0.00125) and 0.15 + 2*(0.00125), that is in the interval

[0.1475,0.1525]. Since the total rock volume of the reservoir is

(1.62x106)*100*100*1 = 16.2x109 m3 (number of cells multiplied by volume of

cell), then the expected range of pore volumes is approximately [2.389x109,

2.471x109]. Looking at the results of figure 3 we can see that the 100 realizations

of the GRFS are consistent with this estimate while the results from SGS show

considerably more variability than one would expect from the theory. The

following table gives a resume of the results:

What did the rock do all

day? Nothing.....

......I’ll get my coat!



Lower 95% conf interval Upper 95% conf interval

Theoretically correct result 2.389 2.471

GRFS – observed result 2.386 2.482

SGS – observed result 2.350 2.502

Table 1. Confidence Intervals for the Total Pore volume variation for the reservoir.

Results are in units of 109 m

3.

Overall, this shows that the GRFS simulation gives results that are more

consistent, in terms of total pore volume modeled, with the information used to

develop the model (in this case the variogram, mean and standard deviations of

any well data). This becomes especially important in the case where we are

conditioning to well data where the GRFS does a better job of modeling the

expected variability around the distributions of porosity observed in the wells.



Career Networking

3. Geology & Technology

Simon Haworth – Nexen

Have you ever wondered what it might be like working in an oil company

in 2025? Will we still be working with computers and bulky, costly

computer screens? What if desks were your screens? And all you had

was an internet connection?

There are many more questions like this, but if I wrote them all down it

wouldn‟t make for an enthralling article.

I‟m an avid believer that one day, in the not so distant future, we will be

working, in fact geomodelling, on the walls of our office. Not just by

hanging a plasma screen on it, but by interacting with the wall itself.

Consider yourself interacting with digital experiences that move beyond

digital tradition, that blur the boundaries between art and science, and

transform social assumptions. We are already in an era where current

technology offers insights into interactive techniques, projects that explore

science, high-resolution digital-cinema technologies, and interactive art-

science narrative.

Most people have seen Minority Report- a Steven Spielberg special in which cyber cop Tom Cruise manipulates wall-sized displays powered by gesture recognition, and seamless information convergence: it‟s the stuff that interface designer dreams are made of. So how did Tom Cruise get such a nice set up? It turns out that Microsoft Research, MIT, and several design shops had a say in the interface designs found in the film.

Figure 3 – Above: pore volume distribution after running 100 porosity realizations using

SGS. Below: pore volume distribution after running 100 porosity realizations using GRFS.

The histogram for GRFS is less spread than for SGS, because SGS tends to give higher

variance results than the one from the input distribution (in this case higher porosity

variance, hence higher pore volume variance).

“It was with unalloyed

pleasure that I became

aware that a vigorous

earthquake was in

progress.”

-G.K. Gilbert on the 1906 San

Francisco earthquake.



Hand and touch screen recognition devices already exist but how can we

get involved?

So, if the big boys are cleverly adapting and developing the hardware,

which oil service companies are going to get their mitts on it first? Cost is

likely to be an issue- nobody wants to venture into a novel arena because

there is some likelihood the adaptation and, more importantly, uptake

could flop. On the other hand, it could be a resounding success- „Qui

Audet Adipiscitur („He who Dares Wins‟).

The willingness to embrace change is heavily dependent on the decision

makers and their own visions for the future. The demography of the

industry is changing so rapidly that geoscience and engineering

professionals are taking on more and more responsibility at a young age.

It is seemingly more possible that the fully loaded virtual office will

become a reality therefore allowing the industry to leverage the

experience of others from the comfort of their own home. Less office

space in prime locations means lower overheads. Meetings will take place

in your living room with the aid of holographic projections (ref. Cisco‟s

Why did the biker carry

a large piece of an

extrusive, pyrclastic,

igneous

rock composed chiefly of

volcanic ash as on his

motorcycle?

He wanted to act tuff.



Telepresence) just like R2D2‟s relay of the all important message to Luke

Skywalker in Star Wars.

To sum up, the technology already exists- the software doesn‟t. I‟d like to

see more of an uptake in the design and adaptation of the technology for

the oil industry. Petrel, RMS and others are great tools- it‟s how we use

them that matters.

Mitch has kindly offered to publish this article so that I can gauge interest

amongst other Geoscience professionals and Software Developers alike

who share my vision for taking this further. My initial thoughts are to bring

together software, technology and geology professionals in a combined

Special Interest Group with a view to development of bigger concepts and

product development in an arena which is under-explored and under-

funded. Please email me with any comments, thoughts and ideas.

There are numerous societies with SIGGRAPH (Special Interest Group on

GRAPHics and Interactive Techniques) being a key organisation. For

anyone interested, this year‟s conference is being held in New Orleans

(3rd-7th August 2009). Further details can be found below

http://www.siggraph.org/s2009/.

“Dreams will get you nowhere; a good kick in the pants will take you a long way”.

"...And yet it does move."

- Galileo (referring to the Earth)

http://www.siggraph.org/s2009/



4. High Frequency Characterization of an Outcropping Sinuous Leveed-Channel Complex, Dad Sandstone Member, Lewis Shale, Wyoming (Abstract only) Staffan Van Dyke – Nexen

This paper presents the results of data collection, analysis and integration to build a 3D geological model of an outcropping leveed-channel complex. Data is from more than 120 standard measured stratigraphic sections, behind-outcrop drilling/logging/coring, ground-penetrating radar and electromagnetic induction surveys and 2D shallow seismic reflection acquisition.

This leveed-channel complex, which is part of the Dad Sandstone Member of the Cretaceous Lewis Shale, Wyoming, consists of ten channel-fill sandstones, confines within a master channel. The complex is 67m (200ft) thick and 500m (1500ft) wide and has a net sand content of approximately 57%. Individual channel-fills are internally lithologically complex, but in a systematic manner which provides a means of predicting orientation and width of sinuosity. Although it has not been possible to completely document the three dimensionality of the system, the 3D model that has evolved provides information on lithologic variability at scales which cannot be verified from conventional 3D seismic of subsurface analog reservoirs. This vertical and lateral variability can provide realistic lithologic input to reservoir prediction. An outcome of this study has been knowledge gained of the extent of manipulation required to obtain the spatially correct geometry and architecture of strata when integrating outcrop and shallow, behind-outcrop data sets. If anyone is interested in reading the complete paper, simply click on the link below and find the presentation called “GCS-SEPM Lewis Shale” http://www.linkedin.com/osview/canvas?_ch_page_id=1&_ch_panel_id=1&_ch_app_id=7544200&_applicationId=1200&appParams=%7B%22from%22%3A%22owner_network_slideshows_home%22%2C%22view%22%3A%22canvas%22%2C%22page%22%3A%22owner_minifeed%22%7D&_ownerId=8140385&completeUrlHash=GMNx

5. The Blueback Toolbox Blueback Reservoir (www.blueback-reservoir.com) (Full download instructions for the Blueback Toolbox can be found at the end of this article)

The latest software product from the software development team in Blueback Reservoir is our new Toolbox. The Blueback Toolbox is a set of smaller Petrel plug-ins for solving specific problems not supported in standard Petrel.

Q: What is the

difference between a

geologist and a chemist?

A: A chemist will drink

anything that is

distilled.

A geologist will drink

anything that is

fermented....

http://www.linkedin.com/osview/canvas?_ch_page_id=1&_ch_panel_id=1&_ch_app_id=7544200&_applicationId=1200&appParams=%7B%22from%22%3A%22owner_network_slideshows_home%22%2C%22view%22%3A%22canvas%22%2C%22page%22%3A%22owner_minifeed%22%7D&_ownerId=8140385&completeUrlHash=GMNx








The aim with the Toolbox is to facilitate faster workflows and to provide Petrel users with functionality not already in available in Petrel. The current Toolbox is available for FREE – just send us an email with your details. The content of the Toolbox is increasing all the time as we keep adding new plug-ins to it. Most of the plug-ins have been developed upon direct requests from Petrel users, and in most cases resulting in functionality we make available to all Toolbox users.

The Toolbox functionality as of 1 March’09: Make Cube

Generates a seismic cube data object from a point data set. Specify resolution, min/max values and interpolation algorithm.

Import/Export

Support for new data formats.

Export of navigation data for a seismic survey.

Import of IESX interpretations as points.

Import 3D seismic interpretations from a general ASCII format.

Import ASCII files into a seismic cube. Sample Attribute

Sample points from a seismic cube. Using a point set to sample values from a seismic cube. The sampled values will be appended as an attribute to the point set



Comments to data objects

Easy addition of comments to the Info tab in the Settings dialog of a selected data object. No need to open the Settings dialog

Extracts points from cube

Creates a point set from a seismic cube. One point per sample in the cube. Limited by a top and bottom surface

Created point set located in the same survey folder as the input cube

One of the key things about this Toolbox is that we at Blueback see it as an evolving set of tools. As such, the contents are expected to change quite dramatically over time when new functionality is requested and added. For this reason it is important that all users of the Toolbox provide feedback to Blueback as without this feedback we will not be in a position to make any changes or amendments. We would therefore like to know if you find the functionality useful or whether you would like to see any tweaks made to what is already there. Also, we would like to know if you have any suggestions for any additional functionality that you would like us to add to the Toolbox, which would benefit the Petrel user community. If one of these suggestions makes it into the official Blueback Toolbox then I will happily send that member a Blueback iPod.

How to download the Blueback Toolbox The Toolbox is now on our FTP site.

“The elements that unite

to make the Grand

Canyon the most sublime

spectacle in nature are

multifarious and

exceedingly diverse.”

-John Wesley Powell



ftp.blueback-reservoir.com

User: TOOLBOX

Passwd: xxToolbox2009

There are 3 files there:

1 –“ TOOLBOX 1.1 2008.1-306-110209.zip”. This is the installation file if you are

running Petrel 2008 on XP.

2 – “TOOLBOX 1.1 2009.1 32bit-398-180309.zip”. This is the installation file if

you are running Petrel 2009 on Vista or XP 32 bit.

3 – “TOOLBOX 1.1 2009.1 64bit-398-180309.zip”. This is the installation file for

those running Petrel 2009 on Vista 64 bit.

Download the file you need, unzip it and run the installation. Then start Petrel

and open the Blueback License dialog from the HELP pulldown menu. To

activate the Toolbox – you must send to [email protected] the

COMPUTER CODE. This is found if you click the Manage Licenses button.

This download information can be forwarded to anyone interested in taking a look at the Toolbox.

Geo2Flow Dan O’Meara

For those of you who have been a member of the Geomodeling Network for a wee while you will probably recognise the name Dan O’Meara. Dan is the chap who has contributed some very eloquent articles for discussion on our very informative discussion page. To prove to you all that Dan does not spend all of his time posing technical questions to our network and that he does have an actual day job, Dan has contributed an excellent article on Geo2flow. Geo2Flow is a software product developed by O‟Meara Consulting who have

gained respect for developing leading-edge, interdisciplinary tools that “raise the bar” technically in the arena of reservoir characterization. Geo2Flow uses patented technology for identifying reservoir compartments, for calculating 3D permeabilities that are consistent with saturation logs and for ensuring that 3D saturations match their corresponding logs exactly. Integrating Geo2Flow into your workflow ensures that your method for estimating reserves is “best in class”

ftp://ftp.blueback-reservoir.com/




Requests for the newsletter No5 The next newsletter is planned for a May 2009 release, so please send any articles to me at the following email address for inclusion ([email protected]). Finally, please take advantage of the Geomodeling Network discussion board on LinkedIn to initiate comments on any Geomodeling subject of interest to you, or to respond to any of the articles in this newsletter – all I ask is that you respect other people’s opinions.

Fin


march 2009 the geomodeling network newsletter

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