Download January 2016 – January 2019

SCHEDULE “A”
CONSORTIUM FOR HEAVY OIL/UNCONVENTIONAL RESEARCH BY
UNIVERSITY SCIENTISTS
RESEARCH PROPOSAL
January 2016 – January 2019
Summary of Research
This research project work plan outlines the use of reservoir characterization in unconventional
petroleum reservoirs - including reservoir simulation, engineering geoscience, geology (core analysis and
petrophysics), and seismic monitoring applications. This reservoir characterization for enhanced heavy oil
production/.tight oil/gas reservoirs will be implemented by the Consortium for Heavy Oil/Unconventional
Reservoir Research by University Scientists (CHORUS). The CHORUS research project will be conducted
through our various industry sponsors, university faculty project research staff, and the several project
graduate students located in the Department of Geoscience, University of Calgary and the Department of
Geophysics, Colorado School of Mines., This research project will be in complete cooperation with our
CHORUS international heavy oil and unconventional industry/oil field services colleagues. This proposal
outlines the research activity and personnel participating in the project. CHORUS is a global leader in the
investigation of reservoir characterization for enhanced heavy oil recovery using CHOPS (Cold Heavy Oil
Production with Sand). CHORUS has also done some very interesting reservoir characterization field
studies in thermal heavy oil recovery methods such as SAGD (Steam Assisted Gravity Drainage). At this
time CHORUS is also investigating the tight oil/shale gas techniques as well.
CHORUS will be doing a large heavy oil field project study that will develop and resolve the
many challenges of carbon fracture isses, sandstone and shale reservoirs by using the integrated reservoir
characterization methods. With the expansion of the CHORUS project now focusing on the South
American heavy oil reservoirs, these new collaborations in core geology, seismic inversion, rock physics
fluid saturation, AVO, new fracture problems in carbonate reservoirs and other well challenges will bring
new important solutions to the heavy oil/bitumen oil industry sponsors.
1.0 Background
Our CHORUS team research project at the University of Calgary has studied the seismic
monitoring/reservoir characterization/reservoir engineering simulations of hot and cold heavy oil
production for more than 16 years. At this time due to the global industry needs, CHORUS will be
investigating unconventional reservoirs. All of the team research has been funded by the global heavy oil
industry research contracts. Our research consortium team specializes in the areas of heavy oil/, tight oil
and gas reservoir engineering characterization and simulation, and geoscience (seismic, rock physics,
geomechanics, core analysis and petrophysics).
1.1 Project Objectives
The long-term goal is to develop and apply a joint inversion software system for reservoir
characterization of geological, geophysical, and reservoir production data. The new technical research
science for the unconventional reservoir will be extremely useful to industry. In the contemporary
unconventional reservoir development, hydraulic fracturing of the reservoir is essential to achieve success.
Hydraulic fracturing is particularly important within the unconventional shale plays due to the inherent lack
of natural permeability. Therefore one of the key factors that must be assessed when attempting to
characterize a potential shale reservoir is the formation brittleness. Elemental and mineralogical based
brittleness affects the relationship and by combining mineralogical and elemental data with an
environmental and depositional interpretation, an effective extrapolation of formation brittleness from
using mineralogy is possible.
In this unconventional research project we are investigating the brittleness throughout the
reservoir. We will integrate the multicomponent seismic, rock physics, geomechancis and engineering
methods to determine brittleness. Through this integration the issues and errors associated with each
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methodology will be defined. The environmental and depositional research methods will be obtained by
integrating petrophysical log analysis , lithological core and cuttings analysis, ichnological analysis and
sequence stratigraphy. These data will be organized in a facies based model where the lithofacies,
petrophysical facies and chemofacies will be related. These facies will then be upscaled and tied into the
regional seismic along with the logs and core to assist in the sequence stratigraphic interpretation. Synthetic
core software will be developed and available upon thesis publication.
1.2 Tight oil/gas reservoirs
In recent years, there has been a revolution in production from tight shale formations that have
very low porosity and permeability. The extraction of oil from such formations has been made possible by
induced fracturing. The creation of fractures through injected fluids has essentially meant that shale is not
only a petroleum source rock and a seal, but can now be considered a reservoir rock.
For such production from tight reservoirs, it is important to characterize the lithology and
“fracability” of reservoir rocks. This can be done through analysis of core, well logs, 3-D seismic data, and
microseismic monitoring in order to map induced fractures. Rock physics characterization of the reservoir
sweet spots is made possible through mapping the rock’s brittleness and ductility. Brittleness and ductility
mapping can be described by the elastic constants of Poisson’s ratio and Young’s modulus. Both of these
constants can be derived from knowledge of the P-wave and S-wave velocities, and these velocities can be
estimated from seismic data or from dipole sonic logs.
2.0 Joint inversion of geophysical data
Earth models can be derived by using several types of geophysical data combined with
geophysical modeling in a process known as joint inversion.
2.1 Least-squares model-based inversion
Our determination of Earth models derived from real data is made possible by a model-based
least-squares inversion process. We can fit seismic, electromagnetic, and potential field observations by
Earth modeling, and in the process we can obtain reliable estimates of the reservoir rock properties. The
model-based inversion method (Lines and Treitel, 1984) would involve least-squares fitting of model
responses to observations by the following. Let d be the vector of observations, f be the model response
vector, and x be the vector of model parameters. An error norm for least squares is be given by:
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S  df .
(1)
A solution for x is found by least-squares inversion through finding
S
 0, for all x j . By
x j
minimizing (1) for the seismic, electromagnetic and potential field data sets, we can minimize the error
norm sequentially in a sequential inversion or we can combine the weighted error of seismic, EM and
potential fields in a joint inversion.
In the inversion of different geophysical data sets, there are various approaches to finding an
Earth model that fits different types of geophysical data. These approaches were given the name
cooperative inversion by Lines, et al. (1988). Cooperative inversion can subdivided into two types of
inversion: sequential inversion and joint inversion.
Sequential inversion deals with one type of data at a time in the sequence. The inversion is
applied to each data set individually, whether it be seismic, potential field, or electromagnetic, by
minimizing S in equation (1) for each type of data. The model computed from each inversion is used as an
initial guess for the inversion of the next data set. This sequence of inversion is continued for each data set
until all data sets are adequately described to within the error criteria.
Joint inversion essentially attempts to minimize all available geophysical data simultaneously.
For example, if we have seismic, EM and potential data, we may attempt to minimize an objective function
given by:
S
1

2
seismic
d seismic  f seismic
2
+
1

2
EMy
d EM  f EM
2
+
1

2
pot
d pot  f pot
2
.
(2)
The joint and sequential inversions were compared in Lines et al. (1988) for seismic and gravity
data, and there are advantages and disadvantages to both types of cooperative inversion methods.
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2.2 Seismic , electromagnetic, and potential field modeling
The estimation of earth properties is enhanced by the fitting of many types of geophysical data. The
ambiguity of geophysical inversion is reduced by the fitting many types of geophysical data. Inversions
involve adjusting earth model parameters in modeling equations so as to match model responses, f, to data,
d. The determination of model responses used in equation (1). is crucial to the inversion. We now briefly
describe these modeling methods.
In the seismic case (Shearer, 1999) the wave equation for the P-wave potential,  , is given by:
 2 
1  2
.
2
2
V P t
(3)
Where the P-wave velocity is:
VP 
k  (4 / 3)G

,
(4)
where k is bulk modulus of incompressibility, G is shear modulus (rigidity) and
 is rock density.
The wave equation for the S-wave potential,  , is given by:
1  2
  2
.
2
VS t
2
(5)
Where the S-wave velocity is:
VS 
G

.
(6)
For the electromagnetic case, Maxwell’s equations can be manipulated to derive the
electromagnetic wave equations. As outlined in Corson and Lorrain (1962), the electromagnetic wave
equation for the electric field vector E in zones of no enclosed charge distribution is given by:
 2E
E
.
 E   2  
t
t
2
(7)
Here  is the electric permittivity,  is the magnetic permeability and  is the electric conductivity. For
high frequencies, the first term dominates and equation (7) is basically a wave equation with the
electromagnetic wave velocity being
1

. For low frequencies, the second term dominates and equation
(7) becomes a diffusion equation. For mid-frequencies, equation (7) the equation of an attenuated wave has
two terms on the right hand side.
If E has a sinusoidal time variation, the electric field, E, has an exponential decay factor e
where the absorption coefficient is given by:
  f
,
 x
,
(8)
where f is the temporal frequency. Therefore, if we estimate absorption coefficients through EM
tomography, then we can determine the conductivity of the subsurface for a given frequency and a given
magnetic permeability. We will discuss geophysical inversion methods such as tomography that allow us to
estimate seismic P-wave and S-wave velocities and electrical conductivity (or resistivity). These physical
rock properties should allow us to characterize the subsurface lithology.
The purpose in this study is to deal with observations of both seismic and electromagnetic waves,
and to use these observations to estimate general Earth models whose parameters are both seismic
properties such as P-wave velocity, S-wave velocity, and density, and the electrical properties such as
electric permittivity (determined by dielectric constants),
magnetic permeability, and electrical
conductivity. The seismic and electromagnetic model parameters can be obtained by rock physics
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measurements. Knowledge of both sets of parameters can more uniquely determine the nature of fluidfilled reservoir rocks.
The modeling of gravity and magnetics observations basically deals with the modeling of
potential fields (Garland, 1965). In the case of gravity, the potential field, U, is given by:
U  G
 ( x, y , z )
V
r
dV ,
(9)
where G is the universal gravitational constant. Here  ( x, y , z ) is the density distribution over a volume V
at a distance r from an observation point. The measured gravitational acceleration, g, is given by the
gradient of U, the potential field:
g  U .
(10)
The measured value of g can be modeled by the appropriate values of rock density and distance
from the point of observation. Finding a match of the model response to the measured values of g is not a
sufficient condition for determining the correct density model. There is nonuniqueness in matching
models to potential field data. There are various density-depth models that will describe the observations.
Actually nonuniqueness is present for geophysical inversion in general. There is ambiguity in finding
suitable models for finite data sets. Nevertheless, the fitting of many geophysical data types by a
model will reduce the ambiguity (nonuniqueness) in our inversion. This is essentially the reason for
applying cooperative inversion to several data types.
2.3 Geophysical tomography
Tomography can be viewed as a particular type of model-based inversion.
An excellent
description of seismic tomography and EM tomography is given by Dines and Lytle (1979) The seismic
traveltime tomography basically uses interpreted (picked) arrival times from seismic recordings as the data
and minimizes the difference between the data traveltimes and modeled traveltimes obtained by ray tracing.
This minimization is achieved by adjusting the slowness (s=reciprocal velocity) of a set of velocity cells.
This minimization leads to a set of equations given by:
Dij s j  t i
(11)
Where
ti
is the traveltime of the ith ray, s j is the slowness of the jth cell and
D ij is the
distance of the ith ray in the jth cell as shown in Figure 1.
Figure 1. A subsurface region and its discretization into cells for a crosswell seismic tomography
survey. The ith ray travels a distance Dij in the jth cell. (After Dines & Lytle 1979).
In matrix-vector notation, the traveltime tomography equations can be written as:
Ds  t ,
Where D is the distance matrix, s is the slowness vector and t is the traveltime vector.
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(12 )
We can also perform seismic amplitude attenuation tomography in which we solve a systems of
equations for the attenuation of seismic waves. The algorithm of Quan and Harris (1997) relates seismic
attenuation coefficient,  0 to shifts in the centroid frequency, f centroid by an equation of the form:
D 0 
where
f centroid
,
 s2
(13)
 s2 is the variance of the spectrum at the source. The seismic tomography equations (12) and
(13) were used by Vasheghani and Lines (2012) to estimated seismic velocity and seismic-Q. Through use
of rock physics, seismic-Q was then related to fluid viscosity in order to create a viscosity tomogram.
In EM tomography, we analyze the attenuation of electromagnetic signal amplitudes. The decay
of the signal intensity of EM waves in a medium can be related through a series of equations given by:
D   .
(14)
Here D is the distance matrix, α is the EM attenuation rate, and γ is the integrated attenuation over
the entire path of the ith ray. The EM attenuation rate (absorption)  can be related to Earth’s
conductivity as shown in equation (8).
The inversion results and the geological information obtained from the seismic, EM and potential
field inversion can be summarized in Table 1 below.
Geophysical data type
Rock
physics
parameters
Multicomponent seismic data
VP , VS , seismic-Q
EM wave recording
Potential fields
magnetics data)
(gravity
and
inversion
Electrical resistivity
Electrical permittivity (dielectric
constant)
Density, magnetic permeability
Geological
obtained
Lithology
information
(from
VP / VS ),
porosity,
fluid
viscosity,
formation depths, density
Lithology, fluid content
Density,
rock
formation depths
magnetism,
Table 1: An overview of geophysical joint inversion for different data types and inversion
parameters with the geological information obtained.
2.4 Microseismic analysis
While many seismic investigations have focussed on the use of active seismic sources, there is a
great interest in the use of adapting earthquake seismology to the micro-level and utilizing microseismic
investigation to characterize induced fractures.
The characterization of induced fractures with
microseismic data can locate miniature earthquakes due to fracking by using hypocentre location methods.
A comparison of the geomechanical analysis of microseismic events with statistical methods is being
carried out at the present time. Mr. Glen Young is doing an M.Sc. with CHORUS that focuses on analysing
fractures in a Horn River Basin field of North Eastern British Columbia. This analysis should help to
optimize the production of tight gas in the Horn River Basin.
3.0 Integration of geophysical inversions with petrophysical data and rock cores
Thus far, we have examined geophysical data and the geological information obtained from joint
inversions of these data. Such data generally sample large volumes of the petroleum reservoir at scales that
are limited in resolution by the wavelengths contained in the data. There is some reservoir information that
covers smaller volumes of the reservoir but that has higher resolution. This includes petrophysical data and
rock core from the well bore. The idea of using a common model to describe rock physics and
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petrophysics allows us to provide corrections to well log data. This utilizes the idea of joint inversion to
enhance petrophysical data and the Earth models derived from them.
4. Rock physics for unconventional reservoirs
In recent years, unconventional reservoirs have had a great impact on North American oil and gas
production. Unconventional reservoirs include heavy oil and tight oil and gas reservoirs. In heavy oil
reservoirs, the viscosity of cold heavy oil is very high for temperatures less than 60 degrees Celsius
requiring that Gassmann’s theory be replaced by rock physics theory that describes the enclosed viscous
fluid to be more similar to a glass rather than a conventional fluid.
In tight oil and gas reservoirs, the induced fracturing in the reservoir is characterized by various
geophysical measurements. In order to determine what part of the reservoir to fracture, it is important to
characterize the brittleness and ductility of the reservoir. This can be accomplished from seismic data by
estimation of two elastic constants: Young’s modulus, Y, and Poisson’s ratio,  . Both of these elastic
constants can be estimated from the P-wave velocity ,
VP and S-wave velocity, VS .
Young’s modulus is given by:
 3VP2  4VS2 

Y  V  2
2 
V

V
S
 P

2
S
(15)
Poisson’s ratio is given by:

VP2  2VS2
2(VP2  VS2 )
.
(16)
A graph of Poisson’s ratio versus Young’s modulus and its relationship to brittleness and ductility
is given by Figure 2 from Rickman et al. (2008). Hence if we estimate P-wave and S-wave velocity, we
can derive a map of Young’s modulus and Poisson’s ratio to determine what part of the reservoir might be
best suited for induced fractures.
One measure of brittleness is given by the ratio of Young’s modulus to Poisson’s ratio, or
brittleness factor 
Y

.
(17)
While this measure of brittleness involves the two elastic parameters in (17), it could be expressed
in terms of Lamé parameters, or bulk modulus and shear modulus, or any combination of two elastic
parameters as summarized in Sheriff and Geldart (1995).
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Figure 2. The relationship of ductility and brittleness of a medium are given by the size of Young’s
modulus and Poisson’s ratio. This figure is from Rickman et al. (2008).
The estimation of
VP and VS , or their ratio, can be done by at least two methods. One of these
methods uses seismic traveltime interpretation from vertical and horizontal components in multicomponent
recording. By timing the vertical component (primarily PP energy) and the radial component (primarily PS
energy), we can apply equation (18) to determine the VP / V S ratio from the PP traveltime, t PP , and PS
traveltime,
t PS .
VP 2t PS  t PP

VS
t PP
The
(18)
VP / VS ratio or Poisson’s ratio can also be determined by the use of amplitude versus offset
(AVO) analysis. The maps of Poisson Ratio and Young’s Modulus variations are used to determine
brittleness and ductility, as shown in the example from Gray et al. (2010), as shown in Figure 3.
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Figure 3 shows a color background of Young’s Modulus on a 3-D seismic display from Gray et al. (2010).
This project will focus on determining and accurately extrapolating brittleness throughout a formation by
combining geological, geochemical and geophysical methods. We will be comparing and contrasting a
number of different seismic and sonic log-derived, elastic property based rock physics brittleness indices
with an elemental and mineralogical brittleness assessment. To effectively correlate and extrapolate the
elemental and mineralogical data, a lithofacies/petrofacies based model of the formation of interest will be
developed. Elemental and mineralogical data will be assigned to facies based on core samples, cuttings,
thin sections and well logs. Facies will be extrapolated and correlated stratigraphically based on well logs
and seismic data. A facies model will also be adopted for sonic log based rock physics brittleness
assessments. The model will then be upscaled and tied into seismic data and the brittleness assessments
based on seismic data. All of the facies-based brittleness interpretations will be assessed geologically to
understand factors influencing the various interpretations. Issues with upscaling and downscaling will be
addressed, and recommendations that may improve the brittleness formulae or their applications will be
made. A simplified pictorial summary of the shale gas reservoir characterization is given in Figure 4
below.
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Figure 4. A simplified pictorial summary of shale gas reservoir characterization
5. Integration Methods
Much of reservoir characterization involves the integration of different data (geological,
geophysical, petrophysical, and reservoir production) in order to obtain the reservoir model that provides
the best model fit to all available observations. There are at least three closely related methods of
integrating this information - joint inversion, geostatistics and neural networks.
5.1 Improvement of Reservoir Simulator Models
A major goal of EOR projects is to understand the reservoir through reservoir production
simulation. In order to do this we need excellent estimates of reservoir parameters that are consistent with
geomodels. Reservoir simulation software conservation of mass and Darcy’s Law for fluid flow which for
single phase flowis given by:
q

Ap

,
(19)
Where q is the volume of fluid flow,  is the permeability,  is the fluid viscosity, A is the cross-sectional
area and p is the pressure.
Simulator parameters include porosity, permeability, fluid density, fluid viscosity, fluid
saturation (gas, oil and water), pressure, temperature, lithology and formation depths. The geomodel
parameters deduced from geological and geophysical data include seismic P-wave and S-wave velocities,
seismic anisotropy,, seismic-Q, rock density, fracture density, electrical conductivity, cores and well logs
(dipole sonics, gamma ray logs, density logs, temperature logs, and geochemistry from well samples. The
relationship between reservoir simulator parameters and geomodel information is summarized in Table 2.
The linkage between these two sets of parameters is established by rock physics. In many cases, the
rock physics for conventional reservoirs may have to be revised to adequately describe
unconventional reservoirs.
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Reservoir Simulator Parameters
Porosity
Permeability
Fluid viscosity and density
Fluid saturation (oil, gas, water)
Pressure
Temperature
Lithology
Depth to formations
Geomodel Information
Density, P-wave velocity, Vshale, resistivity,
Fracture density (from logs or S-wave splitting)
Effective porosity from density or sonic logs
Seismic-Q, S-wave velocity, geochemistry , density
logs.
Conductivity from EM tomography or resistivity
logs, P-wave velocity (for gas)
P-wave velocity and Bulk modulus
Time-lapse seismic velocities,
Temperature logs
Cores, well logs, VP/VS from dipole sonics and
multicomponent seismic data, Vshale and synthetic
cores
Seismic traveltimes and velocities, cores and well
logs
Table 2: The relationship between the geomodel parameters from joint inversion and the reservoir
simulator parameters.
For the history matching of tight oil reservoirs, simulators will need to model induced fractures
created by the fracking process. CHORUS will integrate all of the listed new science methods and build a
fracking simulator model to improve the industry software. Our new science will develop industry plug-in
software for simulation. and will be done on a field data case.
6. Real Field Data Cases
CHORUS has done extensive reservoir characterization of several unconventional oil fields
during the past 15 years. Most of the studied reservoirs have been heavy-oil fields in Western Canada. In
the future, we intend to apply integrated reservoir methods to tight oil/gas fields. While the integrated
inversion methods are similar, the rock physics will require departure from conventional thinking in both
cases. Our most recent studies have been in two fields: the heavy-oil field at Long Lake, Alberta, a tight
gas field in the Horn River Basin, British Columbia.
7.0 Research Plan: Goals and Deliverables 2015-2018
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Timelines
Research Goals and Deliverables
Summer 2015
M.Sc thesis on microseismic monitoring of fractures in tight gas
reservoirs (G.Young)
Ph.D. thesis on modeling seismic reflections from fractures. (X. Cui)
Background research on shale gas reservoir characterization, seismic
applications, and rock physics.
Fall 2015
Create NSERC CRD proposal on EM-seismic joint inversion project.
Preliminary seismic, petrophysical, core and rock physics analysis on
tight gas field.
Ph.D. thesis on geostatistical analysis of dipole sonic logs from a heavy
oil field (L. Ibna-Hamid).
Winter 2016
Ph.D. thesis on synthetic core in heavy oil field (M. Alam).
Mineralogical analysis of core from tight gas field.
Spring 2016
Ph.D. thesis on time-lapse seismic analysis of a heavy oil field (A.
Javanbakhti).
Summer 2016
Extraction of elastic properties from seismic data, sonic logs and rock
physics in tight gas field.
Fall 2016
Complete brittleness calculations for tight gas field.
Winter/Spring/Summer
2017
Project review for tight gas field reservoir characterization.
Fall 2017
M.Sc. thesis defence on reservoir characterization of tight gas field (B.
Cann).
Winter 2018
Final report on reservoir characterization of tight gas oil field.
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