A reliable reservoir model is an invaluable tool for risk reduction. I will give an overview of seismic reservoir characterization and the quantitative interpretation workflow including the use of pre and post stack seismic attributes and inversion outputs for mapping reservoir properties and integration of the attribute output with petrophysical data to create quantitative reservoir models.
2. 2
Outline
• Brief Introduction to PetroTeach
• Introducing our Distinguished Instructor Dr. Andrew Ross
• Webinar Presentation (45 - 60 min.)
• Course Agenda on “Seismic Reservoir Characterization”
• Q&A (15 - 20 min.)
3. Introduction to PetroTeach
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4. 4
Tuesday 1th – 16:00 GMT
Nightmare of Hydrate Blockage
Professor Bahman Tohidi
Wednesday 9th – 16:00 GMT
Seismic Reservoir Characterization
Dr. Andrew Ross
Thursday 10th – 16:00 GMT
Hydraulic Fracturing
Jerry Rusnak
Monday 14th – 17:00 GMT
3D Printing: The Future of Geology
Dr. Franek Hasiuk and Dr. Sergey Ishutov
Free Webinars in September
Monday 21th – 17:00 GMT
Elements of Fiscal Regimes and Impact on
E&P Economics and Take Statistics
Professor Wumi Illedare
Thursday 3th – 16:00 GMT
Advanced Petrophysics
Mostafa Haggag
6. Dr. Andrew Ross
PetroTeach
Distingushed Instructor
• Andrew has a Ph.D. in Geological Sciences from Cornell University ,
M.Sc. in Exploration Geophysics from Imperial College, Bachelors
(Hons.) in Physics from Hertford College, Oxford.
• He had a postdoctoral research position at the University of
Copenhagen from 1999 to 2003.
• Dr. Andrew Ross has over 30 years of experience in seismology and
geophysics in both industry and academia.
• He has worked with using seismic data to determine subsurface rock
properties at all scales in the Earth, from the reservoir to whole mantle
scales.
• He has worked previously for Hess company as a seismic interpreter,
for Ødegaard on supporting seismic inversion, and for Schlumberger on
supporting seismic inversion and Petrel applications.
• Since 2015, he has been an independent consultant running training in
seismic interpretation, inversion attributes, and quantitative seismic
interpretation.
7. Why do we need seismic reservoir
characterization ?
8. Reservoir characterization - inputs
• Data sources
• Geological background
• Wireline log data
• Rock physics model
• Core data
• Seismic data
• Seismic stratigraphy
• Seismic attributes
• Inversion and Quantitative
Interpretation
Reservoir model
9. Qualitative vs Quantitative
Can seismic predict some key characteristics and properties?
• Depth
• Geological Setting - Origin of Rocks/Fluids
• Geological Structure
• Geometry – thickness, areal extent, volume, seals
• Heterogeneity – Layering, Faults/Fractures,
Compartments
• Lithology
• Porosity
• Saturation distribution
• Pressure Distribution
• Changes in pressure/saturation
10. 10
Seismic Bandwidth limitations
• Low frequencies controlled by
geology prior model
• Intermediate frequencies
controlled by bandwidth of seismic
amplitudes
• High frequencies controlled by
vertical variogram model from well
data Frequency (Hz)
Power
Prior Seismic Variogram
11. Regional geology and stratigraphy
• Regional geology and stratigraphy in basin
• Ages
• Unconformities
• Depositional settings
Lithostratigraphic chart of the North Sea (NPD)
12. Well data: Wireline logs
• Information only at well location
• Calibrated
• Lithology determination
• Fluid identification
• Remote sensing so errors and
assumptions in methodology
• Relatively expensive to acquire
• Check shots needed to compare with
seismic data
Kansas Geological Society
13. Well data: Cores and mud logs
Information only at well location
• Calibrated
• Direct measurements
• Porosity
• Permeability
• Moduli
• Cores not in situ so some uncertainty
in measurements
Expensive to acquire (need a well)
Utah Geological Survey
Jurassic Navajo Sandstone f = 15%
14. Reservoir rock physics model
Modified from Avseth
et al. 2005
Clay
effect
Prediction of the reservoir
characteristics variables like
porosity and clay content
change
Uses wireline and core data
from multiple wells in a basin
or sub basin
Model applicability
dependent on quality of
wellbore data and proximity
to area of interest
15. Seismic data
Information between wells
• Uncalibrated without well control
(check shots)
• 2D, 3D and time-lapse (4D)
• P-wave or S-wave recording
• Structural and stratigraphic
information
USGS Central Alaska
NRPA
16. • Identify features of interest in
seismic data
• Relate these features to modern
analogues
• Infer the location of reservoirs, traps
and hydrocarbons
• Provide input to geological model
• Relate quantitative measurements
to well data and petrophysical
models
Seismic interpretation: using our data
correctly
Courtesy ConocoPhillips and Geoscience Australia
17. Attributes and Quantitative Interpretation
Seismic attributes derived from amplitudes
• Additional information related to bed
thickness, fluids, porosity etc.
Inversion and Quantitative Interpretation
• Convert seismic to elastic properties
• Relate seismic to rock physics model to
produce property volume e.g. porosity
Porosity cube from NN on F3 Dataset, OpendTect
F3 demo
18. Technology Timeline
1960 1970 1980 1990 2000
Digital recording
Bright spot
technology -
Amplitudes Complex trace Coherency
Spectral
decomposition
Curvature
Interval
attributes
Seismic
stratigraphy
3D Seismic
Work
stations
High resolution
3D
4D; QI; AI
Interpretation on seismic paper sections
(modified from Chopra & Marfurt,
2005)
2010
AVO
Pre-stack data
Inversion
2020
Stochastic
Inversion
19. PSDM depth vs PSDM time
Depth Time
Depth(m)
TWT(ms)
Volve data courtesy of
Equinor
Depth data appears stretched because velocities increase with depth
20. Effect of tuning on amplitude
Seismic amplitude as a function of layer thickness for a given wavelength
Wedge modelling with real logs gives better estimate than simple theoretical wedge
Layer thickness
Relativeamplitude
l/
4
1.
0
Offset
Tim
e
22. Color Blending – identifying thin beds
Time 880 ms
RGB Blend 25 Hz - 35 Hz - 45 Hz CCT cubes
Time 880 ms
Amplitude
23. RGB blend fan interpretation
Frequency Decomposition RGB blend at mid-upper fan interval highlighting areas of interpreted upper
fan sand presence (white dash polygons). Table) List of upper fan sand thicknesses as listed in well
reports.
Understanding frequency decomposition colour blends using forward modelling —
examples from the Scarborough gas field, Chris Han, First Break, May 2018
24. From the top of the producing reservoir:
a) time structure map in contours with an
amplitude overlay in colour and b) SOM
classification with low probability less than
1% denoted by white areas.
Interpretation of DHI characteristics with machine learning
Rocky Roden and Ching Wen Chen, First Break, May 2017
Machine learning – combining attributes
25. Machine learning - Class 3 AVO anomaly
North-south vertical profile 9411 through the middle of the field: a) stacked seismic amplitude display with the field location
designated, b) SOM classification with 25 neurons indicated by the 2D colour map over a 170 ms window, and c) three neurons
highlighting the reservoir above the oil/water and gas/oil contacts and the hydrocarbon contacts (flat spots). The expanded insets
denote the details from the SOM results at the downdip edge of the field.
Interpretation of DHI characteristics with machine learning
Rocky Roden and Ching Wen Chen, First Break, May 2017
27. Inversion results at a discovery well
Seismic inversion results at the Yttergryta well location, including (a) acoustic impedance
and (b) VP /VS. The hydrocarbon zones are identified with relatively low AI and VP /VS
values.
Avseth et al.
2016
28. Attributes from inversion outputs: CPEI
and PEIL
Curved pseudo-elastic impedance (CPEI),
Pseudoelastic impedance for lithology
(PEIL)
Avseth & Veggerland,
2015
29. Ambiguous inversion results discriminated
(a) The PEIL and (b) CPEI estimated from the seismic
inversion data. Note the increasing rock stiffness with depth to
the left and the strong fluid anomaly to the right
Seismic inversion data along a seismic section
intersecting
a well. (a) The AI and (b) VP∕VS. We see a relatively soft
AI anomaly and a strong low VP∕VS anomaly near the top
of a rotated fault block. The VP∕VS indicates that there is a
flat
spot event. However, we also see low VP∕VS anomalies
below
the flat spot.
Avseth & Veggerland,
2015
30. LFM vs FWI controlled inversion results
Quantitative interpretation workflows integrating separated wavefield seismic data and
FWI P-velocities for reservoir characterization in areas with limited access to well
information.
Feuilleaubois et al., 2017
31. Time lapse (4D) inversion
(a) Acoustic impedance and (b) Poisson’s ratio can be derived from 3D AVO inversion. Time-lapse AVO inversion
additionally allows computation of (c) change in acoustic impedance and (d) change in Poisson’s ratio
Herwanger et al. 2010
32. Comparison with simulation results
(a) Map of DSw derived from time-lapse rock physics inversion and (b) map of DSw predicted by the reservoir simulation model. The average water
saturation in the lower reservoir (Tor) is vertically averaged and the average DSw is displayed on the base reservoir surface.
Herwanger et al. 2010
33. Lithology determination: Lithology classes
and PDFs
(a) Crossplot of P-impedance versus VP /VS ratio using data from (b) Scarab-Da and (c)
Scarab-Db wells. Reservoir lithologies are defined using FMI facies. The FMI facies and
the calculated P-impedance and VP /VS curves for the Scarab-Da and Scarab-Db wells
are shown in (b) and (c), respectively.
Unlocking gas reserves in bypassed stratigraphic traps in a deepwater brownfield
using prestack seismic inversion: A case study from offshore Nile Delta, Egypt
Hamed Z. El-Mowafy, Mohammed Ibrahim, and Dallas B. Dunlap, The Leading Edge, July
2018
34. Lithology determination: Inversion results
a) P-impedance map for the prospective Upper Scarab RML. (b) South-southeast–north-northwest section along the
proximal levee showing the character of the RML and the LAPs reservoirs from facies classification (upper panel), P-
impedance (middle panel), and VP /VS (lower panel).
El-Mowafy et al., 2018
35. Lithology determination: Facies sections
a) Northwest–southeast facies classification section through Scarab-Da well showing the distribution of the thin-bedded (brown) and thick-bedded (red)
gas-sand facies in the wedge-shaped RML, Upper Scarab Channel, and Lower Scarab Channel. (b) Probability distribution of thick-bedded gas-sand facies.
(c) Probability distribution of thin-bedded gas-sand facies.
El-Mowafy et al., 2018
36. Simultaneous inversion results
Simultaneous inversion quantitative interpretation exact extractions at RRU4 horizon. (a)
seismic amplitude, (b) most probably facies using 2D Bayesian classification, and (c) porosity
Quantitative interpretation using conventional and facies-based pre-stack inversion — A thin
dolomite reservoir case study in Cabin Creek Field, Williston Basin
Paul El Khoury*, Ehsan Zabihi Naeini, Thomas L. Davis. First Break, June 2018
37. Facies based inversion results
Facies-based inversion quantitative interpretation using exact extractions at RRU4 horizon. (a)
seismic amplitude, (b) most probably facies from facies-based inversion, and (c) porosity
El Khoury et al.,2018
38. Improved correlation with well data for
facies-based inversion
Red River U4 interval seismic-derived porosity versus porosity from well data. Red dots represent nine wells within Cabin Creek Unit.
Porosity value at each well corresponds to the mean log value (red dot) for RRU4 interval and uncertainty bar (horizontal) is the
standard deviation. Seismic-derived porosity is extracted at the corresponding well location with 2.5 p.u. uncertainty bar width
(vertical). Blue line represents the 1:1 line.
El Khoury et al.,2018
39. Stochastic inversion
Geostatistical seismic inversion workflow.
Application of geostatistical seismic inversion in reservoir characterization of
Sapphire gas field, offshore Nile Delta, Egypt
Mohamed G. El-Behiry, Said M. Dahroug, and Mohamed Elattar
The Leading Edge, June 2019
40. Stochastic inversion results
• Three P-impedance high-frequency
realizations overlain by original
unfiltered P-impedance logs.
El-Behiry et al., 2019
41. Deterministic vs stochastic comparison
A comparison between deterministic and geostatistical inversions. (a) Deterministic P-impedance. (b) Geostatistical P-impedance
= mean of 150 realizations. (c) Minimum P-impedance extraction from deterministic inversion between top and base Sapphire-40
surfaces overlain by top S40 two-way-time contour lines. Unfiltered log curves are overlain on top of both inversion results.
El-Behiry et al., 2019
42. Using stochastic inversion results
Acoustic impedance distribution for
Sand and shale (log data). The
value of 8150 [m/s*gr] is used for
sand-shale discrimination.
CDF of average sand fraction from
100 acoustic impedance
realizations.
Deterministic
sand (8.5%)
Stochastic
sandP50: 13.2%
Francis, 2006
43. SEISMIC RESERVOIR CHARACTERIZATION (online)
12 – 16 Oct. 2020
18 – 22 Jan. 2021
Register@petro-teach.com
The course will cover identifying and evaluating appropriate seismic attributes for mapping reservoir
properties and integration of the attribute output with petrophysical data to create quantitative
reservoir models. We will also cover the use of well wireline and core data to create rock physics
models and the correlation of those models with seismic data with the final aim of creating an
integrated reservoir model. The theory and application of both post and pre-stack (AVO) attributes will
be covered. We will look at deterministic and stochastic seismic inversion methodology to produce
reservoir elastic parameters and using those parameters to convert to lithologies. We will also cover
the evaluation of the uncertainty in the output. We will look a the using multiple attributes in
supervised and unsupervised classification with linear and artificial intelligence methods. We will also
cover time-lapse (4D) and multi-component seismic (3C) and azimuthal inversion for fracture
densities. The course will be conducted by group discussion, and Exercises and using of Software.
Learning Objectives
• Understand basics of relation between rock physics and seismic attributes
• Understand theory behind creation of seismic attributes and interpretation of attributes
• Understand theory and application of seismic inversion
• Understand application of artificial intelligence in classifying attributes
• Understand the contribution of seismic controlled inputs to quantitative reservoir models
Course price (Euro):
• Normal registration:1490+VAT
• 20% DISCOUNT for PhD students, Group (≥ 3 person) and early bird registrants (1 week before)
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