may 2009 the geomodeling network newsletter

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

The Geomodeling Network Newsletter May 2009

Has it really been 2 months since the last Geomodeling Network

newsletter? Based on the number of (kindly) reminder emails I received

in my inbox this morning asking where the May edition is, I think it must

be!

This month’s newsletter is one of the best yet and has contributions from

E&P companies, software vendors as well as a great discussion taken

from our online forum.

Talking of emails, you may have spotted that this newsletter does not

have the Blueback Reservoir watermark running through it. The reason

for this is that a couple (2) of you recently contacted me requesting that

this should be removed and thus making it easier to read. Never being

one to shirk from my responsibilities, (especially when the elderly are

concerned), I have removed the offending watermark – the jumbo-print

version should be available for the next release :o)

Anyway, for the next 20 or so minutes sit back, relax, grab a cup of coffee

and enjoy the latest offering of the Geomodeling Network newsletter.

Many thanks to those members who took the time to contribute the

interesting articles contained in this version, it’s very much appreciated.

And finally, as our network quickly approaches the 800 members mark, I

hope some of you will take inspiration from the articles and discussions of

this (and previous newsletters). If you do get the urge to make a

contribution for future versions, drop me an email with your thoughts –

the next one is not due out until the end of July 2009, so you have plenty

of time!

Mitch Sutherland [email protected]

mailto:[email protected]

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






Table of Contents

1. How “Good Looking” are your Faults?

The first of a series of short articles that will look at faults and fault geometry, using straightforward structural geological principles. Titus Murray & Merrick Mainster - FaultSeal Pty Ltd Page 3

2. Rock types and flow zones

Practical methods for defining rock types, their use in property models and flow zone characterisation. Steve Cannon – Senior Staff Geologist at DONG E&P UK Ltd Page 8

3. The Petrosys Plug-in for Petrel The Petrosys Plug-in for Petrel allows geoscientists and engineers utilizing Petrel to present their insight, integrated with information from many other data sources, through the Petrosys map interface. Scott Tidemann, Global Sales & Marketing Manager at Petrosys Page 18

4. What problems have you had using horizontal well data within your models? This was a question placed on the Geomodeling Network discussion forum which generated a fair bit of response from our members Brian Casey – Geological Consultant at Oxy Page 19

5. EAGE 2009 This year’s event is in Amsterdam. Page 26

6. The Blueback Toolbox (a Petrel plug-in) – update Page 27

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



Member Articles, Reviews & Questions

1. How “Good Looking” are your Faults? Titus Murray & Merrick Mainster, FaultSeal Pty Ltd

This is the first of a series of short articles that will look at faults and fault

geometry, using straightforward structural geological principles. We aim

to help your understanding by showing examples from our software

application FaultRisk that we use on a daily basis when assessing fault

seal capacity in our consulting business.

Within a faulted 3D model it is important to understand the uncertainty

related to the position and throw of the faults in the model. In many of

the models we come across in consulting projects we see that the

structure is “watertight” it is also in some cases geologically improbable.

This is generally due to faults not being imaged in seismic but they are

actually inferred from the absence of a reflector.

Due to the inherent problems of seismic fault imaging, the following

uncertainties arise in:

Position of the footwall;

Throw on the fault;

Shape of the fault;

Stratigraphic thicknesses;

Growth across the fault;

Tectonic inversion.

The best way to define fault displacement is based on detailed well

correlation but wells are generally only drilled on one side of the fault!

Throw Profiles

The displacement on a fault should vary systematically across and down

the fault plane. Faults should have a point of maximum displacement with

a zero throw at the tips of the fault, the throw diminishes radial from the

The most exciting phrase

to hear in science, the one

that heralds new

discoveries, is not

'Eureka!' but 'That's

funny...'

Isaac Asimov




maximum displacement. See the diagram below which shows the footwall

and hanging wall of a faulted stratigraphic layer.

In most faulted reservoir cases the lateral variation of displacement is the key factor and when looking at the displacement it is common to review these profiles.

If the profile is not consistent is it likely that the fault is segmented or

there is another problem with its interpretation in some way as shown in

the diagram below.

Gulfax Field Examples

As an example of this type of analysis we will look at the Gulfax model

that ships as a demonstration example set in Petrel.

Fault polygons have been made from the Petrel grid and imported into

FaultRisk™. The picture below shows the FaultRisk™ mapping interface,

with a structure contour map loaded.

“The use of solar energy

has not been opened up

because the oil industry

does not own the sun.”

Ralph Nader



As the fault polygons are made from the Petrel grid it includes a set of XYZ

coordinates that can be split into hanging wall (down-thrown) and foot

wall (up-thrown) lines. This diagram shows either side of the fault as a set

of points that we can edit or modify.

When reviewing the displacement profile in FaultRisk™ any potential

anomalies in the profiles can easily be identified. In the case below a

“Bow Tie” displacement can be seen.

“The past history of our

globe must be explained by

what can be seen to be

happening now. No powers

are to be employed that are

not natural to the globe, no

action to be admitted except

those of which we know the

principle.”

James Hutton



Looking at another fault from the Petrel model, segmentation along the

fault’s length can be observed.

In this fault there are some anomalous cross cutting faults in the hanging

wall

“The oil can is mightier

than the sword.”

Everett Dirksen



that generate a complex displacement profile.

In an ideal world one would review the seismic data to look for fault

linkages and branch lines. Pragmatically in a fault seal analysis the fault

can be split into two or three segments to look for leak points from the

compartment.

This style of analysis is quick and easy to do and will greatly improve the

quality, and accuracy of your 3D models and help you amend the Petrel

model with good looking faults. When investigating the probability of

fault leakage and/or across fault flow this analysis is a vital step in the

workflow.

The next article will look at throw length ratios and fault segmentation.

If you have any queries on this article or fault seal issues please contact us

at [email protected] and/or visit our website www.faultrisk.com.

One has to look out for

engineers -- they begin with

sewing machines and end

up with the atomic bomb.

Marcel Pagnol


http://www.faultrisk.com/



2. Rock types and flow zones Steve Cannon, DONG E&P UK Ltd

This article attempts to present some practical methods for defining rock

types, their use in property models and flow zone characterisation. Rock

typing is common practice in Middle Eastern carbonate reservoirs

because they tend to be extensively cored with vast conventional and

special core analysis datasets used for petrophysical interpretation. Each

petrophysical data point will usually be associated with a petrographic

description based on thin section analysis, and often SEM data as well:

such comprehensive and consistent datasets are less common in clastic

reservoirs. Such detailed studies can have their downside however,

especially when an over-enthusiastic sedimentologist defines 85

lithotypes in a field where 40% are in non-reservoir sections and the rest

just subsets of the about eight major petrophysical rocktypes; some

simplification is required before they can be used for reservoir modelling.

The list below attempts to define some of the nomenclature commonly in

use to define different levels of description:

Lithofacies/lithotype: the character of a rock described in terms of its

visible components; structure, colour, mineral composition, grainsize,

sorting etc: the smallest scale purely geological description of a rock.

Facies: a mappable unit of rock that forms under certain conditions of

sedimentation, reflecting a particular process or environment. A facies

may be defined by the types of component lithofacies and is an

interpretation rather than a description of any unit.

Facies association: a group of facies that together define a sedimentary

unit with a common depositional setting; again this is an interpretation

based on an understanding of the different components and their process

of deposition. The recognition of a specific facies and facies associations

defines the depositional environment of a group of rocks.

Rock type: a rock with a well defined porosity network leading to a

unique porosity-permeability relationship and saturation profile: the




porosity network is the result of a predictable depositional and diagenetic

history.

Petrofacies/petrotype: terms used to integrate petrophysical

relationships with lithofacies/lithotype descriptions: petrophysics in a

geological context. The term petrofacies is also used by many researchers

to define only mineralogical/petrographical classes.

Flow unit/flow zone: a mappable unit of the total reservoir volume

within which geological and petrophysical properties that effect fluid flow

are internally consistent and predictably different from other reservoir

rock volumes. Other terms used are hydraulic flow unit and genetic

hydraulic unit that attempt to standardise or map different petrotypes

within discrete depositional environments. A flow unit, while ideally

being related to geologically defined depositional package, may not

correspond with discrete facies boundaries and may not be laterally and

vertically contiguous.

Essentially all these terms fall into two categories, either purely

descriptive or largely interpretative: both are important to understand

when characterising a reservoir, especially for dynamic modelling. But

what does all this mean to the different subsurface disciplines? To a

geologist, a flow unit is a discrete facies object such as a channel or a

carbonate shoal; to a petrophysicist it is correlatable zone with similar

petrophysical properties; to a reservoir engineer it is a layer in a model

that has a consistent and predictable dynamic response to flow in the

simulator. To a reservoir modeller it is all of these things! Petrophysicists

and engineers still often think in terms of simplified zone average

property models as the way forward in determining in-place volumes and

reserve estimates, hence the concept of discrete zones rather than the

more stochastic idea of reservoir objects with common petrophysical

properties.

Amaefule et al (1993) developed a method of reservoir description using

core and log data to identify hydraulic flow units and predict permeability

in un-cored intervals. This method has been used in many ways to define

both rock types and flow units, and is based on well founded

experimental methods developed over many years. The method is

depends on understanding the pore geometry of a rock and relating this

to the mean hydraulic unit radius of the pore throats: mean hydraulic



radius realtes porosity, permeability and capillary pressure

measurements. A similar approach was published by Kolodzie (1980)

based on work down by Winland of Amoco. Pore geometry is a function

of the mineralogy and texture of a rock, which means that different

lithofacies may have similar pore throat attributes: in this way different

facies may belong to the same rock type.

Theoretical background

The basis for all rock typing is Darcy's Law and Pouseille's theory for

capillary bundles under laminar flow: these are used to derive a

relationship between porosity and permeability for a capillary bundle.

The mean hydraulic radius is function of grain surface area (Sgv) and effective porosity.

Carmen and Kozeny obtained the following relationship by substituting

for mean hydraulic radius.

where F is a shape factor, which for a circular cylinder is 2. In real rocks

the Kozeny constant (Fsτ2) can vary between 5 to100. Because it is a

"variable constant", varying between hydraulic units in a reservoir a

2

22

22

2

2228

mheee rrrk

Equation 1

e

e

mhe

egv

rrS

1

1

1

2

Equation 2

222

31

1 gvse

e

SFk

Equation 3

“It puzzles me how they

know what corners are

good for filling stations.

Just how did they know

gas and oil was under

there?”

Dizzy Dean



further mathematical transformation is required: dividing both sides by

effective porosity and taking the square root of both sides the following

relationship is derived:

where permeability, k is in μm2.

Presenting permeability in millidarcies, then a Reservoir Quality Index

(RQI) can be defined:

The Flow Zone Indicator (in μm) is related to RQI by the term

where φz is the ration between pore volume and grain volume.

Thus Flow Zone Indicator,

On a log-log plot of RQI against φz (PhiZ) all samples with a similar FZI will

lie on a straight line with unit slope; samples with other FZI values will lie

on parallel lies. Samples that lie on the same straight line have the same

pore throat attributes and therefore constitute an hydraulic unit, even

though they may represent different facies. Permeability can also be

calculated from this relationship using the appropriate hydraulic unit or

FZI relationship:

gvse

e

e SF

k 1

1 Equation 4

e

kRQI 0314.0

Equation 5

e

ez

1

zgvs

RQI

SFFZI

22

1

Equation 6

2

3

2

1)(1041

e

eziFk

Equation 7

“Sometimes, I guess there

just aren't enough rocks.”

Forrest Gump



Corbett et al (2005) present an excellent case study of braided river

sandstones which goes through the workflow and offers some interesting

interpretations of the effect of grain-size and sorting on capillary pressure

measurements.

Workflow

There are two main phases to the process; firstly analysis of core data to

determine the various petrophysical relationships and secondly

application within a reservoir modelling workflow where the relationships

are integrated with log data.

Data analysis

1. Routine core porosity and permeability data should be characterised in

terms of an appropriate lithofacies or facies scheme. It is essential to

have a consistent dataset, and any outlying data removed. To ensure

data integrity later in the process it is important that depth

correspondence between core and log data is accurate. The data should

be plotted in the normal way and general porosity permeability

relationship established for one or two dominant facies groups, if

sufficiently different: these can be used to produce a permeability curve

from the wireline log derived porosity.

2. In a spreadsheet, the flow terms PhiZ, RQI and FZI should be calculated

for each core analysis point and sorted in order of decreasing values of

FZI. As a first approximation the results can be plotted as two groups,

greater than and less than a value FZI equal to one on a log-log plot of

PhiZ against RQI: the better rock types will be greater than one. Any

recognizable rock type variations will be apparent as parallel groups of

data. Generally this will reveal the better rock types, and correspond with

those that have previously been identified from the facies breakdown as

better quality.

3. Plotting porosity against permeability classified by FZI will allow the

generation of a predictive permeability relationship for each rock type.

The quality of the predicted permeability can be compared with the

original core permeability using a Q-Q plot: an organised plot of

comparing the same points Alternatively the relation shown in Equation



7 can be used to predict permeability and again be compared with the

original data.

Application

4. Within the reservoir modelling package calculate the FZI terms using the

log derived porosity and permeability. Using a property calculator

develop a series of logical statements to classify each of the rock types

according to the core defined scheme. Visually check the results against

cored intervals to ensure reasonable correspondence and consistency:

anything over an 80% correspondence is acceptable; 60 to 80% is a

common result. The key is to ensure that the extremes are captured,

especially low permeability layers that could form barriers/baffles, and

high permeability streaks that might dominate flow in the reservoir.

5. Block the data to grid scale and check that the detailed description of rock

types is retained in the upscaled well data.

6. Either use the rock types directly to populate a model zone or build a

detailed facies model and populate each facies/object with the

appropriate rock type. Reservoir properties, porosity, permeability and

water saturation can then be distributed according to the rock type

relationship defined in the analysis stage. When modelling Sw, a direct

link to core-based capillary pressure data can be established with respect

to height above a local or regional free water level. The value of SCAL

data cannot be over-emphasised; capillary pressure measurements

should be representative of the different rock types recognised. A data

base of as little as ten samples can be sufficient to characterise a series of

reservoir rocktypes; fifty is even better!

Other applications

In an attempt to standardise all possible lithofacies in terms of hydraulic

flow zones, Corbett & Potter (2004) used the Amalaefule methodology to

create 10 Global Hydraulic Units (Figure 1) that utilised fixed FZI lower

boundaries against which they plotted core derived porosity and

permeability from different depositional environments. This is an

alternative to the clustering method described above and relies on

"The box said 'Required

Windows 95 or better'. So,

I installed LINUX."



mapping these predetermined classes on the porosity-permeability

crossplot; they coined the term petrotyping.

Because the rock types in the petrotyping

approach are "global" in the sense they are

predetermined, the base map can be used

to determine whether a reservoir

comprises one or more rock types.

Different reservoirs can be compared

quickly using this method as well as a rapid

technique for screening and selecting

samples for further analysis. This method

can also be used to decide the appropriate

scale of cells needed to capture the

porosity and permeability distribution in a

reservoir model; ideally at the smallest

scale,

each grid block should contain an individual global hydraulic unit.

Using an example data set from a series of braided river deposits, Corbet

et al (2005) demonstrated the workflow output. Figure 2 shows the

results of the PhiZ:RQI log-log plot and the four hydraulic units indentified

with the corresponding FZI values. Figure 3 recasts the data in terms of a

familiar Phi:K plot with the predictive relationships calculated for each

hydraulic unit or rocktype. Figure 4 shows the result of grainsize and

sorting analysis for each hydraulic unit/rock-type: grain size shows a clear

contrast whereas sorting has little impact.

Conclusions

Rock-typing can be a challenging process, but ultimately very satisfying:

engineers would much rather talk about a rock-type than a lithofacies

because it infers some sort of numerical consistency, even when it is

directly related to depositional unit! But these methods must be used

FZI Global Hydraulic Unit

48 10

24 9

12 8

6 7

3 6

1.5 5

0.75 4

0.375 3

0.1875 2

0.0938 1



with care; a detailed understanding of what rock-types might be expected

and how they can be grouped is required. Rock-typing works when the

geologist is in control of the input and the output, especially when that

output is going to be used to populate a static model.

References

Enhanced Reservoir Description: Using Core and Log Data to Identify

Hydraulic (Flow) Units and Predict Permeability in Uncored

Intervals/Wells: Amaefule et al, SPE 26436 (1993)

Use of Flow Units as a Tool for Reservoir Description: A Case Study:

Guangming et al, SPE 26919 (1995)

Permeability Prediction by Hydraulic Flow Units – Theory and Application:

Abbaszadeh et al, SPE 30158 (1996)

Early Interpretation of Reservoir Flow Units Using and Integrated

Petrophysical Method: Gunter et al, SPE 38679 (1997)

Petrotyping: A basemap and atlas for navigating through permeability and

porosity data for reservoir comparison and permeability prediction:

Corbett & Potter, SCA2009-30 (2004) (Society of Core Analysts)

The geochoke test response test response in a catalogue of systematic

geotype well test responses: Corbett et al, SPE93992 (2005)

"If at first you don't

succeed; call it version

1.0"



Figures

Figure 1: Global hydraulic units as applied to a shallow marine sandstone

Figure 2: Log-log plot of PhiZ against RQI used to define different hydraulic units

Plot of RQI vs. Phi(z) for well X2

0.01

0.1

1

10

0.01 0.1 1

Phi(z)

RQ

I

HU-1 FZI = 2.509

HU-2 FZI = 1.233

HU-3 FZI = 0.685

HU-4 FZI = 0.323



Figure 3: Porosity-permeability cross-plot broken down by hydraulic units

Figure 4: Impact of grainsize (violet) on definition of hydraulic units

Crossplot of (k vs. Phi) for different

Hydraulic Units, Well X2

0.0001

0.001

0.01

0.1

1

10

100

1000

0 0.05 0.1 0.15 0.2 0.25

Phi, frac.

k, m

D

Grain Size and Sorting for each HU

0

0.5

1

1.5

2

2.5

3

3.5

4

G7HU1 G7HU1 G7HU2 G7HU3 G7HU4 G7HU5

Hydraulic Units

Gra

in S

ize

and

So

rtin

g

, P

hi

Un

its



3. The Petrosys Plug-in for Petrel Accelerate exploration, improve productivity. Get collaborative mapping results more easily. Scott Tidemann, Petrosys

The Petrosys Plug-in for Petrel allows geoscientists and engineers utilizing Petrel to present their insight, integrated with information from many other data sources, through the Petrosys map interface. This enables asset teams to accelerate decision making through consistent use of Petrosys mapping and surface modelling as their focus moves from the regional overview through the field to the reservoir scale.

Australasia: +61 8 8227 2799 > Americas: 1888 PETROSYS > Europe: +44 141 420 6555 > Calgary: +1 403 537 5600 > Web: www.petrosys.com.au

Harness the power of the Petrosys plug-in for Petrel to:

Start Petrosys mapping, surface modelling or 3D viz from icons in the Petrel application.

Effectively map and present opportunities by directly incorporating Petrel 3d seismic horizons and 3d model grids using Petrosys map colorfill and 3D viz displays. Compute and map contours for the structures.

Integrate decision making, using a range of other Petrosys display options to overlay geoscience and cultural data from OpenWorks, GeoFrame, ArcSDE, SMT, PPDM and many other data sources directly accessible through Petrosys.

Map in many coordinate reference systems (CRS); the underlying CRS of maps can be switched to effectively map surfaces in regional interpretation situations.

Your vital information comes together, with both applications working side by side to support collaborative workflows and understanding.

When NASA first

started sending up

astronauts, they

discovered that pens

would not work in zero

gravity. To combat this

problem, NASA

scientists spent a decade

and $12 million

developing the ball point

pen that writes in zero

gravity, upside down,

underwater, on almost

any surface including

glass and at

temperatures ranging

from below freezing to

over 300C.

When confronted with

the same problem, the

Russians used a pencil.




Use Petrel seismic data as a direct input data source in Petrosys gridding workflows.

Effectively combine 2d & 3d interpretive workflows, using Petrosys surface modelling functions such as volumetrics and well tie. Use direct data inputs and efficient import/export facilities, while creating repeatable workflow processes.

Import faults, model grids/horizons and seismic data to Petrosys. Export Petrosys grids directly into Petrel projects. * Petrel is a mark of Schlumberger.

4. What problems have you had using horizontal well data within your models? Taken from a discussion posted on the Geomodeling Network discussion forum. Brian Casey, Oxy

Many modern fields are dominated by horizontal wells, but modelers are reluctant to use this data due to: - Zonal bias - Imprecise tops - Imprecise well path locations - Other?

Petrosys effectively and efficiently handles the mapping of Petrel models, including faults, colorfill display and posting of surface values.


http://www.google.co.uk/imgres?imgurl=http://www.dwahzonsvillage.com/images/geological-map.jpg&imgrefurl=http://www.dwahzonsvillage.com/index3.html&h=309&w=468&sz=38&tbnid=nwYcy4r6A6wLmM:&tbnh=85&tbnw=128&prev=/images?q=picture+of+geological+map&hl=en&usg=__5a5P5sYfr300j2wkz2FxAmY690U=&ei=mz4lStWMEoqQjAe71NHiBw&sa=X&oi=image_result&resnum=1&ct=image



We handle zonal bias through debiasing workflows. Imprecise well paths and imprecise tops can be managed through careful data selection and the use of Zone Log. Do group members have other modeling issues and solutions for the use of horizontal well data in their models? Brian

Li bin – geologist, Tiandi Energy The well path locations are relatively reliable, the tops could be corelated using chosed logs. the reservior quality and facies also could be analysed and evaluated. all these information can be used in modeling Samir Benmahiddi – production geologist, Sonatrach the way I see it, regardless the zonal bias, continuous lateral data sampling from a decent number of horizontal wells, well distributed throughout the reservoir, should help to check / adjust the main reservoir attributes anisotropy and variograms, should provide hints on lateral heterogeneity and deposits architecture, particularly if you come to run imaging tools (which is quite difficult I agree but still less than coring, and always worth to try) with intent to collect some imagefacies and dipmeter data allowing much better facies mapping at least. But still it should be framed with a robust sedmentary and stuctural conceptual model and avoid mixing fractures/faults with simply a lamination of high contrast steeply dipping on image log ! Otherwise, tops issue is only important when target is a tiny window and you add up uncertainties on depth because the cable length stretch and such ...which is less likely to happen.

Anders Ørskov Madsen –Senior Consultant, Blueback Reservoir I use horizontal well data regularly for building reservoir models. Intead of using the whole horizontal well I have in some cases simply cut out a section where I had high confidence to which zone/stratigraphic unit it was drilled through. In other case I have made a pseudo trajectory for the well due to the depth uncertainty for long horizontal wells, in order to place it correctly in the model. Petter Abrahamsen –Research Director, NCC

We are curently making a software for getting the surface consistent with the zonation in wells. This assumes the zonation is correct. A (vertical) correlated uncertainty on the well path location is possible to include but hasn't been prioritized so far. The easy part is to ensure that surfaces cross the well trajectories at the correct locations. The hard part is to ensure that they do NOT cross the trajectories at the wrong locations.



Here is a link with some more details: http://www.nr.no/pages/sand/area_Cohiba The introduction in the manual (pdf) gives a good overview. We essentially use kriging to interpolate the well picks. The challenge is to use the additional constraints from the horizontal wells in a consistent manner so that we can provide realistic uncertainty description in terms of simulations (Monte Carlo) and prediction errors. Thorbjorn Pedersen –Chief Geoscientist, Oxy

Interesting to see Madsen's approach using a pseudo trajectory to place the horizontal well "correctly" in the model. What is the depth accuracy of the model in the first place? Do you have vertical well tops near the toe end of the Horizontal trajectory? Or is the depth model for horizons constrained by depth converted seismic horizons? In that case what is bigger, the depth uncertainty to the depth conversion or the depth to the toe end of the horizontal well? Holger Rieke –Principal Geologist, StatoilHydro

Peter, Could you please elaborate why you choose kriging for the interpolation between well picks? Thickness of reservoir zones or the depth surface to well tie are not accurately computed using kriging unless you select a large variogram i.e. at least half the distance of well spacing. The kriging result then resembles convergent or global b-spline (depending on which software you prefer). Those algorithms are in my opinion more suitable to extrapolate these types of data. Petter Abrahamsen –Research Director, NCC

The reason we using kriging is to be able to quantify uncertainty. The uncertainty is (indirectly) described by the shape of the variograms and the standard deviations (sill). The depth uncertainty is quantified by prediction error maps (kriging error) or by a set of simulated realizations depending on usage. The shape of the variograms can be chosen so that the result is similar to spline interpolation. This is visually appealing but rarely realistic in natural phenomena. Moreover, the variogram shapes and the sill can be estimated from well picks so that we can confirm consistency between interpolation method and data. We never use zone thickness data directly since these are only available in vertical wells. We always use surface depth data (well picks) and

“It is only when they go

wrong that machines

remind you how powerful

they are”

Clive James

http://www.linkedin.com/redirect?url=http%3A%2F%2Fwww%2Enr%2Eno%2Fpages%2Fsand%2Farea_Cohiba&urlhash=0V-f&_t=tracking_disc



constraints on the surfaces in the horizontal sections. This is to avoid the use of pseudo-data that are hard to make and even harder to justify. Finally note that we consider many surfaces, and the intervals (zones) between them, simultaneously. Variograms for all interval thicknesses are specified. This can amount to 20 or more different variograms. The big advantage of considereing all surfaces simultaneously is that well data influence surfaces below and above. In particular horizontal sections in thin zones lock surfaces above and below very accurately. I hope this didn't obscure things rather than clarified them :-). Thorbjorn Pedersen –Chief Geoscientist, Oxy

I like Petter's approach assessing all surfaces and their associated variograms at once. This allows for a holistic look into the relationship between surfaces, their controlling data and subsequently the total structural/stratigraphic architecture. However, dependent upon the well density and geologic setting, I would still maintain that from time to time usage of zone thickness data may be required to maintain a morphology that is in line with the respective sedimentological setting defined by core data or infered from regional context. These cases generally tend to be used in models of fields in their early stages of development. Tim Wynn –Senior Reservoir Geologist, AGR-TRACS

We have built several models with a large number of horizontal wells and found problems with the use zone logs option in Make Zones (zone logs not honoured, random spikes etc). In a large 'layer cake' stratigraphy reservoir we used pseudo tops shifted by the requisite isochore thickness, this was quite successful, particularly a there was no seismic data constraint. However, care had to be taken around the faults so it was quite time consuming. We have considered using psuedo well paths (polygons) clipped to the required zones and shifted up by an arbitrary amount. These polygons could then used as part of the input data for the surface. These would only be required where the surface cuts a well where it shouldn't Both these options are quite time consuming and result in pseudo data so they are not perfect but they do ensure the surfaces honour the zone logs.

“Geologists don't wrinkle,

they show lineation”



Anders Ørskov Madsen –Senior Consultant, Blueback Reservoir Answer to Thorbjorn question about 'pseudo trajectory' It depends on the well density. In cases where I have shifted the horizontal well trajectories it has usually been on fields with a bunch of vertical or deviated wells near the hz wells highlighting the depth uncertainty of the horizontal wells. In many cases the hz wells have been more than 25000 ft MDRT with an depth uncertainty of +/- 50 ft TVD at TD (e.g. chalk wells in the Danish North Sea), so it has been neceassry to shift these wells in order to use them as input in a 'base case' structural depth model. Petter Abrahamsen –Research Director, NCC

Note that we can use trends for the zone thicknesses so we can impose interpreted sedimetological trends. The trends can be globally adapted to data (well picks and trajectories) or kept untouched. The simplest trend is of course a constant (e.g. 20m). The kriging essentially interpolates the difference between the trends and the observed data. Trends can also include velocity fields, travel times, anomalies, pinch outs and all kinds of weird geological features but thats another story. As Tim Wynn comments, the biggest challenges are really areas close to faults where simple layer cake models can fail. Normal faults will squeze zone thicknesses to zero and this requires special care to avoid opening up the faults. Reverse faults are even worse since this requires multi-z values at surface locations near the fault. This is currently not handled but we are discussing how to integrate surface and fault models in a proper and efficient way. Brian Casey –Geological Consultant, Oxy

Further to Anders comments, it is not just extremely long reach horizontal wells with + 50 ft TVD error that should concern us. Horizontal well placement is relative to our grid dimension, so both the geologist and simulation engineer should be concerned. Even if no static properties are attributed to a horizontal well, dynamic performance must still be matched. If the well is mis-located in the grid the engineer will make the adjustments. Better to be pro-active… Thank you to Petter and everyone else for contributing to this discussion. We may wish to test some of these processes for placing horizontal wells “correctly” in the model. Also, I had not previously considered Petter’s approach toward horizontal well placement uncertainty. I can see a lot of applicability, in both the static and dynamic modeling. There is considerable support for using the static properties of horizontal

To the optimist, the glass

is half full.

To the pessimist, the

glass is half empty.

To the engineer, the glass

is twice as big as it needs

to be



wells, and as Samir observed, these wells provide information on lateral heterogeneity and depositional architecture. [that we might not observe in the vertical wells.] How we handle that lateral reservoir data may need further discussion. Petter, perhaps you would like to further address handling multi-z values in the case of reverse faults (and recumbent folding), and the proper integration of fault and surface data. I would be very interested in your thoughts and approach, but do not wish to bury that discussion within one about horizontal well placement. It deserves its own topic. Juan Cottier –Subsurface Manager, Blueback Reservoir

2 ideas to add in here of a purely pragmatic nature: Firstly, I have built all sorts of models (in PETREL) using horizontal wells (West African, UK, Danish and Norwegian producing fields) and have found that from "get the job done" approach that if one takes the MAKE HORIZONS and MAKE ZONES steps slowly and try to achieve incrementally improving results then a decent job can be done in most cases. In PETREL I tend to turn off the "use in geomodeling" option for most horizontal wells and repeat and repeat layer by layer slowly turning on each well or even each individual well top. This allows simple QC of the results and the "problem children" can be more easily identified. Secondly, those of us who have been around more than 10 years have seen how deviation surveying has changed and how so much more confidence can be put upon surveys. However, MWD surveys still do not get close to a wire/slickline survey in terms of accuracy so it is worth checking where you survey comes from. Also if drilling we'll often get a MWD survey and then some days later there maybe a wireline survey ... so has the project trajectory been updated? Anoother little wrinkle I came across was the elipses of uncertainty on MWD surveys in particular. As they are based upon Hall's Effect the uncertainty varies in geographical regions and azi/inclination. In the Ivory Coast we were drilling horizontal wells to the south, running along the earth's magfield and the lateral uncertainty was huge. Similar thing for UK north sea t 60 degrees (ish) Right ... I'm dragging on a bit, I'll stop now. Keith Milne –Petroleum Geologist This subject has generated a lot of discussion because we regularly come across fields with a mixture of vertical and horizontal wells. Regarding the point about producing a sensible zonation, lets not forget



that the well picks and position of the horizontal well takes more interpretation that a vertical well and there may be more than one alterative. I do not expect any software to be able to solve the problem without some additonal data points to control the zonation, especially if seismic control is poor or if the borehole passed through a fault. If logs have been run that enable dip to be estimated, then this will assist in determining the zones along the well. One approach is to make a cross section before trying to model - this is what would be typically done during actual drilling of the well, using all available data sources. Knut Midtveit –Sales Manager, Roxar I have seen many attempts to solve the problem of handling horizontal wells and getting the model to honour the well data including zonelog in horizontal wells. This range from manual thus very tedious approach to clever scripting methods, to what I feel is a more holistic approach that Peter Abrahamsen talk about, so I look forward to that. Roxar has been including adjust model to zonelog functionality for a couple of years, and we are now seeing it being successfully use on some pretty large fields with many horizontal wells. I find it interesting to observe that some companies focus a lot on this and spend lot of time getting the model right in order to plan wells optimally, and others accept that no one has a good solution. Furthermore some companies regularly shift well positions manually though, and others object strongly to the concept of shifting the well position. Personally I hope that we will get a solution where you consider all your data with a certainty and allow both seismic envelopes and wells to be changed according to the uncertainty of each data type. Until we have a such a solution try the adjust to zone log in RMS, and yes it can handle faults to. Ahmad Nazhri Mohd Zain –Geological Modeler, Saudi Aramco

How do we handle the large upscaling issue with regards to the horizontal wells?. I assume that having a 25m x 25m grid lateral grid dimensions is acceptable to take into account the horizontal section of the wells, thus for a 1km horizontal section, you will have around 40 grid cells. I work with Ghawar and I cannot have anything smaller than 250m x 250m grid spacing otherwise my static model will be in the hundreds of millions cells. How do I handle 6000 data points (6 inch sampling rate) in a 1km horizontal section? That 1km will be blocked into 4 grid cells only. You will

"If GM had kept up

with technology like the

computer industry has,

we would all be driving

$25 cars that got 1000

MPG."

Bill Gates



not be able to compare the blocked wells to the raw dataset as the horizontals will introduce a severe bias due to the amount of samples in the horizontal sections. This is a major issue at the moment in our modeling group. Petter Abrahamsen – Research Director, NCC

For surface modelling this is not a big problem in practice since the well geometry is very regular. Sampling the well at approximately the grid spacing is sufficient in our experience. We can go back and check all the data points although this is hardly necessary (in our experience). We have tested our approach on Troll (North Sea) which is a giant field. But working with Ghawar is of course an even greater challenge due to the volume of data. It would be nice to do a practical test on such a huge field... Upscaling for petrophysics is of course a different issue since comparing e.g. permeability on plug scale and on modelling scale is non-trivial.

5. EAGE 2009 – Amsterdam 8th to 11th June http://www.eage.org/events/index.php?eventid=103 I am sure that a lot of you will have attended, exhibited and indeed presented at previous EAGE’s. In the past, these conferences have been held in fantastic cities such as Rome, Vienna and Madrid, as well as Leipzig. The event organizers have chosen another great city to host this year’s event, with Amsterdam being the chosen one for 2009. There are many things that pop into my mind as I think about Amsterdam (notably canals and tulips and a certain brewery). However, distractions aside, the EAGE is shaping up to be quite an event, not least for Blueback Reservoir. Throughout the entire conference, Blueback will be exhibiting at stand #2538 where we will have a number of staff available to discuss geomodeling consulting opportunities, Bridge and the Blueback Toolbox, software development on the Ocean framework, as well as the Geomodeling Network itself.


http://www.eage.org/events/index.php?eventid=103

http://www.fossilmuseum.net/Fossil_Galleries/TrilobitesRedlichiida.htm

http://www.google.co.uk/imgres?imgurl=http://www.thewinestreetjournal.com/images/Picture_Beer_HeinekenKeg.gif&imgrefurl=http://www.thewinestreetjournal.com/SlickNicksPickArchive2.php&h=323&w=234&sz=45&tbnid=33m5pFcgGL0oQM:&tbnh=118&tbnw=85&prev=/images?q=picture+of+heineken+beer&hl=en&usg=__5e8m4NCAsc3aVMsckIuFLdozUM8=&ei=Dk0lSvaCJNeQjAe5p8HqBw&sa=X&oi=image_result&resnum=3&ct=image



If any of you are attending this event then please feel free to swing by our booth for an informal chat and some Blueback hospitality.

6. The Blueback Toolbox (a Petrel plug-in) Our Toolbox has been widely available for a month or so now and already we are seeing a great take-up with this FREE software. Indeed the reception we have received for the Toolbox is such that users are already seeing the benefits to using plug-in technology to supplement their existing Petrel workflows. A few users have already requested additional plug-in suggestions which we are planning to have ready for the next free release of the Blueback Toolbox – these suggestions include:

- Facies Maps - Shift Well Log - Merge Seismic Cubes - Cube flattening seismic volume attribute - Make empty seismic cube If you would like access to the Blueback Toolbox, then please refer to the March 2009 edition of the Geomodeling Network newsletter on how to download the software and request a license. Or drop an email to [email protected] and he will get back to you with information on what you need to do.

Requests for the newsletter No6 The next newsletter is planned for a July 2009 release, so please send 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




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may 2009 the geomodeling network newsletter

Technology

fault displacement

petrosys page

fault geometry

geomodeling networknewsletter

oxy page

blueback reservoir watermark

petrel plugin update

downthe fault plane