Seismic velocity modeling – 3D grid basedObjectives

• Create a simple 3D grid

• Create layers in the 3D grid

• Sample the interval velocity data points into the 3D grid

• Populate the 3D grid using Ordinary kriging

• Sample well velocity into the 3D grid

• Calibrate the interval velocity property – anisotropy factor• Calibrate the interval velocity property – anisotropy factor

• Convert calibrated interval velocities to average velocities

• Setup Velocity model – calibrated seismic velocities

• Do Checkshot velocity modeling guided by seismic velocities

� Ordinary kriging with trend

� Collocated co-kriging - optional

• Setup Velocity model – checkshots guided by seismic velocities

• Quality control the velocity models

Load:

• Seismic DIX

Make seismic

velocity surfaces

corrected to well

velocities

Setup velocity

model

using velocity

surfaces

Seismic velocity workflows

• Seismic

velocities

• Checkshot

surveys

DIX

conversion

Set up 3D grid and

model seismic

velocities together

with checkshot

velocities

Setup velocity

model

using 3D grid

velocity property

Recommended workflow:

• Load SEGY or ASCII seismic Interval velocities into Petrel

• Sample the seismic interval velocities into a 3D Grid

Petrel Velocity modeling can use an average seismic velocity field property of the 3D Grid

Seismic velocities - 3D grid modeling

• Sample the well interval velocities into the 3D Grid

• Derive the anisotropy factor from the well data and the seismic data and apply it to

the velocity field

• Quality control the calibrated interval velocity field

• Convert the interval velocity field into an average velocity field

• Setup a velocity model using the calibrated average velocity property

It is recommended to model interval velocities instead of average velocities because they are easier to QC :

• Interval velocity is a petrophysical property

• Average velocity shows only smooth velocity variations

• It is difficult to locate the origin of velocity anomalies shown by the average velocity field

Interval velocity versus average velocity

Seismic interval velocities Average velocities, derived from

interval velocities

Choose a grid spacing of approximately the horizontal and vertical velocity data sampling

Reasons:

• The number of grid cells has a big influence on the performance of the chosen gridding algorithm

• A smaller horizontal grid spacing would make sense if local velocity variations should be captured.

However due to their nature seismic velocities generally can only provide a reliable regional velocity

trend; therefore strong local variations should be filtered

Setup of a 3D grid - consideration

trend; therefore strong local variations should be filtered

• A small vertical layering is not necessary because the velocity field varies smoothly with depth

Setup of a simple 3D grid

• Input Data: enter the constant surfaces for top (0 ms) and bottom of the velocity cube. Include the time

surfaces that are defined as velocity boundaries

• Geometry: use Automatic (from input data/boundary) and choose a grid increment of approximately the

velocity location distance

• Tartan grid: allows to create grids with non-uniform refinement

Setup of a 3D grid - Layering

• In general the type of layering is not of critical importance

• For Proportional layering the number of layers defines the layer thickness. Use trial & error to get a layer

thickness similar to the vertical velocity sampling spacing: display an Intersection in a 3D window and

measure the layer thickness. Then adjust the number of layers

Sampling of seismic velocities into 3D grid

Sampling done via the Scale up well logs

process under Property modeling

Sample checkshot interval velocity into 3D grid

Open the Scale up well logs process

Upscaled check shot velocities

and wells

Choose Point attributes and select the checkshot

survey of interest

Select the interval velocity attribute of this data set

Three suitable gridding algorithms:

Functional - Moving average - Kriging

Important parameters:

Functional, Moving average

- Use a vertical range that covers 2 or more velocity samples

- Use Inverse distance (squared) as point weighting

Kriging

Gridding of seismic velocities

Kriging

- Select Ordinary from the Expert tab. It is more suitable for velocity interpolation than Simple

because it uses a locally varying mean. Consequently trends in the data are better honored.

Note:

Kriging is an optimized algorithm introduced in Petrel 2008. It shows very good performance and offers

collocated co-kriging and kriging with trend.

‘Kriging by Gslib’ shows a low performance and should not be used any more!

‘Kriging Interpolation’ does not provide Ordinary kriging option and should no longer be used.

Vertical range: 10Vertical range: 10 Vertical range: 1000

Vertical range: 1000

Gridding of seismic velocities - moving average

Point Weight:

Inverse distance

quadruple

The ‘artifacts’ are the upscaled velocity data:

• Increasing the Vertical range will smooth (average) the velocities vertically

• Increasing the horizontal distance (from Inverse distance quadruple to Equal) will average the velocities

horizontally

• The upscaled data are not changed and may appear as ‘anomalies’. They are adjusted through filtering of the

velocity property

Vertical range: 10

Point Weight:

Equal

Vertical range: 1000

Point Weight:

Equal

Point Weight:

Inverse distance

quadruple

Vertical range: 10

Point Weight:

Inverse distance

quadruple

Vertical range: 10

Point Weight:

Equal

Gridding of seismic velocities - moving average

after filtering

Vertical range:

1000

Point Weight:

Inverse distance

quadruple

Vertical range:

1000

Point Weight:

Equal

• Select Smooth from the Property

operations

• Typically a couple of iterations are

sufficient for removing artifacts

Comparison of kriging options

Ordinary kriging: The mean value is locally

varying. Therefore Ordinary kriging is less

sensitive to the Range

Simple kriging: In the case of a small Range the

mean value of all input data is influencing the value of

the cells between the data points

Ordinary kriging Simple kriging

Range:

700/700/50Range:

7000/7000/500Range:

7000/7000/500

Range:

700/700/50

Horizon

Grid spacing: 900ft, 100ms

1. Sample the interval velocities of the checkshot surveys into the 3D Grid

2. Calculate the anisotropy factor (using the Property calculator):

F_Anisotropy = Vint_Wells / Vint_Seismic

3. In a Function window approximate the anisotropy factor by a polygon

4. Multiply the seismic interval velocity property with the polygon (in the Property calculator)

5. Convert the interval velocity property to average velocities

Anisotropy handling – Workflow

5. Convert the interval velocity property to average velocities

6. Setup and execute the velocity model based on calibrated average velocity property

7. Address the remaining residual depth error through a second depth correction (depth

scaling) model

/ =

Calculating anisotropy

• Calculation of anisotropy is done with the Property calculator

• To plot the anisotropy as a function of TWT you need to create the

property TWT using the calculator: TWT=Z

Correcting for anisotropy

* =

Use the Property calculator to multiply the

velocity cube with the anisotropy polygon

(dropped in using the blue arrow)

Cross plot function Property calculator

Anisotropy handling – quality control

/ =

• The residual anisotropy is of random character, it should not show any trend

• The depth error caused by the residual anisotropy will be addressed through the depth error correction

Well velocities Calibrated seismic

velocities

Residual anisotropy

Converting interval velocities to average velocities

Using the Workflow editor :

• You need to create the following properties prior to the application of the Workflow above:

� Cell height

� V_Average

• Use the Property calculator and create dummy properties with these names

Setup of a velocity model based on seismic

velocities

Layer cake model set up with a 3D grid average velocity property based on seismic velocities

Velocity model based on seismic velocities –

well correction

Velocity field after depth

correction based on well topsDifference between

original and corrected

average velocity field.

Note the effect of the

radius of influence

Parameter settings for the depth

error correction. Choose an

‘influence radius’ to limit the range

of influence of the depth errors

Note: The ‘Interpolation method’ is of no

influence!

Setup of a depth correction model

Residual error after well top

correction of a depth surface

provided by a seismic velocity modelSet up of a depth correction model for

addressing the residual error

Sometimes a residual depth error of several feet still exists

after well top correction. This error needs to be addressed

by a depth error correction model

Pitfalls in velocity modeling

• Influence of selected gridding algorithm

• Interpolation along layers – SimBox gridding

• Selecting proper gridding parameters

Influence of gridding algorithm

Potential problem:

The velocity increase with depth is not correctly handled by the selected gridding algorithm

Layered velocity interpolation

Gridding is done in SimCube: interpolation along layers

Moving average with Point Weight: Equal does too much smoothing

Choice of proper gridding parameters

Point Weight: Equal Point Weight: Inverse distance squared

Algorithm: Moving average

Increasing

velocity with

depth

Same velocity

within layer

Workflow :

• Convert the checkshot data to interval velocities

• Load the point set into the 3D grid

• Interpolate the data using the seismic velocities as secondary input in Kriging with trend or Moving

General idea : Model checkshot data in a 3D grid guided by seismic velocities

Modeling of checkshots

• Interpolate the data using the seismic velocities as secondary input in Kriging with trend or Moving

Average/Functional with trend

• Convert the resulting interval velocity cube into average velocities

• Setup a Velocity Model based on the modeled checkshot data

• Setup a depth correction model for addressing the residual depth error

Note: Collocated co-kriging generally

delivers poor results unless you have

many wells with checkshot surveys!

Vwells – kriging w/trend

Modeling checkshot interval velocities – kriging

w/trend

Kriging of checkshot interval velocities

using seismic interval velocities as trend

VseismicSeismic interval velocities

V seismic

Note: Kriging with trend of checkshot

velocities deviates from calibrated seismic

interval velocities by less then 4%

Deviation of velocities based on Kriging

with trend from calibrated seismic

velocities:

F_Trend (TWT) =

V_Kriging_Trend / V_Seismic_Calibrated

Vwells - kriging

Kriged interval velocities

(checkshot surveys)

Modeling checkshot interval velocities -

collocated co-kriging

Seismic interval velocities

Check shot average velocities,

Collocated co-kriged with

seismic velocities.

Correlation coefficient: 0.96

Comparison Co-Kriging with calibrated

seismic velocities :

F_CoKrig (TWT) =

V_Co_Kriging / V_Seismic_Calibrated

Note: with increasing depth, collocated co-kriging

delivers too small velocities. Probably there are not

sufficient checkshot points to deliver a reliable result

Comparison of depth errors

Depth error (no well top correction)

of 3 surfaces using velocity model

Kriging with Trend

The depth error comparison between the two velocity models shows that the model based on checkshots

using Collocated co-kriging does not give satisfactory results (at least in this example)

Depth error (no well top correction)

of 3 surfaces using velocity model

Collocated co-kriging

EXERCISE Seismic velocity modeling – 3D grid based