3D Oil Reservoir Visualisation Using Octree Compression Techniques Utilising Logical Grid Co-Ordinates
Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.
<p>[1] R. McCauley, Marine seismic surveys: a study of environmental
implications. Australian Petroleum Production and Exploration
Association, 2000.
[2] P. Samier, “Reservoir simulation in the oil industry,” APOS-EU,
vol. 1, p. 1, July 2011.
[3] D. G. Donato, E.-O. Obi, and J. M. Blunt, “Anomalous transport
in heterogeneous media demonstrated by streamline-based simulation,”
Geophysical Research Letters, vol. 30, no. 12, pp. 1–4,
2003.
[4] C. Zhang, A. Bakshi, and V. K. Prasanna, “Data component
based management of reservoir simulation models,” in Information
Reuse and Integration, 2008. IRI 2008. IEEE International
Conference on, 2008, pp. 386–392.
[5] J. R. Fanchi, Fundamentals of Reservoir Simulation. Burlington:
Gulf Professional Publishing, 2006a, pp. 162–186, doi:
10.1016/B978-075067933-6/50012-X.
[6] Conceptual Reservoir Scales. Burlington: Gulf
Professional Publishing, 2006b, ch. 12, pp. 210–232, doi:
10.1016/B978-075067933-6/50014-3.
[7] J. E. Aarnes, V. Kippe, and K.-A. Lie, “Mixed multiscale finite
elements and streamline methods for reservoir simulation of large
geomodels,” Advances in Water Resources, vol. 28, no. 3, pp.
257–271, 2005, doi: 10.1016/j.advwatres.2004.10.007.
[8] J. Yu and H. Sun, “Influence analysis of calculation error of
reservoir numerical simulation by direction and size of grid,”
Flow in Porous Media - from Phenomena to Engineering and
Beyond, vol. 1, pp. 152–156, 2009.
[9] J. Bonet and J. Peraire, “An alternating digital tree (adt) algorithm
for 3d geometric searching and intersection problems,” International
Journal for Numerical Methods in Engineering, vol. 31,
no. 1, pp. 1–17, 1991.
[10] M. Manouvrier, M. Rukoz, and G. Jomier, “Quadtree representations
for storage and manipulation of clusters of images,” Image
and Vision Computing, vol. 20, no. 7, pp. 513–527, 2002.
[11] A. Filinski, “Recursion from iteration,” LISP and Symbolic Computation,
vol. 7, no. 1, pp. 11–37, 1994.
[12] G. Favalora, R. Dorval, D. Hall, M. Giovinco, and J. Napoli,
“Volumetric three-dimensional display system with rasterization
hardware,” in Proc SPIE, vol. 4297, 2001, pp. 227–235.
[13] D. Salomon, Data Compression, 4th ed., W. Wheeler, Ed. London:
Springer-Verlag, 2005.
[14] E. Angel and S. Dave, Interactive Computer Graphics A Topdown
Approach With Shader-based OpenGL, 6th ed., M. Hirsch,
Ed. Pearson, 2012.
[15] D. Shreiner, OpenGL programming guide: the official guide
to learning OpenGL, versions 3.0 and 3.1. Addison-Wesley
Professional, 2010, vol. 1.</p>
<p> </p>
<p>[1] R. McCauley, Marine seismic surveys: a study of environmental
implications. Australian Petroleum Production and Exploration
Association, 2000.
[2] P. Samier, “Reservoir simulation in the oil industry,” APOS-EU,
vol. 1, p. 1, July 2011.
[3] D. G. Donato, E.-O. Obi, and J. M. Blunt, “Anomalous transport
in heterogeneous media demonstrated by streamline-based simulation,”
Geophysical Research Letters, vol. 30, no. 12, pp. 1–4,
2003.
[4] C. Zhang, A. Bakshi, and V. K. Prasanna, “Data component
based management of reservoir simulation models,” in Information
Reuse and Integration, 2008. IRI 2008. IEEE International
Conference on, 2008, pp. 386–392.
[5] J. R. Fanchi, Fundamentals of Reservoir Simulation. Burlington:
Gulf Professional Publishing, 2006a, pp. 162–186, doi:
10.1016/B978-075067933-6/50012-X.
[6] Conceptual Reservoir Scales. Burlington: Gulf
Professional Publishing, 2006b, ch. 12, pp. 210–232, doi:
10.1016/B978-075067933-6/50014-3.
[7] J. E. Aarnes, V. Kippe, and K.-A. Lie, “Mixed multiscale finite
elements and streamline methods for reservoir simulation of large
geomodels,” Advances in Water Resources, vol. 28, no. 3, pp.
257–271, 2005, doi: 10.1016/j.advwatres.2004.10.007.
[8] J. Yu and H. Sun, “Influence analysis of calculation error of
reservoir numerical simulation by direction and size of grid,”
Flow in Porous Media - from Phenomena to Engineering and
Beyond, vol. 1, pp. 152–156, 2009.
[9] J. Bonet and J. Peraire, “An alternating digital tree (adt) algorithm
for 3d geometric searching and intersection problems,” International
Journal for Numerical Methods in Engineering, vol. 31,
no. 1, pp. 1–17, 1991.
[10] M. Manouvrier, M. Rukoz, and G. Jomier, “Quadtree representations
for storage and manipulation of clusters of images,” Image
and Vision Computing, vol. 20, no. 7, pp. 513–527, 2002.
[11] A. Filinski, “Recursion from iteration,” LISP and Symbolic Computation,
vol. 7, no. 1, pp. 11–37, 1994.
[12] G. Favalora, R. Dorval, D. Hall, M. Giovinco, and J. Napoli,
“Volumetric three-dimensional display system with rasterization
hardware,” in Proc SPIE, vol. 4297, 2001, pp. 227–235.
[13] D. Salomon, Data Compression, 4th ed., W. Wheeler, Ed. London:
Springer-Verlag, 2005.
[14] E. Angel and S. Dave, Interactive Computer Graphics A Topdown
Approach With Shader-based OpenGL, 6th ed., M. Hirsch,
Ed. Pearson, 2012.
[15] D. Shreiner, OpenGL programming guide: the official guide
to learning OpenGL, versions 3.0 and 3.1. Addison-Wesley
Professional, 2010, vol. 1.</p>
<p> </p>
@article{"International Journal of Information, Control and Computer Sciences:52654", author = "S. Mulholland", title = "3D Oil Reservoir Visualisation Using Octree Compression Techniques Utilising Logical Grid Co-Ordinates", abstract = "Octree compression techniques have been used
for several years for compressing large three dimensional data
sets into homogeneous regions. This compression technique
is ideally suited to datasets which have similar values in
clusters. Oil engineers represent reservoirs as a three dimensional
grid where hydrocarbons occur naturally in clusters. This
research looks at the efficiency of storing these grids using
octree compression techniques where grid cells are broken
into active and inactive regions. Initial experiments yielded
high compression ratios as only active leaf nodes and their
ancestor, header nodes are stored as a bitstream to file on
disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the
graphics card eliminating the need of reconstructing the original
matrix. This results in a more compact vertex table, which can
be loaded into the graphics card quicker and generating shorter
refresh delay times.
", keywords = "3D visualisation, compressed vertex tables,
octree compression techniques, oil reservoir grids.", volume = "7", number = "7", pages = "894-7", }
