Reservoir Property Mapping - read.pudn.comread.pudn.com/downloads130/doc/556189/train/train/GF4_LPM_man… · GeoFrame 4 Reservoir Property Mapping Schlumberger About this Course

GeoFrame 4

Log Property Mapping

Training Guide

Training & Development

March 22, 2002

GF4_LPM_22Mar02.pdf

GeoFrame 4 Reservoir Property Mapping Schlumberger

Copyright Notice

© 2002 Schlumberger. All rights reserved.

No part of this manual may be reproduced, stored in a retrieval system, or translated in any form or by any means, electronic or mechanical, including photocopying and recording, without the prior written permission of GeoQuest, 5599 San Felipe, Suite 1700, Houston, TX 77056-2722.

Disclaimer

Use of this product is governed by the License Agreement. Schlumberger makes no warranties, express, implied, or statutory, with respect to the product described herein and disclaims without limitation any warranties of merchantability or fitness for a particular purpose. Schlumberger reserves the right to revise the information in this manual at any time without notice.

Trademark Information

Software application names used in this publication are trademarks of Schlumberger. Certain other products and product names are trademarks or registered trademarks of their respective companies or organizations.


Table of Contents

Copyright Notice......................................................................................................................................1

Disclaimer ................................................................................................................................................1

Trademark Information............................................................................................................................1

Table of Contents .......................................................................................................................................1

About this Course .......................................................................................................................................2

Lesson 1 — LPM Main Selection...............................................................................................................3

EXERCISE: .............................................................................................................................................3

Lesson 2 — LPM Data Analysis ................................................................................................................5

EXERCISE: .............................................................................................................................................5

Lesson 3 — LPM Data Population – Guided Mapping .............................................................................9

EXERCISE: .............................................................................................................................................9

Lesson 4 — LPM Data Population – Geostatistical Mapping.................................................................13

EXERCISE 1: ....................................................................................................................................... 13

EXERCISE 2 ........................................................................................................................................ 17

Lesson 5 — LPM Data Population – CoKriging ......................................................................................20

EXERCISE: .......................................................................................................................................... 20

Lesson 6 — Analyze Data Anisotropy Using Variomap..........................................................................25

EXERCISE: .......................................................................................................................................... 25

Lesson 7 — Use LPM for Time-Depth Conversion.................................................................................29

EXERCISE: .......................................................................................................................................... 29

Lesson 8 — How to Use 3D Crossplot Tool............................................................................................35

EXERCISE: .......................................................................................................................................... 35

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About this Course

This one-day course is designed to introduce GeoFrame 4 Log Property Mapping (LPM) application functionalities and practical usage of the application through a sequence of hands-on exercises.

The targeted audiences for this course are geologists, geophysicists, and reservoir engineers, who intend to use LPM as an integral part of their workflow, to improve the quality of reservoir property maps by integrating borehole geology and seismic geophysics.

Major lessons in this course include:

Set LPM Main Selection for mapping environment and data sources.

LPM Data Analysis to calculate the correlation between reservoir properties and seismic attributes, and calibrate their relationships quantitatively.

Populate reservoir properties using Guided Mapping to cooperate borehole data and seismic attributes.

Populate reservoir properties using geostatistical algorithms from CoKriging, and Geostatistical Mapping.

Use LPM to convert time horizon grids to depth.

After finishing this course, you will learn the basic operation skills and the background knowledge of LPM.

Some results need to be made available before we can run LPM. The input data to LPM come from

ResSum: Summations of reservoir properties calculated from property log curves within a reservoir interval defined by Layers and LithoZones;

IESX/Charisma: Seismic attribute grids generated from either IESX or Charisma;

Scatter Set of any source can be used in Geostatistical Mapping.

The exercises in this course are built on the Gullfaks training project. Recommended item for each selection is listed in a bracket in this exercise guide, for example, [Depth_RANNOCH]. Use the recommended selection to maintain the flow of the exercises. Users should have little difficulty to apply the same procedures to other projects.

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Lesson 1 — LPM Main Selection

The most common use for LPM is to generate reservoir interval property grids and property maps by combining borehole geology data and seismic attributes. It becomes especially useful in places that lack of good well control. In places with only a few wells, borehole data along will not yield a very reliable property map. On the other hand, some reservoir properties are closely associated with certain seismic attributes. For example, it is known that amplitudes and frequencies reflect the density of the rocks, and the density of the rocks is affected by rock porosity and the content in the pores of the rock. Gas, oil, or water will result in different density; therefore show differences in seismic attributes.

LPM Main Select includes:

Surface: the Container to which seismic interpretations and seismic attribute grids belong. In addition, the grids generated from LPM will also belong to the Surface container.

Zone Version: contains the Markers, Litho Zones, and Layers created in WellPix.

Layer: the target layer.

Master Grid: a grid whose underlying grid lattice will be used as the grid geometry for the LPM output grids. You may create a new lattice if none of the existing grid lattices is suitable for the output grids.

Prop Version: the output version of ResSum property results.

EXERCISE:

In this exercise, we will select from the Main Selection folder to determine the mapping environment and data source.

1. From the GeoFrame Application Manager, open the Geology Catalog. LPM can be launched either from the Geology Catalog, or from Geology Office.

2. In the LPM Main Selection folder, click the Surface button to open the Select Surface menu. Select the container surface [RANNOCH] from the list and click OK.

3. Click the Zone Version button to open the Select Zone Version menu. Select a Zone Version containing all needed data [ZoneVersion_demo_1], and then click OK to close the menu.

4. Click the Layer button to open the Select Layer menu. Select the target layer [RANNOCH] and click OK.

5. Click the Master Grid button to open the Select Grid menu. Select a grid with suitable grid lattice from the list [RANNOCH_depth] and click OK.

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6. Click the Prop Version button to open the Select Property Version menu. Select the Prop Version that contains the ResSum results [ResSum_Output_1], and then click OK to close the menu.

7. Toggle on All Boreholes to load all boreholes.

8. Toggle on Include Partial Penetration Values.

9. Set the Property Index Type to TVD (default).

Remember that for the purposes of this manual, the Gullfaks data set selections are shown inside brackets, as seen in steps #2 – 6 above.

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Lesson 2 — LPM Data Analysis

LPM Data Analysis deals with two very important tasks:

Calculate the correlation between reservoir interval properties and seismic attributes.

Quantifying the relationship between a reservoir property and one or more seismic attributes, if they have a “good” correlation.

EXERCISE:

In this exercise, we will learn how to use LPM to analyze the correlation between selected properties and seismic attributes, and to establish mathematical relationship between reservoir properties and seismic attributes.

1. From the LPM window, select the Data Analysis folder.

2. Under Quality Matrix, click the Properties button to open the Select Properties menu. All properties calculated in ResSum should be available here. Make your selection.

In the Gullfaks training project, the following properties were calculated from ResSum:

Gross_Porosity

Net_Pay_Porosity

Net_Pay_Thickness

Net_Pay_Water_Saturation

Net_Reservoir_Gross_Thickness_Ratio

Net_Reservoir_Porosity

Net_Reservoir_Water_Saturation

Net_Thickness

3. Under Quality Matrix, click the Attributes button to open Select Grid menu. All seismic attributes contained by the select surface should be available. Make your selection from the available seismic attributes.

In the Gullfaks training project, the following seismic attributes are calculated from Charisma:

Time_Interpretation

Depth

Integrated_Seismic_Amplitude

Integrated_Apparent_Seismic_Polarity

Integrated_Instantaneous_Frequency

Integrated_Reflection_Strength

Integrated_Cosine_Of_Phase

Max_Amplitude

Reflection_Strength

Amplitude_Difference

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4. Under Quality Indicator, toggle on Regression Coefficient (default) to calculate Linear Regression, and toggle on Significance Value for non-linear relationship.

5. Click the Edit Intersection button to open the Edit Intersection menu. Edit Default radius, if needed.

6. Click the Compute button to calculate the quality matrix. When it finishes, the Quality Matrix window should display the LPM Quality Matrix value calculated for each possible pair of Property/Seismic Attributes.

7. The Quality Matrix is displayed as a percentage value of the Correlation Coefficient, regardless of whether that is positive or negative. Color-coded cells represent the range of the degree of correlation between a property and a seismic attribute.

8. To see the cross-plot between a property and a seismic attribute, double-click the cell to open the cross-plot window. Select Regression Line, and toggle on Linear to display the regression line.

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You may toggle on and off each borehole to see the change of Quality Matrix value. You may also use this technique to eliminate erroneous data points.

NOTE: The key is to find a property that correlates to one or more seismic attributes well enough to be used as supplementary data for reservoir property mapping. For instance, Gross_Porosity correlates to Intergrated_Reflection_Strength at a Quality Matrix > 70 in the RANNOCH LAYER of the Gullfaks training project.

9. Under the Calibration Function, click the Property button to select a property. Click the Attribute button and select one or more seismic attribute grids that you intend to use for the property map. Calibration Method is set to Linear Calibration or Non-linear, based on the Quality Matrix calculation.

10. Click the Compute button to calculate the calibration formula.

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For the Gullfaks training project, three key properties need to be calibrated:

Net_Pay_Porosity

Net_Pay_Water_Saturation

Net_Gross_Thickness_Ratio

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Lesson 3 — LPM Data Population – Guided Mapping

Once the correlation relationship between a reservoir property and one or more seismic attributes is confirmed and calibrated, we can proceed to property population in the LPM Data Population folder.

There are three different approaches for property population in LPM.

Guided Mapping: A pre-defined process to directly convert the seismic attribute grid to property grid based on their calibration, then correct the residual to the well data.

CoKriging: Deterministic and stochastic, Kriging-based algorithms to populate property grid combining well data with seismic attribute.

Geostatistical Mapping: Deterministic and stochastic, Kriging-based algorithms to populate property grid with only well data.

LPM Guided Mapping consists of five fixed steps:

I. Apply Calibration Function: directly convert seismic attribute grid to property grid based on the calibration function;

II. Calculate Residual Scatter: calculate the residual values at well locations;

III. Calculate Residual Grid: generate a residual grid based on the residual scatter set;

IV. Apply Residual: correct directly converted property grid with the residual grid to ensure the match between well data and the final grid;

V. Estimate Confidence: a block map showing possible error between well data and the property grid as an indication of confidence to the result.

EXERCISE:

In this exercise, we will follow the procedures to calculate a property map using Guided Mapping.

1. In the LPM window, select the Data Population folder.

2. Under Property to Map, click Property to open the Select Property menu. Select a property from the calibrated properties [Net_Pay_Porosity].

3. Under Mapping, select the Guided Mapping folder (the default).

4. Under Apply Calibration Function, click the Compute button to calculate the

Output Grid [Net_Pay_Porosity_ESTIMATED]. Click the icon to view

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the property grid map. Click the icon to view the histogram and statistics of the grid.

5. Under Calculate Residual Scatter, set Correction Type to Residual, click the Compute button to calculate the residual scatter set

[Net_Pay_Porosity_RESIDUAL_SCATTER]. Click the icon to display the histogram and statistics for the residual scatter.

6. Under Calculate Residual Grid, select a suitable Population Algorithm [Inverse Distance].

7. Click the icon to open the LPM Gridding Parameters window.

8. Select the Search folder, and choose a search Algorithm [SuperBlock], then set the Range [~5,000]. Leave the others at default selections.

9. Select the Population folder, set the Power [2], and Tolerance [50]. Click the Close button to close the LPM Gridding Parameters window.

10. Click the Compute button to calculate the Output Grid

[Net_Pay_Porosity_RESIDUAL]. Click the icon to view the residual grid

map. Click the icon to view the residual grid histogram.

11. Under Apply Residual, give the output grid a new name [Net_Pay_Porosity_RANNOCH]. Click the Compute button to calculate. Click

the icon to view the final grid map.

12. Under Estimate Confidence, click Compute button to calculate the confidence

map [Net_Pay_Porosity_CONFIDENCE]. Click the icon to view the confidence map.

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Lesson 4 — LPM Data Population – Geostatistical Mapping

Geostatistical Mapping provides four geostatistical algorithms for property population of well data calculated from ResSum or any scattered data. Seismic attributes are not directly used in this process.

The algorithms include:

Simple Kriging: a deterministic, Kriging-based algorithm

Ordinary Kriging: a deterministic, Kriging-based algorithm

Sequential Gaussian Simulation: a Kriging-based stochastic algorithm

Uncertainty Mapping: generate probability maps and confidence maps

EXERCISE 1:

In this exercise, we will learn how to build a variogram and use a geostatistical algorithm to populate reservoir property grid.

1. In the LPM Data Population folder, under Property to Map, click the Property button and select a property [Net_Gross_Thickness_Ratio].

2. Under Mapping, select the Geostatistical Mapping folder.

3. Under Geostatistical Mapping, click the Population Algorithm button and select an algorithm [Sequential Gaussian Simulation].

4. Go back to Property To Map portion on the top of the folder. Click the icon to view the histogram distribution and statistics of the selected data.

NOTE: If you select Sequential Gaussian Simulation, the data will automatically be normalized. The grid will be automatically backtransformeded.

5. Click the icon to open both the Property Variogram Plot Specification and Property Variogram Plot windows.

6. In the Property Variogram Plot Specification window (under Variogram Direction, Horizontal), toggle on Omnidirectional (default), or Major/Minor if there is an anisotropy in the data.

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7. Select the Experimental Variogram folder. Under Spatial Equations, set the Equation to Semivariogram.

8. Under Search Distances, set the Lag and Maximum.

9. Click Compute variogram to generate the Experimental Variogram cross plot (red x) in the Property Variogram Plot window.

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10. In the Property Variogram Plot Specification window, select the Model Variogram folder.

11. Under Model Parameters, select a Model Type (Spherical, Exponential, or Gaussian) and adjust the Range, Sill, and Nugget values to fit the green model curve with the red x, trying to achieve the best possible fit.

12. Under Output Variogram Model, type in a name for the variogram. Click the Save button to the right to save the variogram. Click Close to exit the windows.

13. Back to LPM Data Population, Geostatistical Mapping folder, click the icon to open the LPM Gridding Parameters window.

14. Under Data Parameter Selection, select the Search folder. Select Algorithm. Type in an appropriate value for Range. Leave other parameters as default.

15. Select the Primary Variogram folder. Under Input Variogram Model, click the Select button and select the previously saved variogram from the list. Click the Close button to exit the LPM Gridding Parameters window.

16. In the Geostatistical Mapping folder, type in a name for the Output Grid. Click the Compute button (to its right) to start computing.

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17. Click the icon to display the result.

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EXERCISE 2

In Geostatistical Mapping, there are five algorithms for Uncertainty Mapping.

P map for given lower cutoff: Define property Lower Cutoff, the result shows the probability above the cutoff at each grid nodes.

Lower cutoff map for given P: Define Confidence Level (%), the result shows the minimum property value at each grid node.

Lower confidence limit for given P: Define Confidence Level (%), the result shows that property value at each grid node for which you are percentage confident that the value will be less than the grid value.

Upper confidence limit for given P: Define Confidence Level (%), the result shows that property value at each grid node for which you are percentage confident that the value will be more than the grid value.

Confidence spread for given P: Define Confidence Level (%), the result shows the difference between the minimum and maximum property value for the given confidence level.

In this exercise, we are going to use the well data to generate Probability Map of Gross Porosity above 20%.

1. In LPM Data Population folder, under Property to Map, click Property button and select Gross_Porosity.

2. Under Mapping, select the Geostatistical Mapping folder. Click the Population Algorithm button and select Uncertainty Mapping from the list.

3. Back to the Property to Map portion on the top of the folder, click the icon to its right to open both the Property Variogram Plot Specification and the Property Variogram Plot windows.

4. In the Property Variogram Plot Specification window (under Variogram Direction, Horizontal), toggle on Omnidirectional (default), or Major/Minor if there is an anisotropy in the data.

5. Select the Experimental Variogram folder. Under Spatial Equations, set the Equation to Semivariogram.

6. Under Search Distances, set the Lag and Maximum.

7. Click Compute variogram to generate the Experimental Variogram cross plot (red x) in the Property Variogram Plot window.


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9. Under Model Parameters, select a Model Type (Spherical, Exponential, or Gaussian) and adjust the Range, Sill, and Nugget values to fit the green model curve with the red x, trying to achieve the best possible fit.

10. Under Output Variogram Model, type in a name for the variogram. Click the Save button to the right to save the variogram. Click Close to exit the windows.

11. Back to LPM Data Population, Geostatistical Mapping folder, click the icon to open the LPM Gridding Parameters window.

12. Under Data Parameter Selection, select the Search folder. Select Algorithm. Type in an appropriate value for Range. Leave other parameters as default.

13. Select the Population folder. Under Uncertainty Mapping, set the Option to P map for given lower cutoff. Type in an appropriate value for the cutoff [0.20].

14. Select the Primary Variogram folder. Under Input Variogram Model, click the Select button and select the previously saved variogram from the list. Click the Close button to exit the LPM Gridding Parameters window.

15. In the Geostatistical Mapping folder, type in a name for the Output Grid. Click the Compute button (to its right) to start computing.

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16. Click the icon to display result.

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Lesson 5 — LPM Data Population – CoKriging

CoKriging includes a group of Kriging-based algorithms to populate property grids, combining well data with seismic attributes. They can be used to generate deterministic, as well as stochastic results.

There are four algorithms in this group:

Collocated CoKriging: a Kriging-based algorithm combining well data and seismic attributes to generate a deterministic grid.

CoKriging: similar to Collocated CoKriging, but with more restrictions, thus slower algorithm.

Ordinary Kriging with Drift: using an exist grid as a trend to guide the population of primary data.

Sequential Gaussian CoSimulation: a Kriging-based simulation algorithm combining well data and seismic attributes to generate multiple realizations of stochastic results.

EXERCISE:

In this exercise, we will learn how to combine well data and seismic attribute to generate property grid using one of the CoKriging algorithms.

1. In the LPM Data Population folder, under Property To Map, select the property you intend to map [Net_Pay_Water_Saturation]. This property needs to be calibrated in the Data Analysis folder.

2. Click the icon to view the distribution of data and statistics.

3. Click the icon to open the Property Variogram Plot Specification and the Property Variogram Plot windows.

4. In the Property Variogram Plot Specification window, under Variogram Direction, Horizontal, toggle on Omnidirectional, or Major/Minor if there is an anisotropy in the data.

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5. Select Experimental Variogram folder. Under Spatial Equation, set the Equation to Semivariogram (the default).

6. Under Search Distances, set appropriate distances for Lag and Maximum. Click Compute to generate the Experimental Variogram cross plot (red x) in Property Variogram Plot window.


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8. Under Model Parameters, select a model (Spherical, Exponential, Gaussian) under Model Type, adjust the Range, Sill and Nugget values to fit the green model curve with the red x’s, trying to achieve the best possible fit.

9. Under Output Variogram Model, type in a Name for the variogram. Click the Save button to the right to save the variogram. Click Close to close the windows.

10. In LPM Data Population folder, under Mapping, select the CoKriging folder.

11. Under Select Secondary Grid, click the Attribute button to open the Select Grid menu. Select a seismic attribute grid that correlates with the property from the list, and click OK to close the menu.

NOTE: For the Gullfaks training project, Reflection_strength correlates reasonably well with Net_Pay_Water_Saturation.

12. Under Apply CoKriging, select a Population Algorithm [Collocated

CoKriging]. Click the icon to open the LPM Gridding Parameters window.

13. In the LPM Gridding Parameters window, select the Search folder. Select a search Algorithm and set the search radius (Range).

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NOTE: In the Population folder, under Secondary Data, the value of the Correlation Coefficient is used as the default weight of the secondary data. You may adjust the number to increase/decrease the weight of the secondary data (seismic attribute).

14. Select the Primary Variogram folder. Under Input Variogram Model, click the Select button to open the Variogram Model Selector menu and select the variogram you just created. Click Close to exit the LPM Gridding Parameters window.

15. Under Apply Cokriging, type in a name for the Output Grid. Click the Compute

button to calculate the grid. Click the icon to view the result.

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Lesson 6 — Analyze Data Anisotropy Using Variomap

Anisotropy is a very common phenomenon in geological data. Due to the effect of flow direction, grain alignment, layering, etc., reservoir property data may display a better correlation in one direction than the other. Directional variogram will show a longer range in the major direction than in the minor direction. LPM in GeoFrame 4 provides us a very effective tool, Variomap, to help us determine the anisotropy of data.

EXERCISE:

In this exercise, we will learn how to build and interpret a Variomap to reveal the data anisotropy. Sufficient data is needed to build an effective Variomap. Sufficient in this case means: a) minimum 40~50 well data, b) seismic attribute grid, c) analogue data. Therefore, we are going to use a seismic attribute converted property grid as input data.

1. In LPM Data Population folder, under Property to Map, click the Property button and select a property [Net_Pay_Water_Saturation].

2. Under Mapping, select the CoKriging folder.

3. Under Apply Calibration Function, click the Compute button to generate the

Output Grid [Net_Pay_Water_Saturation_ESTIMATED]. Click the icon to view the map.

4. Click the icon to display the Variogram Plot window.

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5. From the Variogram Plot window, click the icon to open the Variogram Plot Specification window.

6. In the Variogram Plot Specification window, let the Variogram Direction remain at the default: Omnidirectional. Select the Experimental Variogram folder.

7. Set the Lag [200]. Toggle on Decimation in Horizontal direction [100]. Leave other parameters at default values. Click the Compute variogram button to display the variogram.

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8. Click the Compute Variomap to generate and display a variomap.

9. Select the Model Variogram folder. Click the Fit model button to automatically fit the model variogram to the experimental variogram. Click the Fit ellipse button to display the Ellipse Plot window.

NOTE: The ellipse generated from the Net_Pay_water_Saturation_ESTIMATED indicates the anisotropy of the data, Major direction at 0° (N-S) with a Range about 11,000 m, Minor direction at 90° (E-W) with a Range about 4,000 m.

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10. Save the variogram, and use it to populate the Net_Pay_Water_Saturation.

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Lesson 7 — Use LPM for Time-Depth Conversion

LPM Guided Mapping is often used for time-depth conversion in areas with interpreted seismic horizons and reasonably good well coverage. At the level of reservoir thickness, time horizon should have excellent linear correlation with depth markers. That linear relationship can be used to convert time horizon into depth horizon with great accuracy. A residual grid will be calculated and apply to the converted depth horizon in order to correct the converted depth horizon to the depth markers. The resulted depth grid should maintain all characteristics of the seismic horizon while honoring depth markers.

EXERCISE:

In this exercise, we will learn how to use depth markers to convert the seismic time horizon to depth in LPM.

1. In the LPM Main Selection folder, make the appropriate selections:

• Surface: select the one containing the time horizon grid you intend to convert to depth [RANNOCH]

• Zone_Version: select the one containing geological markers for the horizon you intend to convert [Zone_Version_demo_1]

• Layer: select the target layer [RANNOCH]. Not necessary for T-D conversion.

• Master Grid: select the grid with appropriate grid geometry (Binset) [RANNOCH_depth]. Create a new one if needed.

• Prop Version: select the one containing property data [ResSum_Output_1]. Not necessary for T-D conversion.

• Leave the other parameters with their default selections.

2. Click the icon in the upper-right corner of the window to open the Strat_Marker Selector. Select all markers [RANNOCH Strat_Markers] from the list, and click OK to close the menu. Now, you should see a depth marker scatter file [RANNOCH_Depth] appearing in the Scatter Set window.

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3. Select the Data Analysis folder. Under Quality Matrix, Depth set appears under the Properties window. Click the Attribute button, and select the Time grid from the list.

4. Under Quality Indicator, toggle on Regression Coefficient. Click Compute to generate the Quality Matrix. The resulting display should have excellent correlation (> 90). Double-click the cell to display the crossplot of Time-Depth, which should show excellent linear distribution.

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5. Under Calibration Function, Depth is set for Property, and the Time grid is selected for Attribute.

6. Toggle Calibration Method to Linear Calibration. Click the Compute button to calculate the function.

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7

7. Select the Data Population folder. Keep Property to Map set to Depth (the default setting).

8. Under Mapping, select the Guided Mapping folder.

9. Under Apply Calibration Function, click Compute to calculate Depth_ESTIMATED.

10. Under Calculate Residual Scatter, Correction Type is set to Residual. Click Compute to calculate Depth_RESIDUAL_SCATTER.

11. Under Calculate Residual Grid, set the Population Algorithm to Inverse

Distance (the default setting). Click the icon to open the LPM Gridding Parameters window.

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12. In the LPM Gridding Parameters window, under Data Parameters Selection, select the Search folder. Select a search Algorithm [SuperBlock], and set the search Range [5000].

13. Select the Population folder. Set the Power [2], and the Tolerance [50]. Click Close to exit the LPM Gridding Parameters window.

14. Returning to the Guided Mapping folder, under Calculate Residual Grid, click

Compute to calculate Depth_RESIDUAL. Click the icon to view the residual grid map.

15. Under Apply Residual, give the Output Grid a new name [Depth_RANNOCH].

Click the Compute button. Click the icon to view the final depth grid map.

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Lesson 8 — How to Use 3D Crossplot Tool

A 3D Crossplot is added to GeoFrame 4 LPM for visualization and data analysis between reservoir properties and seismic attributes. It is similar to GeoPlot in most of its functionality, but is specialized for LPM.

The main functions of Crossplot include:

2D or 3D cross plot between properties, attributes, boreholes, etc.

3D visualization of crossplot

Grouping of plot points

Data statistics

Regression calibration

EXERCISE:

In this exercise, we will learn the basic functions of the Crossplot tool in LPM.

1. In LPM Data Analysis folder, select Properties and Attributes.

2. Under Quality Matrix analysis, toggle on Regression Coefficient. Click the Compute button to start calculation.

3. In the Quality Matrix window, highlight a cell [Gross_Porosity – Reflection Strength] from the spreadsheet to select a Property-Attribute

pair. Toggle on 3D Crossplot. Click the icon to open LPM XPlot window.

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4. Under Variables panel, set Z for the Boreholes to see the 3D crossplot. Click

the icon to change to View mode, and use MB1 to rotate the 3D cube, MB2

to move the image. Click the icon to change to the Select mode.

5. To define Selections, toggle on any selection window and type in a name for the

selection. Click the icon to draw a rectangle area and select the points within.

6. To view statistics of the data, select Statistics > Statistics in LPM XPlot to open the Statistics menu. You may select All Points or any Selection group to display statistics in a spreadsheet.

7. In LPM XPlot window, select Statistics > Correlation to open the Correlation Coefficient menu. The Correlation Coefficient of selected property-attribute will be displayed.

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8. To calculate Regression between a reservoir property and a seismic attribute, select Statistics > Regression in the LPM XPlot window to open the Regression menu.

9. In the Regression menu, select a Fit algorithm under Fit Options. Under Regression Parameters, set Degree and Constraint.

10. Under Appearance, type in a Name. Click the Fit button to calculate the Regression results.

--- End of Document ---

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Reservoir Property Mapping - read.pudn.comread.pudn.com/downloads130/doc/556189/train/train/GF4_LPM_man… · GeoFrame 4 Reservoir Property Mapping Schlumberger About this Course