M5 Facies Modeling

(1)

Module 5

Property Modeling

(2)

Petrel Workflow Tools

3D Grid 3D Grid Construction Construction Structural Gridding Structural Gridding Stratigraphic Stratigraphic Modeling Modeling Pillar Gridding Pillar Gridding Well Log Well Log Upscale Upscale Facies & Facies & Petrophysical Petrophysical Modeling Modeling

Make contacts &

Volume Calculation Volume Calculation I I n n t t r r o o t t o o P P e e t t r r e e l l I I n n t t e e r r f f a a c c e e

W W o o r r k k f f l l o o w w E E d d i i t t o o r r Property Modeling Property Modeling Make Horizons Make Horizons

Zones & Layering

3D Grid Construction: Structural Modeling

S S t t u u d d i i o o

3D Grid 3D Grid Construction Construction Structural Structural Framework Framework Fault Modeling Fault Modeling Introduction

Introduction Surfaces andSurfaces and

Data edit

(3)

Facies Modeling

Objectives



General Property Modeling Workflow



Discuss Different Facies Modeling Techniques

–

Deterministic techniques

–

Stochastic techniques.



Learn How to use Common Settings: Set filters



Learn How to use Zone Settings: Define zones



Learn How to use different Algorithms

–

Sequential Indicator Simulation

–

Object Modeling

–

Fluvial channel

–

General object modeling

(4)

Property Modeling General Workflow

Less data

More uncertainty

More data

Less uncertainty

Deterministic Addressed Pixel based Interpolation Estimation Object based Stochastic

(5)

Stochastic vs. Deterministic Modeling Methods

Stochastic

Deterministic

Random (Seed number) It is unlikely due to unpredictable factors. It generates different equiprobable results for

different seed numbers.

It generates the same result for a given set of initial conditions.

Variable states are described by probability distributions.

Variable states are described by unique values.

It does not need upscaled cells: Unconditional modeling.

Need upscaled cells; needs more data.

Allows more complexity and variability in the model; can help assess uncertainty.

(6)

Algorithms Covered in the Course

Stochastic methods

Deterministic method

Pixel based technique Object-based technique Direct addressing technique Sequential Indicator Simulation

algorithm

Object modeling algorithm Interactive modeling drawing

Distributes the property using the histogram. Directional settings, such as variogram and trends, also are honored.

Allows you to populate a discrete property model with different bodies of various geometries, facies types, rules, and fractions.

Allows you to paint facies directly on the 3D model.

(7)

Facies Modeling Dialog Box

Two main modeling settings buttons are available: (Common

and Zone settings).

Zone Settings

Defines settings for individual zones

(captured from Models pane >

Zone

filter

_folder).

Common Settings

Defines general settings for the grid

properties to be made for all zones.

(8)

Common Settings

Use filter: Should be chosen only if a filtered part of the

grid is to be modeled.

Ensure that all cells get a value: If there is no input

data, all cells will be populated by averaging

surrounding cells.

Overwrite: Will overwrite the previous realizations with

same suffix number.

Number of realizations: When running Uncertainty

analysis, multiple realizations are made with the same

input data.

Local model update: Updates the model inside a

region, inside a property, or around a well

(9)

Zone Settings

Zone: Click to activate zonation. Choose a

zone to model from drop-down list.

Facies: If conditioning to a previous facies

model, click the Facies button.

Method: Set the appropriate method from

the drop-down list for the zone to be

modeled.

Lock: Leave zone unchanged; unlock to

activate zone settings.

(10)

Create a Sequential Indicator Simulation

Property Model (1)

1. Set an upscaled property: (U) as suffix.

4. Choose the facies from the template. Click the Blue arrow to insert them into the model.

SIS is a pixel-based modeling

algorithm, using upscaled cells as the

basis for fraction of facies types to be

modeled. The variogram constrains

the distribution and connectedness of

each facies.

3. Set SIS as the Method for one zone. 2. Choose the zone to model and unlock it.

(11)

Create a Sequential Indicator Simulation

Property Model (2)

5. Variogram (2 methods):

• Specify Range, Nugget and Type manually. • Click Get a variogram from Data Analysis

6. Fraction (3 methods):

• Use Global fraction from Upscaled cells. • Use probabilities (property/trend).

• Use attribute probability curves or vertical

(12)

Variogram: Quantifies Spatial Continuity of the Data

Vertical Major

Variogram is calculated in 3 directions

Range Sill Nugget Separation distance (lag) 1 2 3 4 5

Variogram & parameters

There are many variogram types that can be fit into the

data. Petrel provides three options of prominent types:

exponential, spherical, and gaussian variograms.

•

You need three directions: Two in the horizontal

(major and minor ) and one in the vertical direction.

•

The range points the distance from which above, the

spatial dependence is set to randomness.

(13)

Create a Fluvial Channel Model (1): Facies Bodies

• Use fraction of Channels and Levees from upscaled cells. (Gray

field is not editable.)

• Enter a fraction. (The white field is editable.)

The Object modeling method uses upscaled cells as a basis for

the fraction of facies types to be modeled. The objects follow a

strict geometry, distribution, and trend defined by the user.

1. Set an upscaled property: (U) as suffix. 2. Set the zone to model and unlock it.

3. Set Object modeling as the Method to use.

4. Click the Fluvial channels icon to insert a channel body. 5. Choose facies properties to match Channel and Levee.

(14)

Create a Fluvial Channel Model (2): Geometry

Channel:

Specify the width and thickness of the channel.

Thickness can be in distance units or as a fraction of the width.

Levee:

Levees are the wing shaped deposits on the side of the channel. Specify width and thickness (smaller than channel).

Layout: Specify Orientation, Amplitude and Wavelength.

(15)

Create a Fluvial Channel Model (3): Trends and

Probabilities

Use volume probability:

• Use a function • Use a surface

• Use a 3D probability property (usually a seismic attribute).

Use Channel trends:

• Flow lines are digitized polygons used as fairways for the

channels to follow

• Source points are indications of paleoheighs/provenance;

(16)

Create a Fluvial Channel Model (4): Background

Background facies

•

After the channel is defined,

choose a background facies. This

is distributed wherever channels

are not placed.

•

Background can be undefined, a

single facies type, or a previously

generated property.

(17)

Create a General Object Model: Facies Bodies

The General object modeling approach creates standalone

objects following a strict geometry defined by the user.

1. Set an upscaled property: (U) as suffix.

2. Set the zone to model and unlock it.

3. Set Object modeling as the Method for the zone.

4. Click the Add a new geometric body button. (Ellipse geometry is chosen by default.)

5. Choose the facies type you want your body to have.

• Use fraction of upscaled cells.

(18)

Perform Interactive Modeling: (Draw Facies)

Interactive drawing of facies types that are not easily modeled.

Tip: Use Simbox view and make a copy of the property.

Note: Irreversible process: This overwrites all other facies, including upscaled cell values. No undo!

Radius

Height Brush type

Profile Facies type

(19)

EXERCISE

(20)

Extra: Object Modeling: Fluvial Channels Result

(21)

Facies Modeling Methods: Overview (1)

Deterministic Learning system Estimation Direct Addressing Artificial

Indicator Kriging Asign values Interactive Neural Net Discrete distribution of

the property honoring the predefined

histogram

Choose from

undefined, constant, other property, surface and vertical functions.

Allows you to paint facies directly on the 3D model.

Uses the classification model made in the Train Estimation model.

(22)

Facies Modeling Methods: Overview (2)

Deterministic Learning system Estimation Direct Addressing Artificial Indicator Kriging Asign values Interactive Neural Net

(23)

Facies Modeling Methods: Overview (3)

Stochastic

Pixel based Object based Sequential Indicator Simulation Truncated Gaussian Simulation Truncated Gaussian Simulation with trends Multi-point Facies Simulation Object Modeling Distributes the property using a histogram. Directional settings (e.g., variogram and extensional trends), also are honored.

Used mostly with carbonates where facies are known to be sequential. It deals with large amounts of input data, such as global fractions and trends.

Distributes the facies based on a transition between facies and trend direction. Trends are converted into probabilities to then run TGS.

The variogram is replaced by a training image giving both the facies and the relative position to each other, describing the spatial correlation from one-to-multiple points.

Allows to populate a discrete facies model with different bodies of various geometries, facies and fraction.

(24)

Facies Modeling Methods: Overview (4)

Stochastic

Pixel based Object based Sequential Indicator Simulation Truncated Gaussian Simulation Truncated Gaussian Simulation with trends Multi-point Facies Simulation Object Modeling