RockSI Guide

RockSI Tutorial

Tutorial outline

Introduction

RockSI software overview

Part 1: Log Analysis

Part 2: Rock Physics Template (RPT)

Part 3: Creation and calibration of user-

defined PEMs

Part 4: Uncertainty analysis using Monte Carlo

simulations

Part 5: LithoSI analysis using simulated PDFs

May 2015 2

Introduction

Rock Physics: the interdisciplinary glue

Structural

Geology

Petrology

Stratigraphy

Geology

Gravity

Electro

Magnetism

Seismic

Geophysics

Mineralogy

Coring

Logging

Rheology

Petrophysics

Reservoir

Modeling Flow

Simulation

History

Matching Reservoir

Engineering

May 2015 4

Interpretation and Quality Control:

Well logs

Inversion results

Predictions:

Deterministic:

Logs prediction, pseudo-well creation

Part of petrophysical inversion

Stochastic:

Augmentation of litho-classification training sets through

Monte Carlo simulations

Uncertainty analysis

Time-lapse feasibility studies

Trend modeling

Inverse Rock Physics Transforms

Rock Physics: Applications

May 2015 5

Note that there are a number of terms used in the creation

of rock physics models that are almost synonymous, but

have slightly different meanings.

These terms are:

the Rock Physics Model (RPM)

the Petro-Elastic Model (PEM)

the Rock Physics Template (RPT)

In the following slides we will define these three terms.

In the RockSI program, and this tuorial, we will mainly be

concerned with the creation of Petro-Elastic Models, or

PEMs, and their display are Rock Physics Templates (

RPTs).

Rock Physics: some definitions

May 2015 6

Rock Physics Model (RPM)

A Rock Physics Model, or RPM, is a model which describes how a

rock react to a given stimulus, e.g.:

Change of pressure (seismic waves)

Change of temperature

Electric/magnetic current

May 2015 7

Petro-Elastic Model (PEM)

A Petro-Elastic Model, or PEM, is a model which describes how a rock

reacts to seismic waves. The elastic response mainly depends on:

Rock composition (minerals and fluids)

Rock texture (spatial distribution of minerals and porosity)

May 2015 8

Petro-Elastic Model (PEM)

A PEM is comprised of a set of equations which model the elastic

response of a rock from its petrophysical properties.

The PEM is an important link between the well and seismic data.

PEM

Porosity Sw

Lithology

Vp

Vs

Density

WELLS

SEISMIC

May 2015 9

Rock Physics Template (RPT)

A Rock Physics Template, or RPT, is the projection of a Rock Physics

Model in a particular N-dimensional space:

Type of pores

Cracks Intergranular Vugs Change of P-Impedance

due to gas injection

(ΔSG = +0.6)

May 2015 10

RockSI Overview

The RockSI program has two main windows with tabs: on the left is

the input menu and on the right is the QC display.

RockSI: Overview

May 2015 12 Input QC and display

The RockSI program has two main windows with tabs: on the left is

the input menu and on the right is the QC display.

RockSI: Overview

May 2015 13

The Input tab allows the user to:

1. Select the input data:

- Wells (in Depth or TWT)

- SIModels

- Seismic (Angle Stacks)

- Wavelets

2. Select rock types and PEMs.

3. Create tables showing the

correspondence between PEM

variables and well logs or model

attributes.

4. Create tie point for each well in

depth (TVD) vs time (TWT).

RockSI: Overview

1

2

3

May 2015 14

4

The PEM Library tab allows the user:

1. A list of available Petro-Elastic Models:

- The ability to create new models

- The ability to load existing models

- Access to standard models

2. The definition of each PEM:

- Input variables

- Output variables with associated equations

3. Three editing modes:

- Read-Only to see the content of the

PEM

- Parameters editing to edit the value of

the internal parameters of the PEM

- Advanced editing to modify the

input/output and equations of the PEM

RockSI: Overview

1

3

2

May 2015 15

The variables tab gives:

1. A list of variables: any

variable with a valid name is

available for use in PEMs and

QCs

Default list

User-defined if more variables

are needed.

2. Variable display limits used in

the Log Viewers and Cross-

Plots tabs

3. Variable colormap used in the

Log Viewers and Cross-Plots

tabs

4. Variable unit used in the

Cross-Plots tab

RockSI: Overview

4 2 3 1

May 2015 16

Fluids tab:

Fluid property computation:

Density

Velocity

Bulk modulus

Based on Batzle & Wang equations

Brine, oil and gas properties as a function of pressure, temperature and fluid composition

RockSI: Overview

May 2015 17

Simulation tab:

1. Selection of PEM used in Monte

Carlo simulation

2. Definition of the input distributions and correlations

3. Simulated correlations

4. Export of simulated data as ASCII file or xsel file used for litho-classification (LithoSI)

RockSI: Overview 1

2

3

4

May 2015 18

Log Viewers Options tab:

Definition of the display template in the

Log Viewers tab:

1. Curve display options

2. Log table (selection of variables not the actual logs defined in the Input tab)

3. Angle Stack table (selection of keywords not the actual stacks defined in the Input tab)

RockSI: Overview

1

2

3

May 2015 19

Cross-Plots Options tab:

1. Definition of the display template in the

Cross-Plots tab

2. Graphical options for the display of RPTs from the PEMs defined in the PEM Library tab

3. Filters to apply on the cross-plots

4. Graphical options for the display of the data selected in the Input tab

5. Graphical options for the display of the predicted logs

6. Graphical options for the display of the data simulated in the Simulation tab

RockSI: Overview

1

2

3

5

4

6

May 2015 20

Log Viewers tab:

Logs and seismic traces

display for each well selected in the Input tab (template defined in Log Viewers Options tab)

RockSI: Overview

May 2015 21

Cross-Plots tab:

Cross-plot display (template

defined in Cross-Plots options tab)

RockSI: Overview

May 2015 22

Menus/Toolbars:

1. Load session

2. Save session

3. Create a new PEM

4. Load an existing PEM

5. Save a PEM

6. Update all the current QCs

RockSI: Overview

1 2 3 4 5 6

May 2015 23

Part 1

Log Analysis

QC and interpret log data through templates:

Rock type, fluid type

Petrophysical and elastic properties

Data interpretation and QC

May 2015 25


This first exercise will guide the user through the following tasks:

Identify the different lithologies encountered at the wells.

Identify the main petrophysical variables that impact the rock

elastic response.

Understand the impact of each of those variables.

Those tasks are not strictly talking part of the rock physics

modelling but are a preliminary work that will ease the modelling

task.

May 2015 26

Start the HRS10 Geoview program by clicking

Geoview icon on your desktop:

When you launch

Geoview, the first

window that you see

contains a list of any

projects previously

opened in Geoview.

Your list will be blank if

this is the first time you

are running Geoview.

27 May 2015


For this exercise, we will start with a

prepared project. Before doing that,

we will set all the data paths to point

to the location where we have stored

the workshop data. To do that, click

the Settings tab:

To change all of the directories to the same location, click on the option Set

all default directories and then click the button to the right:

Then, in the File Selection

Dialog, select the folder

which contains the

workshop (check with the

instructor the folder

location):

28 May 2015


After setting all three paths, the Geoview window will now show

the selected directories (note that yours may be different):

When you have

finished setting all

the paths, click

Apply to store

these paths:

29 May 2015


Go to the Projects tab. Click Find

Project:

30 May 2015

Select the project RockSI.prj.

Click OK to open the project:


Now the GEOVIEW

window looks like this:

In the project manager,

there are four pre-

loaded deviated wells.

31 May 2015


May 2015 32

First, look at the tabs to the left of the

GEOVIEW window. You will see that one

of those tabs is called Processes.

Click on that tab to see a list of all the

operations which are available in

Geoview. Each of the processes can be

expanded.

For example, if you click on the Petro-

Elastic Modeling (RockSI) option and the

RockSI option , the following expanded

list is seen:


May 2015 33

Double click on New RockSI Session:

A new tab called RockSI is created in the

GeoView window:

On the left of the RockSI

window are a series of

tabs for data control. On

the right side of the

window, there are two

tabs for displaying the

Log viewer and Crossplot

window. (Note that

depending on the project

itself, some data may

already be loaded, but we

will re-do the loading).


May 2015 34

Under the Input tab,

select the four available

wells by clicking Select

All Items button. This

will select all four wells

and create a QC display

automatically in the Log

Viewers tab for each of

the wells, as well as the

synthetic trace shown

in red at the far right

hand side of the plot.

In the seismic table,

add two new lines by

clicking the + sign

button twice.


May 2015 35

For this exercise, there are 3 angle

stack seismic volumes at 10, 20

and 30 degrees. Select each of the

seismic volumes under the column

Seismic by left-clicking on the

down arrow and selecting the files

Amp_10, Amp_20 and Amp_30.

Specify the Amplitude as Seismic.

Specify the Angle for each seismic

volume (10, 20, 30). Finally, select

the wavelets that have been

previously extracted from the

corresponding seismic volumes:

Wav_10_1, Wave_20_1, Wav_30_1.

Click the Update QCs button at the

top of the dialog. Also note that

three logs have already been

selected, which will be

automatically displayed in the Log

Viewers window.


Update QCs

May 2015 36

After selecting the well and

seismic data, the Log Viewers

window on the right side of the

RockSI window will look like

this:

As mentioned before, three logs

are now displayed. On the right

of the Log Viewers window, it

shows the extracted seismic

traces in black from each of the

seismic volumes. The red line is

the calculated synthetic trace

using the logs and wavelet.

Note that well-seismic ties were

performed prior to running

RockSI. Hence, the good match

between synthetic and extracted

seismic.

Synthetic

Extracted

seismic


May 2015 37

We will add a few variables for

QC purpose. You can do that by

clicking on the ‘+’ button a few

times to add the following

variables. Any extra row can be

removed by clicking the ‘-’

button. Proceed to add: Vsh, Phi,

Sw, Ip, VpVs, Vqz and Facies.


May 2015 38

Next we need to map the variables to

the actual log curves.

In each of the well tab, there are Input

Variable and QC Variable mapping

tables. Input Variable table by default

list P-wave(Vp), S-wave (Vs) and Bulk

Density (Rho) variables. This table will

automatically display the input

variables required by the Petro-Elastic

Model (PEM) selected in subsequent

steps. QC Variables are mainly used

in various QC plots.

Specify the variables and log curves

mapping as shown.

Click on the “Replicate selection to

other tabs” button to apply the

changes to all the well tabs.


May 2015 39

Click on Log Viewers

Options tab. Highlight the

first row and click on the

+ sign to add three rows.

Change the logs in these

new bottom three rows to

Vp, Vs and Rho.

Select Shale volume

(Vsh), porosity (Phi), and

water saturation (Sw) for

the top three logs in the

well log table. We also

want to color-code these

logs by selecting them

under the Color column.


May 2015 40

We can optimize the well log

display in the General Options

tab:

Update the Max value for the

porosity log type to 0.35.

We can also change the color

scheme by clicking on the color

Palette and select the desired

color scheme. Here, we will

keep the default selection.

Click on the Update QCs button

and see the new display in the

next slide.


May 2015 41

Three newly added logs are shown on the left of the Vp track.


May 2015 42

Click on Cross-Plots Options tab. The Cross-plot window will appear on the

right. Select the logs for the X and Y axis. Click the Update QCs button.


May 2015 43

Select Vsh to color code the

crossplot, then click on Replicate

current value to apply to the

other cross plot.

One option of the filter is to limit

the number of points which by

default is 100,000. The number of

points are randomly selected

from these 4 wells. We can also

select the wells of our interest

only.

Display legends turns on/off of

the color keys on the plots.


May 2015 44

The right of the RockSI

window shows the cross

plot of the selected wells.

The red points have a

higher volume of clay

(shale), while the blue

points have a lower

volume.

From the figure, we can

clearly see that the

porosity decreases with

increasing Vsh. The

shale lithology can be

easily identified by the

cluster of red points as

pointed by the arrow.


May 2015 45

Repeat previous steps to

display the effects of

volume of quartz (Vqz).

The sand is now

identified on the

opposite side from the

Vsh case, as shown by

the arrows


May 2015 46

Repeat for Phi (Porosity)

as the variable used to

color-code each of the

cross-plots.

Again, the high porosity

clusters roughly

correspond to the sand

as pointed by the arrows.

We can check additional

properties like: Sw(Water

Saturation), Vcalc

(Calcite Volume), Vcoal

(Coal Volume) … etc.


Next, we will either read in or create a

Facies log for each well to specify the

associated rock type for each sample.

First we will define a series of Facies by

clicking on Define Facies Classification

from the process list in the project

manager.

On the dialogue that appears, click

on the Load button:

May 2015 47

Part 1: Facies Logs

Then, in the file chooser that appears, locate the file Facies.xml and select

it and click Open. If you can’t find a facies xml file, skip the next slide.

May 2015 48

Part 1: Facies Logs

When the facies xml file is loaded, it will look like this, with Coal,

Shale, Sand, Oil and Calcite facies:

May 2015 49

Part 1: Facies Logs

Then, click OK.

May 2015 50

Next we want to find out if the four

wells contain facies logs. To do this,

select Project Data / Well / well A and

see if one of the logs is called

FACIES, as shown on the right.

Check wells B, C, and D also.

Part 1: Facies Logs

May 2015 51

Go to the RockSI window and

click on the Log Viewers Options

tab.

Click on the first line in the log

viewer table, and click the + sign

to create a new row. Select

Facies to display and the Facies

as color-coding variable.

Part 1: Facies Logs

Click here

before the

+ sign

May 2015 52

Go to the General Options

tab.

Click on the + sign to add a

row at the bottom of the

table, select Facies for the

Property Type. Specify 0-7

for min-max range. Click

the color palette and select

Facies.

Part 1: Facies Logs

May 2015 53

Now the Facies log is

displayed on the first track

of the log viewer window.

From the figure, we can see

clearly that the Facies log

is closely related to the

other color coded logs

(Vsh, Phi, Sw and Vp).

At depth of 1930 m, it is the

Oil sand and Shale

interface. By tracking the

curser position, we can see

a strong reflection at this

interface.

We can also check the

other wells by clicking

those well tabs.

Part 1: Facies Logs

May 2015 54

Click on Cross-Plot Options tab.

Select Facies as the variable

used to color-code each of the

cross-plots.

The cross plot windows now look

like this. The color key of the Facies

displayed is the color of the five

facies we have just defined.

Part 1: Facies Logs

Part 2

Rock Physics Template (RPT)

Different types of effective medium models

Empirical models:

Gardner

Greenberg-Castagna

Effective Medium models:

Granular models:

Pack of spheres with interstitial porosity

Used to model high porosity rocks such as

unconsolidated and lightly cemented sandstones

Inclusion models:

Background and inclusions (spheres, needles,

penny cracks):

Self-consistent model (SC)

Differential effective medium (DEM)

Used to model low permeability rocks such as

tight sandstones and carbonates

May 2015 56

Soft (unconsolidated) sandstone model

Pack of identical smooth spheres

Porosity filled by a single phase

Model

Effective grain

Effective fluid

Simple texture

Real rock

Multiple mineral types

Multiple fluids

Complex texture May 2015 57

Soft (unconsolidated) sand model

Effective matrix: mix of Quartz and Clay

Effective fluid:

quartz

clay

clay

clay

quartzclayclayclaymatrix

M

V

M

VMVMVM

12

11

2

1

GGOOWWfluid ρSρSρSρ

G

G

O

O

W

Wfluid

K

S

K

S

K

SK

1

quartzclayclayclaymatrix ρVρVρ 1

Hill

average

(Wood equation)

May 2015 58


Effective frame:

HMHM

HMHMHM

HMHM

HMHMHMmatrix

C

HMHM

HMHMHMHM

C

HM

HMmatrix

C

HMHM

Cdry

eff

matrix

matrixC

matrix

matrixHM

eff

matrix

matrixCHM

GK

GKG

GK

GKGG

φφ

GK

GKGG

φφG

G

GK

φφ

GK

φφK

PPRπ

GφC

PR

PRG

PPRπ

GφCK

2

89

6

2

89

6

1

2

89

6

3

4

3

4

1

3

4

1

12

13

25

45

118

1

1

3

1

22

222

3

1

22

222

(Hertz-Mindlin model)

May 2015 59


Combination of frame and fluid:

Elastic attributes:

(Gassmann equation)

2

2

1

1

matrix

dry

matrixfluid

matrix

dry

dryB

K

K

KK

K

K

KK

GV

GK

V

S

B

P

matrixfluid

3

4

1

May 2015 60

Unconsolidated sand model

A

A B C D

D

C

B

Modified

Voigt

Unconsolidated

Sand PHI

SG

May 2015 61

May 2015 62

In the previous exercise, we used volume logs to interpret the

different lithologies. When the volume logs are not available, we can

use the standard Petro Elastic Models (PEMs). You can also create a

new PEM with your own equations.

In this next exercise, we will learn how to display Unconsolidated

Sand RPT using one of the predefined PEMs. Also see how we can

display these RPT on standard GEOVIEW crossplots.

Go to the PEM Library tab.

Right click on the PEMs item

and select the Load PEMs.


May 2015 63

A number of published models are

included in RockSI. Click different

tabs to review them.

Select the Unconsolidated Oil

Sandstone model

To see the full documentation of this

model, click the Information Icon.


May 2015 64

This brings up the

documentation for the

Unconsolidated Oil

Sandstone model as

shown on the right.


May 2015 65

Click Apply to load the Unconsolidated

Oil Sandstone PEM, then close the

dialog.


May 2015 66

After loading the sandstone

PEM, we can see a list of

reservoir conditions, mineral

and fluid properties.

Click the Advanced toggle to

change to full editor mode.


May 2015 67

In Advanced editing mode,

you can modify the

relationships, parameters or

define new variables as

needed.

We will not make any

changes to the PEM in this

exercise.


May 2015 68

Before display the

unconsolidated sandstone

RPT, it will be useful to limit

the crossplot QCs to show

only sandstone facies. It

requires the FACIES log

curve and Classification table

to be specified.

In the Input tab, select the

option Several rock types

and the Facies table imported

in previous exercise as the

rock classification.

Scroll down to well tabs

parameters section.


May 2015 69


Specify the FACIES log curve

for Well A.

Then, click the Replicate Icon

to select the same log curve

for the other three wells.

May 2015 70

In the Cross-Plots Options tab, modify the first crossplot to show Vp vs

Rho. Toggle on Display only rock type and select Sand. Then click the

Update QCs icon. The plots now show data points for only the Water Sand.


May 2015 71

To display both Water Sand and Oil Sand facies, we will use the Display only

range option. We need to specify the Facies classification and the range 2.5 to

4.5. Also, the Display only Rock Type option must be unchecked.


Range: 2.5-4.5

May 2015 72

We are now ready to display

the RPT. Go to the Cross-

Plots Options tab

Select PEM Unconsolidated

Oil Sandstone, Main

graduation: Phi and check

Secondary Graduation and

select Vsh.

Fill in the PEM parameters as

shown in the table.

With Sw= 0, we are modelling

100% Oil saturation.


May 2015 73

The soft sandstone

template is as shown

in the crossplot

window. Meshes are

displayed in the plots

that represent the Oil

sandstones with Phi

between 0.1 and 0.35

and Vsh between 0

and 0.4.


May 2015 74

Set Sw to 1 to model the

water sand.


May 2015 75

Observe that the

meshes now match

the water sand.

You can model any

combination of two

parameters in the

table with ranges.


May 2015 76

The color of the RPT can be

changes by clicking on the

color selector icon.

You can control how often the

increments of the mesh without

recalculating the RPT. Click the

Palette Icon to bring up the

Viewer options.

Click Close.


May 2015 77

Click Save current session to

save your work in RockSI.

Next we will use the

GEOVIEW standard crossplot

tool.



May 2015 78

The same RockSI PEM/RPT can also be displayed in the standard

Hampson-Russell Crossplot tool. It requires a license of RockSI

to enable the option.

We will start by creating a

standard VpVs vs P-impedance

crossplot in GEOVIEW.

- Go to the Processes tab.

- Expand the Cross Plotting folder

- Double click Cross Plot Logs

May 2015 79

In this example, we will only use

Well A to demonstrate the steps.

Make the following selections:

-Single Well

- well_A

- Vp/Vs vs P-impedance

We will use all data.

Click OK.


May 2015 80

A crossplot similar to the

one on the right will appear.

On the menu bar, click

Options and select “RPM

and PEM…”

Note that this require a

RockSI license.


May 2015 81

The PEM parameters

dialog will appear as

shown on the right.

See the picture for

various ways to access

PEM.


May 2015 82

The Unconsolidated Oil

sandstone PEM should

readily available for use

since we previously

loaded it in RockSI. Any

PEM created in RockSI

can be used in this

Crossplot tool.

Select Unconsolidated Oil

Sandstone.


May 2015 83

Fill in the PEM parameters as

shown in the table.

With Sw= 0, we are modelling

100% Oil saturation.


May 2015 84

RPT of

Unconsolidated Oil

Sandstone is now

displayed in the

crossplot.

Any dynamically

created models in

RockSI can be

posted on

standard

GEOVIEW

crossplots to guide

seismic and well

logs interpretation.


Click the Cross Plots icon in

the lower right hand corner

to dock the window.

Part 3

Creation and calibration of user-defined PEMs

Part 3: Creation of Petro Elastic Models

In this exercise, you will create or import a Petro Elastic Model (PEM)

for each of the previously determined lithologies.

The quality of the modelling will be tested by comparing the model

predictions with the actual logs both in 1D viewers and cross-plots.

May 2015 86

Part 3: Geological context

Lithostatic Pressure (MPa)

Pore Pressure (MPa)

Effective Pressure (MPa)

Temperature (°C) 0 10 20 30 40 50 0 10 20 30 40 50

0

500

1000

1500

2000

2500

Depth (m) Depth (m)

45

9.8

49

4

Sea Water

ρ = 1g/cc

Overburden

ρ = 2.25 g/cc

Reservoir

ρ = 2.5 g/cc

26.4 27

38

Gradient = 30°C/km

35.5

12

10.9

11.4

33

15.5

24.1

May 2015 87

Part 3: Geological context

Temperature: 35 °C

Lithostatic Pressure: 30MPa

Pore Pressure: 20 MPa

Brine Salinity: 0.01 kg/L

Gas-Water ratio: 0 L/L

Oil gravity: 30 API

Gas-Oil ratio: 50 L/L

Gas gravity: 0.6

May 2015 88

Fluid Parameters:

May 2015 89

In this exercise, we will:

1. (Optional) Create a user defined PEM to model Shale. Instead of

building a full PEM, we will only build it up to modelling bulk density.

That should be enough to demonstrate how it can be done.

2. Then proceed with importing a PEM for each lithofacies and use these

PEMS to predict elastic properties: Vp, Vs and Rho. The predicted

curves can be visually compared with the measured curves.

Part 3: Creation of PEMs

May 2015 90

Click the PEM Library tab.


and select Delete all PEMs.

Click Yes to delete the PEMs.

Toggle the Advanced button.


and select Create New PEM.


May 2015 91

Right click on the newly

created PEM and select

Rename PEM.

Name this PEM as Shale and

click Ok.


May 2015 92

Go to the Cross-Plots Options

tab, toggle off Display only

Range and toggle on Display

only rock type with shale

selected.

Set Facies as the color-coded

variable for the crossplots if

it is not already set.


May 2015 93

We have seen in Exercise 1 that

the main variables affecting the

elastic response of the shale are

Vsh, Phit (total porosity) and the

effective pressure Peff .

Click on PEM Library tab, add

three new lines in the input table

of the Shale tab by clicking on

the Add Line Below button.

Select Vsh, Phit, Depth (True

Vertical Depth) and Pp (pore

pressure) respectively. Pay

attention to ensure that proper

units are specified.


May 2015 94

Select Peff as the Output

Variable, and MPa as the unit.

peff P/*(Depth.*.P )1000)10002521(819

In many cases, Peff is derived

from Depth when Pp is known.

Effective pressure in our case is

given by the following equation:

Click the Arrow sign to see a list

of Input parameters, Math

operators and precompiled

equations, which we will use for

writing the equations.

Input parameters

Math operators

Precompiled equations


May 2015 95

Type in part of the

equation as shown on

the right.

Next we will select the

variable Depth. Click the

arrow sign beside the

equation box and select

Depth.

Type in the rest of the

equation and select the

variable Pp.


May 2015 96

The final equation to

calculate Peff looks like this:

Add eight new lines by clicking

on the Add Line Below button.

Select the output variables as

shown (notice that the new

Variables are Temperature,

Salinity, Gas/Water Ratio, and

five different densities:


If you have entered the equation

correctly, a green check mark will

appear after the PEM name.

A red cross will now appear after

the PEM name, indicating that the

equations must be filled in:

We will start with the modelling of the shale density as this is the easiest elastic attribute to model.

The density is given by the following volumetric averages:

We will use 2.65 for the quartz density and the shale mineral density is given as a function of the

effective pressure:

The water density can be calculated using Batzle & Wang equation as a function of:

Temperature [°C]

We will assume a temperature gradient as shown earlier:

Pore Pressure [MPa]

Salinity [kg/L]

Gas-Water ratio [L/L]

*RhoqzVshVsh*RhoshmaRho

ma*RhoPhitPhit*RhowRho

1

1

1000)1000(304 /Depth*T

*Peff..Rhosh 020432

May 2015 97


Part 3: Lithofacies PEMs

Next, we will create PEMs for each of the lithofacies defined earlier.

For example, the shale PEM is created using the following equations:

98

.020432

and

1

:

1

effsh

qzshshshm

mw

*P..

VV

where

May 2015

May 2015 99

The Batzle & Wang equation

for brine density is quite

complex and it has already

been precompiled.

Click the arrow sign beside the

equation box for the fifth row

(Rhow), and select

Precompiled Equations->Brine

density (Batzle Wang).

On the menu, we can also see a

list of other pre-defined

equations.


May 2015 100

Enter the equations

for the rest of the

output variables as

shown.

Make sure the units

of the input

variables and

output variables are

correct.

If you fill in the

equations correctly,

the green check

mark will appear

again.

See next slide for a

description of the

variables involved.


May 2015 101

Vsh – Volume of Shale

Phit – Total Porosity

Depth – True Vertical Depth

Pp – Pore Pressure

Peff – Effective Pressure

T – Temperature

Sal – Salinity

GWR – Gas Water Ratio

Rhow – Brine Density

Rhoqz – Quartz Density

Rhosh – Shale Density

Rhom – Rock Matrix Density

Rho – Bulk Density


May 2015 102

Click the Input tab, and select

Shale for the Petro-Elastic

Model for the Rock Type shale.

Specify the mapping of input

variables to their corresponding

log curves: VCL (clay volume),

PHIT (total porosity), TVDSS (true

vertical depth), and PP (pore

pressure).

Click Replicate Icon to select the

same logs for other wells.

Click Update PEM predictions

Icon to calculate the predicted

logs


You can see the

predicted density in

the Log Viewer of

each well.

The predicted

density (red) for

Shale facies (Brown

color facies) is now

overlaid on top of

the measured

density (black).

The match of the two

logs are quite good.

May 2015 103


You can see the

predicted density

overlaid with

measured values in

the crossplot QCs as

well.

The predicted

density (red) for

Shale facies and

measured density in

brown (colored by

facies). You can

uncheck the

Predictions to see

measured density.

May 2015 104


Now that the density is correctly modeled, we will need the bulk modulus and shear modulus of the

rock to calculate the P-wave and S-wave velocities.

The modulus of the mineral can be computed with a simple Hill volumetric average:

We will use 37 and 45 for the quartz bulk and shear modulus respectively.

We will use 25 and 9 for the shale bulk and shear modulus respectively.

The modulus of the saturated rock can be computed as a function of the mineral modulus, the

effective pressure and the total porosity:

-Vsh)/Gqz) (/(Vsh/Gsh . *Gqz) Vsh*(Vsh*Gsh.Gma

-Vsh)/Kqz) (/(Vsh/Ksh . *Kqz) Vsh*(Vsh*Ksh.Kma

150150

150150

82108130

109036120

*Phit/Peff*Peff.*Kma.Keff

*Phit/Peff*Peff.*Gma.G

May 2015 105


Now that we have modelled the bulk, shear modulus and density of the shale formation, we have all

the input needed to compute its P-wave and S-wave velocities:

Because those equations are used very often, they have been precompiled in the software and can be

used through the following keywords:

VP_WAVE_PROPAGATION_EQ

VS_WAVE_PROPAGATION_EQ.

You can continue to enter equations and variables to build a full PEM to predict Vp and Vs for shale.

Now that you have some experience on building a PEM, we will stop and simply import a number of

prebuilt PEMs instead.

Rho

GKbVP

3

4

Rho

GVS

May 2015 106


May 2015 107

Click the PEM Library tab.


and select Delete all PEMs.

Click Yes to delete the PEMs.

Toggle on the Advanced

button. Right click on the PEMs

item and select Load PEM(s).


May 2015 108

On the Load PEM(s) dialog,

click the Folder Icon.


May 2015 109

Go to the same directory

where the RockSI project

resides. You should find five

files with “.pem” extension.

Select Calcite, Coal, Sand

(Water sand), Oil (Oil sand)

and Shale. Note that water

and oil sand use the same

model. We separated them so

that it will be easier for

comparing simulation results

later.

Click Open to select them.


On the Load PEM(s) dialog,

Click Apply to import them.

May 2015 110

The PEM Library tab should

now show four PEMs available

for use. The Shale PEM is

named Shale #1 since we had

one defined previously.

You can review the equations

and parameters defined in each

PEM.


May 2015 111

With all the necessary PEMs

available, we are ready to use

them for predicting Vp, Vs and

Rho for all facies.

Go to the Input tab.

Specify the associated PEM for

each Rock Type.

Make sure the Output Variable

table has Vp, Vs, Rho, Ip and

Vpvs selected. Use the + sign

to add new variables.

Scroll down to the Well Tabs.


May 2015 112

For Well A, specify the log

curves as shown for each

table.

Make sure you click the

Replicate Icon for each table to

ensure the selections are

applied to the other three wells.

Click through all the wells to

confirm the selections.

Lastly, click on Update PEM

predictions Icon to generate

the log curves.



May 2015 113

In the Log Viewers tab,

you should see the

predicted Vp, Vs and

Rho (red) overlay with

their associated

measured curves (black).

The angle synthetics are

also generated based on

the predicted curves.

You may need to use the

horizontal scroll bar to

scroll to the right in

order to see the results.


May 2015 114

Click on the Cross-Plot Options tab. Uncheck Display only rock type

filter. Go to the Cross-plot tabs to see the crossplot QCs. Predicted

values are in red and measured values are colored by facies.

May 2015 115

Click the Save session button

to save the current parameters

and QC templates.

This concludes this exercise.


Part 4

Uncertainty analysis using Monte Carlo simulations


Porosity

Overburden Pressure

Pore Pressure

Clay Volume

f

Rock Model

Parameters

Sw, Sh, Sg

PEM )inputs(fVp

May 2015 117

Input Uncertainties

Output Uncertainty


)inputs(fVp

Input PDFs

Output PDF

Porosity

Overburden Pressure

Pore Pressure

Clay Volume

f

Rock Model

Parameters

Sw, Sh, Sg

PEM

May 2015 118

Input Distributions

Constant

Uniform: Min and Max

Normal: Mean and standard deviation

Beta (PERT): Min, Mode, Max, Lambda (see

Annex C for charts)

Discrete: Min, Max, Step

May 2015 119

Beta (PERT) distribution

Parameterization suited to rock physics properties:

Min/Max

Most likely = mode

Shape factor reciprocal of “standard deviation” at mode

mode

lambda

=1

1.5

2

2.5

3

5

7

9

May 2015

120

Xm

Ym

X

Y

XP

YP

YXP ,

XYm |

XY |

XYP |

X

Y

Input Correlations

Correlations between the input variables are simulated through the Iman-

Conover technique (see Annex C for more details).

CC = -0.7

Xm

Ym

X

Y

XP

YP

YXP ,

XYm |

XY |

XYP |

May 2015 121

Production effect on elastic attributes:

Simulation of scenarios

pI

sI

shale

oil-sand

water-sand

water flooding

gas sand

gas out of solution

due to depletion

May 2015 122

Part 4: Monte Carlo simulations

In this exercise the user will run Monte Carlo simulations using the PEMs

defined in the previous exercise.

The objective is to make sure that the PEMs reproduce the variability

observed in the log data.

May 2015 123

We will start the simulation of

the shale. Set the following

cross-plots parameters in the

Cross-Plots Options tab.

Filter the data to display shale

only.

Turn off the display of

Predictions result.

May 2015 124


Select the Shale PEM in the

simulation tab. Enter the input

distributions and correlations

as shown. Total porosity (Phit)

is negatively correlated to

volume of Shale (Vsh).

Launch the simulation by

clicking Launch Simulation

button. Compare the simulated

points with the actual logs.

May 2015 125


The Shale simulation result looks like this:

May 2015 126


Measured Data

Simulated Data

With P10, P50, and P90 PDFs

contours

After the simulation, we can select

to output a list of petro-elastic

attributes as LithoSI training set.

LithoSI is a Bayesian

Classification software.

Remove all output and add two

new output attributes.

Select attribute Ip and VpVs.

For the training set name, it has to

be one of the facies we have

defined; Enter Shale.

Name this training set as

Training_Set and click Save.

May 2015 127


Now the simulation has been

saved. We can view the

simulation results in the saved

file. Right click on Training_Set

and select View saved session:

May 2015 128

In the training set file, we can

see that we have three

columns including the two

output volumes Ip and VpVs

and the class of the facies

Shale.


In the Cross-Plots Options tab,

Select Phi instead of Phit in the

cross-plots. Set the rock type

filter to display the water sand

only.

May 2015 129

Check off Shale simulation

results.


Select the PEM Sandstone in the

Simulation tab. Enter the input

distributions and input

correlations as shown:

Launch the Water Sand simulation

by clicking Launch Simulation

button.

May 2015 130


The Water Sand simulation result looks like this. Compare

the simulated points with the actual logs

May 2015 131


Measured Data

Simulated Water Sand Data


contours

Save the simulation result for

Water Sand.

For the training set name, it has to

be one of the facies we have

defined; Enter Sand.

Name this training set as


May 2015 132


Next, we will simulate the Oil Sand.

Modify the input distributions as

shown in the table. The only

difference here is that we are

varying Sw with a Beta distribution

between 10%-70%. (i.e. So ranges

30%-90%)

Launch the simulation by clicking

Launch Simulation button. Compare

the simulated points with the actual

logs.

Set the range filter to display only

the oil sand.

May 2015 133


The Oil Sand simulation result looks like this. Compare the

simulated points with the actual logs

May 2015 134


Measured Data

Simulated Oil Sand Data


contours

Save the simulation result for Oil

Sand.

Change the Training set name to

Oil.

Select the training set

Training_Set and click Save:

May 2015 135

On the pop-up message, click Ok.


We can also simulate what would

happen if the reservoir was filled by

gas instead of oil. Modify the input

distributions for Sw and Sg as

shown in the table:

Launch the simulation by clicking

Launch Simulation button. Compare

the simulated points with the actual

logs.

Set the range filter to display only

the water and oil reservoirs.

Compare with the simulated gas

reservoir.

Click to redraw.

May 2015 136


May 2015 137

The Gas Sand simulation result looks like this. We will not save this result

to Training_Set. This is just for comparison purpose.


Reset simulation back to the Oil

case by modifying Sw and Sg as

shown on the right.

Click on Launch Simulation again.

There is no need to save this result

since we did it previously.

May 2015 138


May 2015 139

Select the PEM Calcite in the

simulation tab. Enter the input

distributions and input correlations as

shown. Launch the simulation by

clicking Launch Simulation button.


Calcite.




The Calcite simulation result looks like this. Compare the


May 2015 140


Measured Data

Simulated Calcite Data


contours

May 2015 141

Select the PEM Coal in the simulation

tab. Enter the input distributions and

input correlations as shown. Launch

the simulation by clicking Launch

Simulation button.

After the simulation, Select the

output attribute Ip and VpVs:


Coal.


Training_Set and click Save:


The Coal simulation result looks like this. Compare the


May 2015 142


Measured Data

Simulated Coal Data


contours

May 2015 143

To see all five simulated facies

data. Go to Cross-Plot Options

tab.

Reduce to number of crossplots

to only showing Ip vs VpVs

Set the display parameters as

shown on the right:

- Uncheck any filters

- Uncheck Data and Predictions

- Uncheck outline for each

Simulation

- Uncheck pdf for each

Simulation

- Specify corresponding color for

each facies


May 2015 144

This the simulated data for the five facies. In the next excerise we will use

these simulated data points as training data set for LithoSI (Bayesian

Classification).


May 2015 145

Click the Save session button

to save the current parameters

and QC templates.

This concludes Part 4 exercise.


Part 5

LithoSI Analysis using Simulated PDFs

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Ip

Vp/Vs

Compute

multivariate PDFs

Litho-classification

1200

1250

1300

1350

1400

1450

1500

1550

1600

1.8 2.6 2.2 1600 2000 2400

Seismic inversion results

Extract

training sets

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Ip

Vp/Vs

Gas

Water

Oil

)/, Class( spp VVIiP

Ip

Vp/Vs

Well logs

Compute probability litho-cubes

0.0

1.0

May 2015 147


Too sparse and not representative

of natural reservoir variability

May not include all “litho-classes”

May not capture expected depth

trends

Training Set

Issues 1200

1250

1300

1350

1400

1450

1500

1550

1600

1.8 2.6 2.2 1600 2000 2400

Extract

training sets

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Ip

Vp/Vs

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5


Well logs

0.0

1.0

Gas

Water

Oil


Ip

Vp/Vs

Compute

multivariate PDFs

Gas sand

Cemented

sand?

May 2015 148

Compute

multivariate PDFs

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5


PEM

Monte Carlo

Simulation Sw

F

Peff Augmented training set

for existing class

Additional simulated

litho-class

Vclay Simulate

training sets

Vp/Vs

1600 1700 1800 1900 2000 2100 2200 2300 2400

1600 1700 1800 1900 2000 2100 2200 2300 2400

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5


0.0

1.0

Gas

Water

Oil


Ip

Vp/Vs

May 2015 149

Part 5: LithoSI analysis

In this exercise, we will use the previously defined PEMs to interpret an

elastic inversion result.

We will also perform a litho-classification of that inverted model using both

the well data and the synthetic training sets created in the previous

exercise.

May 2015 150

151 May 2015

To initiate the LithoSI program, go back to

the Processes tab and under Facies

Classification (LithoSI) , double-click <New

LithoSI Session>.


152 May 2015


The dialog which appears is

multi-tab, which will help you

set up the LithoSI process. The

first tab of the dialog defines

which seismic volumes will be

used in the process. These

seismic volumes are collected

in a “Super Volume”. In this

example we select both the Zp

and VpVs volumes as shown on

the left (either click once on

each volume and click Select or

double-click each volume).

153 May 2015

When you have changed the

first page as shown above,

click the Wells tab to see the

next page:


This page specifies which

wells to use in the

analysis. We want to use

three wells (well_B, well_C

and well_D), so Select

these wells:

154 May 2015

Then, click the Facies

Classification tab:

The Facies Classification

page is used to define the set

of facies classes in our

training data. First we select

the lithology log, which was

created in each of the wells

in the previous section. This

was called FACIES.


In the rest of the page, we define the classes, telling the program how many

classes and what their names are. We will select the classes we just defined

in the previous section. Click on Facies:

155 May 2015

Now click the last tab, Seismic

Attributes-Well Log Mapping:

On this page, we specify how

many seismic attributes are

used, and how the seismic

volumes are related to the

well log curves. The default

number of attributes is 2, and

we will accept that:

The mapping of volumes is as

on the right. We associate the

seismic inversion volume,

Inverted_Ip, with the curve IP.

Similarly, we associate the

seismic inversion volume

Inverted_VpVs with the well log

curve VpVs.


156 May 2015

When you have

filled in the dialog

as shown above,

click OK to create

the LithoSI window:


157 May 2015

The LithoSI window contains a series

of options for creating the probability

distributions associated with each

class. The first thing we can do is

create more space on the computer

screen by temporarily removing the

Project Manager. Click the “x” as

shown:

Note that we can restore the Project

Manager any time later by clicking the

rotated name Project Manager on the

left side:


May 2015 158

Go to the Data Selection tab

We want to use the simulated data

created in RockSI so select

“Load training-set from RockSI”.

Select RockSI Session and the

training set. Note that the session

name will be different in your case

since by default the name is

created with date and time.

Specify the mapping of the

properties used in RockSI with the

log curves.

The a-posterior proportions on the

rightmost column in the bottom

table are calculated from all the

wells based on facies curves.


May 2015 159

To display the external training set

from RockSI in a crossplot.

Turn off Well logs data points.

Turn on External Training-set.


May 2015 160

LithoSI Kernel Analysis now

shows the data points

simulated in RockSI.

Next step is to generate the

pdfs for each of these facies

and fine-tune by adjusting the

smoother parameter.


May 2015 161

Reduce the smoother length to 2.

Click on Compute in Common Parameters panel.

In Kernel Analysis tab, turn off Coal distribution.

The pdfs between these facies overlap quite a bit. Reducing the smoother

length also reduces these overlaps.


May 2015 162

Use the slider to adjust the smoother to 1.

Click Compute button again. The pdfs now fit the facies data points tighter.


May 2015 163

Click on QC at Wells. Select only Well Logs toggle.

The Confusion Matrix gives an overall success and misclassification rates

of the facies at all three well locations.


May 2015 164

Next we QC with an automatically generated arbitrary line that passes

through the three wells. Go to QC on Sections.

Select “Passing through wells” and click on the Select all wells icon.


May 2015 165

You can also QC constant

time map of the

classification by going to the

QC on Maps tab.

Finally, you can generate the

classification results for the

entire volume by running it

in “Run on 3D volume” tab.

It is not necessary to run the

process for this exercise.


May 2015 166

This final exercise demonstrate how RockSI simulated data points

can be used in the Bayesian Classification process (LithoSI).

This concludes our RockSI tutorial.


RockSI Guide

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Transcript of RockSI Guide