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A seismic inversion method for fluvial
reservoirs in Southeast Asia
Mark Sams Ikon Science
David Carter Kris Energy
28th April 2017
SEC 2017
3
Motivation
Aco
ustic im
ped
ance (g
/cm3 x m
/s)
Full-band
AI Inversion
Low spatial
frequency
variations may
arise from poor
spatial sampling
of the well data Yellow / reds =
low impedance After Li & Zhao (2014),
4
Modern day Fluvial Sand Bodies – Analogues of the Sub-surface
Deposits vary from narrow (often
sinuous) ‘shoe-string’ sands to wider
sands with complex internal
architecture
Fluvial deposits often contain abrupt
lateral facies changes due to climatic
variations and the lateral limits of channel
migration prior to avulsion
In the subsurface these often control the
sweep pattern during production
Motivation
5
Productive Fluvial Hydrocarbon Reservoirs from 3-D Seismic
Narrow reservoirs with
internal architecture
that controls sweep
pattern and local
compartmentalization,
Wide, locally composite
reservoirs containing
abandoned channels that
influence the drainage
areas of wells
Reservoir A Depletion Drive
Reservoir B Water Drive
A
B
Well 1 Well 2 Well 3 Well 4 Well 5 Well 6
~3300’ ss, ~3500’ ss,
reds = sands
after Carter (2003),
Motivation
6
Locally adapting LF model
Sand Shale
Porosity Saturation
Acoustic Impedance
Relative Impedance
Absolute Impedance
Impedance Trends
Low Frequency
7
Thailand case
100
ms
Acoustic Impedance gm/cc.m/s
2000 - 12000 2000 - 12000 2000 - 12000 -4000 - 4000 2000 - 12000
8
Thailand case
2000 - 12000
Acoustic Impedance gm/cc.m/s
1.5 - 3.0
100
ms
Vp/Vs
2000 - 12000 1.95 - 2.95
Density gm/cc
1.95 - 2.95 1.5 - 3.0
10
Metrics
1-2-6-12 Hz
LF model from interpolation of first well drilled
Cross correlation 0.75
RMS error 288 g/cc.m/s
LF model from interpretation
Cross correlation 0.94
RMS error 60 g/cc.m/s
Accuracy
22
LF models are a very important part of quantitative seismic reservoir characterisation
Standard approach to LF model construction is not suitable for fluvial reservoirs
A locally adapting approach has significant advantages
This approach can be automated through a facies based inversion
Any method requires
Understanding the geology
Understanding the rock physics
Conclusions
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