Cockshott, W.

Mulholland, S.

English

Enlighten

        Mulholland, S., and Cockshott, W. (2013) 3D Visualisation of Oil Reservoirs [POSTER]. In: Set for Britain, 18 March 2013, London, UK.   Copyright © 2013 The Author    A copy can be downloaded for personal non-commercial research or study, without prior permission or charge   The content must not be changed in any way or reproduced in any format or medium without the formal permission of the copyright holder(s)   When referring to this work, full bibliographic details must be given       http://eprints.gla.ac.uk/77571/        Deposited on: 04 April 2013             Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk 3D Visualisation Using Octree Compression Techniques  S. Mulholland - 2nd year PhD student at Glasgow University  Dr. P. Cockshott – 1st supervisor at Glasgow University, School of Computing Science,  Sir Alwyn Williams Building, Room AW 407, G12 8QQ  Dr. R. Poet – 2nd supervisor at Glasgow University, School of Computing Science, 13  Lilybank Gardens, Room S132, Glasgow G12 8QQ  Dr. R. Barrett – Industrial supervisor at Sciencesoft Ltd Moorpark House 11 Orton Place Glasgow G51 2HF  Oil reservoirs can be extremely large spanning several miles horizontally but are very thin. The subsurface is scanned using seismic sampling and the resulting data can be used to give a 3D representation of potential oil fields. The more accurate the model required the smaller distance between samples. Due to the volumes of rock being represented in these data sets storing this data generates large files. The greater the demand for accuracy the greater the frequency in sampling resulting in larger file sizes.  Octree compression techniques have been used for several years for compressing large three dimensional data sets into homogeneous regions. This type of compression is ideally suited to datasets which have similar values in clusters. Oil engineers represent reservoirs in a 3D grid where the volumes of hydrocarbons occur naturally in clusters. This research looks at the efficiency of storing such a matrix using octree compression techniques where the grids are broken into active and inactive regions. Active cells are those homogeneous volumetric regions within the oil reservoir which contain accessible hydrocarbons and inactive cells are those which do not. The resulting compressed binary files only store these active cells and the parent header cells. This results in a far more compact data structure.   Experiments yielded high compression ratios with significant savings in file space. Further savings in computation time and memory were possible as 3D visualisations were generated while streaming in the compressed files with only the active cells being sent to the graphics card eliminating the need of reconstructing the original matrix.    Octree structure illustrating active white leafcells in a 8 x 8 x 8 element matrix         3D visualisation of a test reservoir sample  

3D Visualisation of Oil Reservoirs [POSTER]

Enlighten: Publications

  
 
 
 
 
 
 
Mulholland, S., and Cockshott, W. (2013) 3D Visualisation of Oil 
Reservoirs [POSTER]. In: Set for Britain, 18 March 2013, London, UK. 
 
 
Copyright © 2013 The Author 
 
 
 
A copy can be downloaded for personal non-commercial research or 
study, without prior permission or charge 
 
 
The content must not be changed in any way or reproduced in any format 
or medium without the formal permission of the copyright holder(s) 
 
 
When referring to this work, full bibliographic details must be given 
 
 
 
 
 
 
http://eprints.gla.ac.uk/77571/ 
 
 
 
 
 
 
 
Deposited on: 04 April 2013 
 
 
 
 
 
 
 
 
 
 
 
 
Enlighten – Research publications by members of the University of Glasgow 
http://eprints.gla.ac.uk 
3D Visualisation of Oil Reservoirs 
Industrial sponsor 
1st Supervisor Dr. Paul Cockshott 
Samantha Mulholland – 3rd year PhD student 2nd Supervisor Dr. Ron Poet 
Industrial Supervisor Dr. Richard Barrett 
Oil reservoirs are large volumetric datasets and can be several kilometres in both width and breadth 
and up to several hundred metres deep. Using seismic testing data, these reservoirs are divided into 
individual cells, each of which represent a hexahedral volume within the reservoir. At present these 
reservoirs can contain several million of such cells. Simulators calculate the various hydrocarbon data 
values from the information obtained from rock properties allowing for visual illustrations of the 
reservoir, its reserves and for accurate predictions of future production levels. Increasing the number of 
cells used to represent a reservoir decreases the cell volume and allows for greater accuracy. With 
advancements in multi-core graphics cards we can now visualise oil reservoir models containing up to 
one hundred million cells. Increasing the number of cells results in extremely large file sizes. Octree 
data compression techniques will prove to store the data in a more compressed and manageable state. 
This research also looks at how to improve on industry standard lookup times generated when 
traversing these datasets of active and inactive cells.  
2D Test Matrices 
2 Flavours  
10 million & 20 million cell  matrices 
 
Randomly scattered  
active matrices  -25 % 
active 
Clustered active  
matrices  -25 % 
active 
2D Quadtree Results 
Quicker search times with both flavours of matrices 
Quicker search times with all aspect ratios of matrices 
Quicker search times with matrices possessing maximum entropy 
Compressed Quadtree structures – 2D 
512 pixels wide 
512 pixels High 
Compress image 
as a quadtree 
400 pixels wide Superimposed into the 
next Largest  
power-of-2 size 
400 pixels High 
Quadtree compression 
Octree compression 
3D Matrix  At Power-of-2 Size Generated From Reservoir Sample 
196 x 129 x 105 cells 
2654820 total cells 
1= active cell 
0 = inactive cell 
Populates a 3D 
matrix 
256 cells deep (z-axis) 
256 cells wide(x-axis) 
256 cells high (y-axis) 
105 cells deep (z-axis) 
196 cells wide(x-axis) 
129 cells high (y-axis) 
Superimposed  
into the next  
power-of-2  matrix size 
Compressed Vertex Tables 
Option 1 
Only active leaf nodes populate a compressed 
vertex table and sent to the graphics card 
 
Option 2 
Only the exposed faces of each  active leaf node 
populates the compressed vertex table and sent 
 to the graphics card 
3D Visualisation 
3D oil reservoir model visualisations 
 Only exposed active octree leaf node faces are drawn 
Retains the geological characteristics of fault lines 
Only writes active leaf 
nodes and their 
ancestor header nodes 
to the bitstream 
 
Octree Compression And Bitstream Generation – 3D 
Octree - 3D De-compression Savings 
No need to add pointers during de-compressing 
 Vertex table values are sent straight to the graphics card 
Lossless compression of floating point vertex co-ordinates 
Face Culling 
Face of leaf node is totally exposed 
Write a ‘1’ to the face culling flag 
Face of leaf node is totally hidden 
Write a ‘0’ to the face culling flag 
Face of leaf node is partially hidden 
Write a ‘1’ to the face culling flag 
Inactive leaf node 
faces being evaluated 
faces being evaluated 
faces being evaluated 
Active leaf node 


http://eprints.gla.ac.uk/77571/3/77571.pdf

3D Visualisation of Oil Reservoirs [POSTER]

Abstract

Similar works

Full text

Available Versions

Enlighten

Enlighten: Publications