technical article
first break volume 33, April 2015
Potential applications of time-lapse CSEM to
reservoir monitoring
O. Salako1, 2, C. MacBeth1* and L. MacGregor3
Abstract
Monitoring of changes in brine chemistry (salinity and temperature), during water-flooding is important for injector optimisation, understanding efficiency, detecting early water breakthrough, locating bypassed hydrocarbons or detecting scaling
in the heterogeneous reservoir. It is already known that water injection into the oil-leg of a hydrocarbon reservoir can be
monitored by both seismic acquisition and also CSEM methods. The problem of an interfering pressure signal for seismic
impacts quantitative evaluation for time-lapse analysis, while with EM there are the counterbalancing effects of salinity
and temperature. CSEM becomes favoured when the pressure effects on seismic are dominant, or heavy oil is present with
similar acoustic properties as the formation and injected waters. The quality of measurement for both methods is influenced
by reservoir facies variations, acquisition repeatability and overburden heterogeneity. Time-lapse seismic is unlikely to detect
brine distributions injected into the water-leg or aquifer, although it may detect associated pressure up effects. However,
our calculations show that it may be possible for time-lapse CSEM to distinguish the inter-mixing of different brines in the
subsurface hydrocarbon reservoir. Specifically, low salinity injection or injection into a highly saline formation can clearly
be detected with this technique.
Introduction
Electromagnetic (EM) methods in industry geophysics are
generally employed to measure the conductivity (or resistivity) of fluid-saturated rocks, which may aid in discriminating
highly resistive hydrocarbon-bearing rocks from those with
relatively more conductive saline formation water. Such
resistivity measurement forms the basis for one of the oldest
(1927) wireline logs to be run for the purposes of reservoir
evaluation. In modern quantitative petrophysical analysis,
direct resistivity from deep and shallow laterologs or induction logs are routinely interpreted together with porosity
from density or neutron density logs and brine resistivity
calibrated in the laboratory, to determine water saturation
and hence recognise hydrocarbon-bearing zones. EM measurement techniques have also evolved to occupy not just the
log domain but other wider aspects of inter-well imaging.
A recent example is high resolution cross-well EM tomography methods that can track details of a water flood in a
hydrocarbon reservoir between wells 500 m to 1 km apart
(for example, Al Ali et al., 2009). Another example is surfacebased EM surveying, and in particular the marine controlled
source EM (CSEM) method which has become popular
over the past 15 years since first publication of the proof of
concept for imaging hydrocarbon reservoirs (Ellingsrud et
al., 2002).
Here, a source in the form of an electrical dipole transmitter is towed close (approximately 25 m) to the seafloor
and used to induce horizontal and vertical loop currents in
the subsurface that are then measured on a receiver sensitive
to (typically) the electrical and magnetic components. The
received signal is a function of source-receiver geometry
(offsets usually in the range of a few hundred metres to 10 or
15 km), seafloor resistivity and transmitted frequency, which
is typically in the frequency range 0.1 to 10 Hz (MacGregor
and Tomlinson, 2014). Although superficially similar to seismic methods because of the multi-component and frequency
dependent aspect of the data, the governing physics is very
different and care must be exercised when drawing parallels
with seismic data.
A natural and obvious drawback of CSEM, as a potential
field method, when used for hydrocarbon exploration or as
a pre-drill appraisal tool, is the poor structural constraint
afforded by the method. As the signal evolves spatially
according to a diffusion equation rather than the familiar
propagative seismic wave equation, vertical detail is much
more poorly resolved than in seismic data. These limitations
require that CSEM data should be interpreted jointly with
seismic data (e.g. MacGregor et al., 2012).
Given the limited thickness of most hydrocarbon reservoirs, producing units or intervals (10 to 50 m), the most that
Institute of Petroleum Engineering, Heriot Watt University, Edinburgh EH14 4AS, UK.
Department of Geology, Osun State University, P.M.B. 4494, Osogbo, Nigeria.
3
RSI, 2600 South Gessner, Suite 650, Houston, TX 77063 USA.
*
[email protected]
1
2
© 2015 EAGE www.firstbreak.org
35
technical article
can therefore be expected of CSEM is an estimate of bulk
electrical resistivity for the reservoir at each location – but as
we shall see, that particular measure may be very useful in
reservoir management and evaluation.
More recently, there has been the suggestion that timelapse EM imaging could provide information to supplement
4D seismic techniques when monitoring hydrocarbon reservoirs during production and recovery (Orange et al., 2009;
Constable, 2010; Andreis and MacGregor, 2011). This new
application to producing reservoirs at a later stage in their
life cycle benefits in particular from pre-existing knowledge
of the reservoir depth, structure and thickness, and hence
well developed geological and simulation models that provide
constraints to negate the accepted weaknesses of the EM
approach. Indeed, one of the obvious benefits of the timelapse EM imaging most commonly cited in the literature, is
the ability to sense changes in water saturation without the
interference of the pressure effects to which the seismic data is
variously susceptible. Thus, it is believed that EM may be used
to help track injected water during the improved oil recovery
stage of a reservoir’s life. Particular attention is paid here to
lateral drive, with less attention on basal aquifer drive. Clearly,
the method is of value in geographical locations where the
injected and displaced fluids have similar seismic properties,
the monitoring of production mechanisms in which pressure
obfuscates water saturation, or quantifying water distribution
in the calculation of remaining oil. There have been several
modelling studies in the past that investigate this topic and
assess the likely magnitude of the EM signal. These studies
have considered representations of the waterflood as an
idealised piston-like lateral displacement mechanism (Lien
and Manneseth 2008; Orange et al., 2009) to modelling of the
response with a more realistic fluid flow simulator (Shahin et
al., 2010; Liang et al., 2011). These studies conclude that the
absolute magnitude of the EM signal appears sufficient to be
detectable with current acquisition and hardware.
Given that one of the largest benefits of EM monitoring is
surveillance of a waterflood, one important area for potential
technology development is the introduction of more realism
into the representation of the waterflood process and development of an understanding of the corrersponding reservoir
recovery processes. This will enable us to better characterize
the EM signal with a view to assessing its ultimate value for
reservoir management. In this context, one important difference between seismic and EM is that the latter is strongly
sensitive to the physical and chemical properties of the aqueous
fluids. Thus, in this study we highlight the role of EM in tracking specific brine distributions. To take this further we must
firstly describe the injection of water for improved oil recovery.
Water injection
Basic principles
Water injection is the oldest and most commonly used secondary oil recovery method, whether onshore or offshore,
36
first break volume 33, April 2015
as it is relatively inexpensive and in most cases appropriate
sources of water (for example, seawater) are readily available. Many fields are on water injection from close to the
start of production, and this continues for most of the production lifetime (for example, the Girassol field – Bouchet
et al., 2004). Such injection helps to maintain reservoir
pressure near its initial level, and displaces (‘sweeps’) oil to
the producing wells via voidage replacement. This process
enhances the recovery factor beyond the forces of natural
drive and maintains the production rate for a longer period,
despite the need for additional water handling at the producers. Injection is preferably into the water column/aquifer to
avoid bypassing down-dip oil accumulations. Understanding
the aquifers and the bottom- or edge-drive that injectors
provide is critical to such reservoir operations. However if
permeability in the water leg is significantly reduced due
to compaction or diagenesis, it may be necessary to supply
higher energy by injecting directly into the oil column at the
beginning or later in the field life. For this, injection is in low
permeable zones at the flanks of the main reservoir to avoid
early water breakthrough and promote a reasonable sweep
(as, for example, in the Nelson field – MacBeth et al., 2005).
Water injection is effective as the mobility contrast between
oil and water is favourable for a continuous sweep, unlike
gas injection. However, the efficiency of the displacement
depends on many factors, such as viscosity and rock characteristics, pattern of injectors and producers, and hence this
process has a high degree of uncertainty.
Thus, over the several decades it takes to fully complete a
water injection programme, effective reservoir management
requires close monitoring of the evolution of the injected
water. This is necessary in order to determine its efficiency at
restoring pressure or control the relative volumes of injected
water into different reservoir intervals, to ensure the process
is efficient and that excessive water is not produced.
Monitoring needs
Production technology aims to monitor and control the
movement of the injected water. In particular, steps to
modify the original design of optimally placed injectors
and producers, or injection intervals, are required if adverse
conditions prevail. Adverse conditions include early water
breakthrough, where water channels along highly permeable
pathways to the producer, or a heterogeneous and inefficient
displacement process with areas of bypassed oil. Assessment
of the waterflood can be achieved by monitoring at the wells.
Waterflood monitoring includes direct observation of the
volume of produced oil and water, bottomhole pressures and
gas-oil ratio, as a basis of evaluating the voidage replacement
rates, water injection pattern efficiencies and water injection rates. As injected and formation (aquifer and connate
water) water have different chemical signatures, measuring
the brine chemistry of the waters produced can identify their
individual origin (and even help history to match the simulation model – Arnold et al., 2012). Reservoir production
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first break volume 33, April 2015
engineers are particularly interested in identifying zones for
infill drilling, and this is clearly where techniques that can
image the inter-well space and monitor water sweep progression can add benefit – provided they are cost-effective. Any
prospective monitoring technique therefore requires a basic
fundamental ability to discriminate between oil-filled (plus
connate water) zones of the reservoir rock and those that
are predominantly water-filled (injected water plus connate
water and residual oil). Also of fundamental importance is
the ability to track the separate progress of injected and formation waters within the formation after injection into the
water leg or aquifer. This is where 4D seismic or EM surveying can help, as in many cases injected and formation water
have a favourable bulk seismic or resistivity property contrast with the ‘oil’ zone which has the smaller water content.
For 4D seismic data, in particular, there are many
good examples of direct imaging of waterflood direction,
shape and distribution (for example, Røste et al., 2009).
However, an important disadvantage of the seismic is the
lack of acoustic contrast between some heavier oils and the
injected/formation waters (see, for example, the measurements of Batzle et al., 2006). Additionally, there is probably
insufficient seismic property contrast to distinguish injected
and formation brines. Both of these disadvantages point to
techniques such as EM imaging as being of value, as the
electrical resistivity they sense is dependent not only on the
amount of brine, but also the chemical state of the brine. We
consider the latter point in further detail in the next section.
technical article
the injected water, followed by aquifer water. The amount
of produced connate water depends on whether the water is
injected into the aquifer or the oil leg. Direct injection into
the oil leg tends to yield a small connate water production
to be followed by the injected water. For increased reservoir
heterogeneity (especially vertical), then the time sequence of
the different breakthroughs and the appearance of the different brines will be more indistinct and complex. Aquifer water
may be produced early by coning, whereas injected water
breaks through at the bottom of the well.
Of concern in this process of complex fluid interaction
is the region or time period over which the injected and
formation brines get a chance to interact. Of particular
interest in oil-field chemistry is the mixing of incompatible
brines with differing ionic composition. Specifically, seawater
rich in sulphate ions can mix with formation water rich in
barium (also strontium and calcium) ions to create a hard
crystalline mineral scale deposit within the formation as
well as at the wellbore. The exact location of the mixing
zones is defined by the reservoir heterogeneity and injector
placement. These scale deposits mainly have an effect at the
well to restrict flow and limit performance at the production
wellbore and production facilities (Sorbie and MacKay,
2000). Scale deposits in the deep reservoir do not have an
adverse effect on porosity or permeability, and may actually prevent near wellbore scale build-up. Scale deposition
is more likely to occur when seawater is injected into the
aquifer. It is therefore important to monitor the location of
the individual brines.
Brine distribution tracking
Brine mixing in the reservoir
Whilst the physical displacement mechanisms associated
with water injection are well known, less well known is
how the injected water interacts with the subsurface aqueous fluids. Brine chemistry may impact souring, scaling and
hence oil recovery. The flow of injected water through the
reservoir and complex interaction with the reservoir fluids
is a strong function of reservoir heterogeneity. Within the
reservoir, the injected water interacts with the formation
waters (connate and aquifer) both physically, to displace
the mobile oil phase, but also chemically at distinct mixing
zones. The composition of the formation brines depends on
their source, history, and present-day thermodynamic conditions. Their distribution in turn depends on petrophysical
conditions of the rocks, and physical and chemical properties
of the fluids themselves. Injected water displaces the oil and
mixes with the connate water in the oil zone, transition zone,
or directly with the aquifer water – depending on where the
injected well is completed. Numerical studies have shown
that co-production of these different brine mixtures or
individual brines is normally expected, and indeed complex
temporal sequences of water production can occur (Sorbie
and Mackay, 2000). Connate water is generally produced
first at the producers by connate water banking ahead of
© 2015 EAGE www.firstbreak.org
Brine resistivity as a function of salinity and temperature
It is well known from published literature that the acoustic
density and bulk modulus of reservoir brines (for example,
Batzle and Wang, 1992) or seawater (for example, Bertrand
and MacBeth, 2005) is mainly a function of salinity (that is,
the NaCl equivalent of the ionic composition) and temperature, but less dependent on the variety of other minor ionic
components. The same dependence is true of the electric
resistivity (Rw) of brines, a fact identified from the early
petrophysical work by Archie. A convenient equation that
captures the general dependency of brine resistivity on salinity and temperature is given by Crain (1986)
(1)
where Τ and Sal are the temperature in degrees Celsius and
salinity in parts per million equivalent of sodium-chloride
solution respectively. In addition, we know that the salinity
and temperature of the brines and their mixtures vary for
hydrocarbon reservoirs in different geological and hence
geographical environments (see Table 1), and they also
depend on the time and rate of production/injection. Thus,
for a static reservoir prior to production, formation brine
(i.e., aquifer or movable connate water in the transition zone
of the reservoir, for which chemical differences are often
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first break volume 33, April 2015
small) varies from almost pure water (1000 ppm) to very
saline (300,000 ppm) (Rider and Kennedy, 2013). As a rule,
salinity generally increases with depth in the reservoir, and if
near-bedded salt (such as in the Gulf of Mexico) can exceed
100,000 to 300,000 ppm. If there is no salt present, salinity
will generally not exceed 30,000 ppm. The temperature of a
hydrocarbon reservoir depends on the proximity to geothermal sources, but roughly varies between 75 and 100oC (100
and 150oF) – excluding high pressure – high temperature
reservoirs.
The conditions of the water injected into the hydrocarbon
reservoir are determined mainly by its origin from a variety of
available sources. The nature of this water depends on whether
the reservoir is onshore or offshore, the reservoir type, and
geographical location. For example, in offshore production
(or near onshore) it is very common to use seawater, filtered
and collected appropriately to avoid algae, corrosive oxygen
content, and other undesirable chemicals. This typically has a
salinity of around 30,000 ppm and temperature of 30oC (86oF)
Fluid type/Cases
Subsurface
Sandstone
aquifer water
2,3
Sea water
Injected water
1,7,9
Example of
oilfield/province
Temperature
(°C)
Salinity
(ppm of NaCl)
Resistivity, Rw,
(Ω-m)
Saudi Arabia
(Wasia and Biyadh)
-
5,000 - 18,000
(av. of 10,000)
0.57
Niger Delta/Nigeria
24
30,000 - 35,000
0.19 - 0.22
Alaskian (Arctic)
5 - 17
30,000 - 35,000
0.22 - 0.36
UKCS
5 - 17
30,000 - 35,000
0.22 - 0.36
15 - 20
(summer)
5,000 - 14,000
0.47 - 1.31
500 - 1,500
(desalinized)
3.05 - 8.03
EOR in an oil-wet reservoir.
Not < 5% of the salinity of
formation water to avoid
clay swelling.
-
Values are function of the in
situ conditions and earlier
injected water
River water
10
Low salinity
water
4
Endicott
Produced water
18.7
(desalinized)
Comment
Rw calculated at 24°C
Ave. summer; 15°C;
30,000ppm; 0.27Ω-m
-
-
-
Simpson sd, Oklahoma
-
298,497
0.000
89
200,000
0.018
Temperature calculated
Burgan, Kuwait
-
154,388
0.053
Rw calculated at 24°C
US average
-
94,000
0.08
1
Saudi Arabia
(Arab - D)
2,3
1
1
Formation water
(drawn from the top layer of the seawater column). For onshore
reservoirs, river water is utilized and after a similar treatment
may be typically 10,000 ppm and 20oC (68oF). Interestingly,
by contrast, in some onshore areas of Saudi Arabia, subsurface
aquifer water with an average salinity of 10,000 ppm is
injected into reservoirs with a very high salinity formation
water 200,000 ppm on average (Rafle and Youngblood, 1987).
Other options for injected water are to extract from the same
or nearby water-bearing formations other than the oil reservoir.
This water is purer and less saline than seawater, being around
3000 ppm and 15oC (59oF). It is also possible to intentionally
inject low salinity water of between 500 ppm and 1500 ppm
for enhanced oil recovery. A final option, aimed at reducing
potential formation damage due to incompatible fluids, is to
inject produced water. For this, work must be done to remove
hydrocarbon and solid contaminants before reinjection, and
this is often quite costly. This volume is never sufficient to
replace all produced volumes and additional water from other
sources is required to make up the volumes.
Woodbine, E, Texas
-
68,964
0.1
100
15,000
0.16
Niger Delta/Nigeria
54.4
20,000
0.19
UKCS
57.7
18,000
1
4,5
Endicott/Alaska
6
7
California petroleum
basins
-
Daquing Field, China
45
Laugunillas, Venezuela
-
8
6
1
0.2
30,000 - 35,000
0.19 - 0.22
5,000 - 7,000
0.55 - 0.74
Rw calculated at 24°C
CASE X
7,548
0.77
CASE Y
Rw calculated at 24°C
CASE Z
Table 1 Resistivity properties for reservoir formation or injected fluids used in secondary and tertiary recovery from a range of geographical locations.
Superscripts refer to the following references: 1Rider & Kennedy 2013; 2Rafle and Youngblood, 1987; 3Youngblood, 1980; 4McGuire et al., 2005; 5State of Alaska,
2011; 6Shehata et al., 2012; 7Martin and MacDonald, 2010; 8Batzle and Wang, 1992; 9Constable, 2013; 10River and Lake Swimming Association, 2013. Examples
given here are ranked according to Rw values that are calculated, when appropriate, using Crain (1986).
38
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first break volume 33, April 2015
technical article
Figure 1 (a) Brine resistivity as a function of salinity and temperature. This is compared against the
acoustic properties of bulk density (b) and bulk
modulus (c).
Table 1 details a range of salinity and temperature for
formation and injected waters, in a selection of hydrocarbon
reservoirs from around the world. In some notable cases the
contrast in salinity between formation and injected water
is quite large. This is particularly true of the highly saline
formation water of the Arab-D reservoir and much lower
salinity injected water from massive underlying aquifers cited
by Rafie and Youngblood (1987) (which we define as Case X
in our study below), or the low salinity water injected into the
Endicott field described by McGuire et al. (2005), for improved
oil recovery (defined as Case Y). The latter is important, as
it is now being currently considered as an effective tertiary
oil recovery procedure. In the reservoir, large temperature
contrasts between the brines may also be present close to the
injector, particularly in environments such as the North Sea.
Figure 1 shows how the seismic and resistivity properties
respond to salinity and temperature variations. Both bulk
density and bulk modulus vary in an approximately linear
fashion with salinity and temperature. Temperature variations are predicted to have a small effect on the seismic data,
with less than 5% change in bulk modulus occuring over a
span of 100oC. Salinity variations generate between 10 and
14% change in bulk modulus for every 50,000 ppm over a
similar temperature span, suggesting the possibility that in
the extreme cases mentioned above, injected and formation
waters may possibly be identified separately with high-quality
data. Interestingly, although there is no literature available
on the separate seismic detection of individual brines within
the hydrocarbon reservoir itself, many seismic examples do
exist from seismic acquisition in deep seawater columns.
For example, seismic reflections have been observed from
the mingling of flows with differing salinity and used to
image the thermohaline structure of oceans (Holbrook et al.,
2003), and have in the past been associated with difficulties
© 2015 EAGE www.firstbreak.org
in seismic imaging for reservoir exploration (MacKay et al.,
2003). Resistivity on the other hand has an obvious stronger
response to both salinity and temperature, and must be plotted on a logarithmic scale in Figure 1. Interestingly, however,
for the North Sea example in Table 1 (which we refer to as
Case Z), injected water of 30,000 ppm and (15oC) 59oF has
a resistivity of 0.27 Ωm and formation water of 18,000 ppm
and (58oC) 136oF has a resistivity of 0.20 Ωm (Figure 2).
This difference is not as high as one might expect, and is due
to the effect of salinity variation on resistivity opposing that
of temperature. This interaction suggests that the situation
best suited for CSEM monitoring application should have
variations of either salinity or temperature, but not necessarily both. These resistivity changes should be compared with
those for bulk modulus (2.42 GPa and 2.58 GPa) and bulk
density (1.03 g/cm3 and 1.01 g/cm3), which are much weaker
(reference reservoir pressure is 20 MPa). For comparison, the
predicted properties for Cases X, Y and Z are compared in
Figure 2.
Finally, it should be noted that there is a lack of data
examples in the seismic or CSEM literature desmonstrating
the identification of individual brines in a hydrocarbon reservoir. This point is considered later, where modelling based
on a UKCS field assesses identification under the subsurface
conditions of a producing hydrocarbon reservoir.
Example of expected variation during injection
To understand the evolution of the brines over the production time-scale, their effect on the distribution of salinity and
temperature through the reservoir, and their relative time
constants and resultant spatial patterns, we take an example of a UKCS clastic field. For this purpose, a well history
matched simulation model is used for a segment of the field.
3D numerical fluid-flow simulation is performed to obtain
39
technical article
the water saturation, pressure, salinity and temperature distributions due to production of oil and water, and injection
of seawater from vertical wells. In the well example we select,
seawater is injected into the oil-leg, therefore formation brine
is produced first, and over time the seawater fraction gradually increases. In this simulation we track the different contributions from the injected and formation water (however, no
distinction is made between aquifer and connate water) by
calculating the salinity and temperature values for the water
component saturating each cell of the model. The modelling
is performed for the pre-production (baseline) reservoir conditions and then for up to several years of production and
recovery via a combination of aquifer support and a constant
rate of water injection into the oil leg. The salinity and temperature of the brines are as given for the North Sea example
(case Z) in the previous section, this yielding formation brine
with a resistivity of 0.20 Ωm in the pre-production reservoir
state and injected water with 0.27 Ωm.
Figure 3 shows the evolution of the brine resistivity with
production time for all cells in the simulation model, together
with their corresponding salinity and temperature for reference. Initially, the cells have a common resistivity, and a single
peak exists at the salinity and temperature of the formation
water. When injection starts, another peak appears at the
higher resistivity of the injected water. Over time, temperature
first break volume 33, April 2015
and salinity gradients are established as these diffusive effects
equilibrate. The injected water is heated up by the formation,
and the higher salinity dilutes out into the larger formation
volume. Temperature has the faster speed of equilibration,
whilst salinity still remains fairly localised. As a consequence,
the injected water heats up quickly and resistivity drops in
the injected water – this can be observed in Figure 3 as a red/
orange/yellow tail emanating from the initial point for the
injected water. Similarly, a smaller volume of formation water
in contact with the injected water slightly cools, thus giving
rise to the shorter purple upward trend from the formation
water point. There is also a drop of resistivity in the formation water due to the impact of the salinity diffusion. The
equilibrium condition for the temperature and salinity profiles
is defined by the injection and production rates, rock and fluid
properties, and the initial conditions of the reservoir. However,
equilibration is complicated by the physical movement of the
brines in the subsurface.
Both equilibration effects are faster, however, than the
physical progression of the water flood. Cells with a different
combination of salinity and temperature from the initial state
may be considered to be in mixed brine conditions. Figure 4
shows the changes in temperature and saturation predicted
from one of the injectors in our UKCS field. Changes are
a complicated interaction of both the temperature and
Figure 2 (a) Brine resistivity as a function of salinity and temperature. This is compared against the
bulk density (b) and bulk modulus (c) for cases X,
Y and Z described in the text. The brine resitivity is
calculated using Crain (1986) while the brine bulk
density and bulk modulus are calculated using
Han and Batzle (2000) at a reference pressure of
20 MPa.
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technical article
Figure 3 Cross-plots of brine resistivity for each
cell of our North Sea simulation model against
the corresponding temperature, colour-coded by
salinity. These plots are drawn as a function of
injection time. The initial temperature and salinity
of the injected and formation water are labelled
as IW and FW respectively. Production occurs after
August 1998, and injection starts at the same time
as production.
Figure 4 The evolution of: (a) water saturation;
(b) salinity; (c) temperature; and (d) resistivity
outwards from a seawater injector over one year
(between Aug 98 and Aug 99 in Figure 3) of
production and injection in our example North
Sea clastic reservoir (Case Z). The composite brine
resistivity changes at each location around the well
are shown in (d). Changes for each property are
displayed as percentages.
salinity variations. To understand these effects further when
combined with the impact of the rock framework, the spatial
distribution of the resistivity must be modelled. This is the
subject of the next section.
Modelled response for a producing reservoir
To better understand how the fluid properties impact the
observed CSEM and seismic responses recorded at the surface,
© 2015 EAGE www.firstbreak.org
full modelling and rock physics calculation is performed for
our field example. The reservoir intervals of interest are buried
at between 1990 m and 2050 m below the seafloor, and are
between 25 and 50 m thick. Porosity ranges from 25 to 30%,
and net-to-gross between 0 and 1 (see Figure 5).
Before modelling can proceed, the resistivity of the fluidsaturated rock of variable porosity and shale content must be
calculated. This will modify the fluid detectability, and thus
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first break volume 33, April 2015
Figure 5 Resistivity log for our North Sea field, from which lithology and fluid conditions are calibrated for our modelling exercise. The mean resistivity for the
water-saturated sand is 2.5 Ωm, the shale is 2.8 Ωm, and an oil-saturated sand is 60 Ωm. The blue horizontal lines denote the reservoir interval considered in
the fluid flow simulation in this work.
lower the chance of a successful direct interpretation of the
fluid response.
The composite resistivity of rock and fluids
In seismic data, to understand the impact of fluids and
the porous rock matrix, the Gassmann (1951) equation is
normally used. In the case of electrical resistivity, the equivalent equation originates from Archie (1942), where total formation resistivity Rt is determined from the fluid resistivity Rw,
(2)
where account is taken of the brine saturation Sw present
in the pore space. The saturation exponent, n, is usually
assumed to be 2. The formation resistivity factor F is a function of sediment texture and is independent of Rw
(3)
42
where α is the formation tortuosity (generally regarded as
unity), Φ is the total fractional rock porosity and m the
cementation factor typically taken as 2 in clastic settings. The
above equations were originally fit by a range of porosities
for laboratory data for clean consolidated brine-saturated
sandstones, but despite these restrictive origins they are still
found to be approximately true for a wide range of cases in
log analysis (Rider and Kennedy, 2013), with only a slight
variation in both a, m and n from the ‘typical’ values above.
With a fairly uniform formation rock, EM methods can
monitor total resistivity and hence changes in water content
(Sw). Although it is believed that resistivity is a poor indicator of lithology, some rocks, such as cemented sands, coals,
anhydrites and limestones, have a high resistivity, and hence
may affect the ability to interpret fluids. Whilst the basic
exponents m and n in the above relation do not vary significantly with the formation, a strong contribution is played
by porosity, and thus the Rw signature as discussed in the
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first break volume 33, April 2015
previous section will be amplified, or not, by the effects of the
formation. Thus, CSEM does not escape the need for us to
know porosity in the formation (in a similar way to seismic
data). Indeed, high resistivity may indicate low porosity and
not hydrocarbon. An important input to this interpretation is
thus a calibration from the resistivity log data.
For our reservoir example, we must also generalise this
equation to the specific case of shaley sand for which the
laminated shale model holds. Treatment of electrical resistivity in shaley sand occupies a considerable body of research
(Worthington 1985 cites and critically discusses more than
models) and is complicated by the ionic distribution in the
bounding water layers. We take advantage of the fact that the
horizontal electrical dipole source and receiver configuration
we are considering is sensitive only to the vertical resistivity
within the reservoir interval (see, for example, Key, 2009;
Brown et al., 2012). Thus, in our case an arithmetic average
which approximates vertical resistivity can be used, as the
(thin) reservoir acts as a serial resistor network. Thus, the
total resistivity in the reservoir is (Tsili and Sheng, 2001)
(4)
where Rsh is the resistivity of brine-saturated shale (usually
lower than that of the oil-filled sands), and NTG is the netto-gross of the reservoir rocks.
Modelled EM and seismic responses
To simulate the EM response recorded at the surface due to
changes in the reservoir conditions, we use the results of the
fluid flow simulator to create a subsurface cellular model of
resistivity distribution. The simulator provides pressure and
saturation changes at the time of each seismic survey acqui-
technical article
sition, which we also assume to be the time at which the
CSEM survey is acquired. These changes are then converted
into either seismic impedances and velocities, or vertical resistivities. A homogeneous overburden resistivity model was
generated from the well logs – this is used as a guide, despite
the fact that resistivity well logs are horizontally directed
while the CSEM-measured resistivity is vertically directed.
We then apply a 1D dipole modelling approach to the in-line
acquisition geometry of Key (2009) on a point-by-point basis
across the reservoir. It should be noted that the 1D responses
obtained using this approach represent the best case scenario
in terms of signal strength since no 3D effects are included
in the modelling. Such higher dimensional effects will reduce
the effect of the reservoir on the CSEM response significantly.
However, the time-lapse 1D approach used here provides an
instructive starting point to examine potential applications of
time-lapse CSEM in tracking different brines involved in secondary and enhanced oil recovery of hydrocarbon reservoirs.
Responses are presented in the common offset domain,
normalised against a baseline model, which in this case is the
starting state of the reservoir. The normalised response for
given source-receiver separation is plotted at the midpoint
between the source and receiver to produce a map of
anomalous response. Numerical computation determines
that an offset of 9 km and frequency of 0.2 Hz gives an
optimal response for the particular reservoir and overburden
structure under consideration. For comparative purposes,
seismic modelling is also performed on the same model using
the work of Amini et al. (2012), which incorporates a calibrated petroelastic model for the reservoir and convolutional
modelling to generate a seismic data volume.
Four scenarios are modelled – two extreme cases (X
and Y, as detailed previously), together with a UKCS
Figure 6 Time lapse seismic amplitude changes
for the four scenarios after 12 months of water
injection: (a) reference case, with constant brine
properties for both injected and formation water;
(b) Case X, in which subsurface aquifer water
(10,000 ppm, 24ºC) is injected into a highly saline
formation water (200,000 ppm, 89ºC) in a Middle
East reservoir; (c) Case Y, in which low salinity
water (1500 ppm, 18.7°C) is injected into formation water (15,000 ppm, 100ºC) in Endicott field
reservoir; and (d) Case Z, in which seawater
(30,000 ppm, 15ºC) is injected into formation
water (18,000 ppm, 58ºC) in a North Sea reservoir; (e) A bar chart displaying the percentage
time-lapse seismic amplitude changes for the four
scenarios.
© 2015 EAGE www.firstbreak.org
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first break volume 33, April 2015
Figure 7 As in Figure 6, but for the CSEM response.
case (Case Z). For reference, a model that holds all brine
properties constant over time and does not differentiate
between injected and formation water is also considered.
Figure 6 shows the corresponding mapped seismic timelapse response for all four scenarios, six months after water
injection. The change in water saturation generates a fairly
substantial hardening signal in the seismic of about 30%
for the reference model – easily sufficient to be detected in
practice. When the brine chemistry effects are included, there
is a difference of only a few fractions of a per cent between
Case X (high saline formation water) and Case Z (UKCS),
but 3% difference relative to the reference for Case Y (low
salinity injected water) where the effect of salinity decrease
can be clearly observed around the injector. Figure 7 shows
the results of the CSEM calculation. There are now some distinct differences between the scenarios and the reference, and
the effects of salinity compete against those of temperature to
create signals that exhibit both positive and negative polarity.
Time-lapse EM responses range from -13.5% to +6.5%, and
at least when only these 1D models are considered, are likely
to be detectable. For Case X, there is a weak 0.1% (positive)
change as the resistivity of the injected water is much higher
than that of the strongly saline formation water. The salinity effect completely counteracts the response due to water
saturation alone. The largest positive change (12.1%) is for
Case Y, and arises due to injection of the low salinity, high
resistivity water. Relative permeabilities of water through oil
are also adjusted as a result of the injection, and the water
front progression is slower than that of the reference in
Figure 7(a) – hence the strong low salinity anomaly in the
vicinity of the injector (see also Figure 2).
Diffusion of the higher salinity formation water into the
displacing front ensures the water saturation response can be
44
detected, unlike case X, albeit reduced relative to the reference. Case Z displays a similar response to the reference. This
is due to the injected and formation water having a similar
resistivity as salinity and temperature counteract each other
for both of these brines. Overall, the deviations of the EM
response from the reference are more significant than we
observed from seismic.
In summary, both time-lapse seismic and CSEM can
detect signals of water saturation (except perhaps case X),
but the seismic response is, in general, stronger. However it is
unlikely that the seismic will detect changes of brine chemistry, or distinguish between the scenarios we have considered.
Conclusions and discussion
It is already well known that both CSEM and seismic timelapse acquisition can be used to monitor the development of
waterfloods in hydrocarbon reservoirs. The CSEM response
is also strongly influenced by brine salinity and temperature
variations in the water itself, and the seismic by a strong
pressure up signal and to a lesser extent salinity and temperature. In practice, either imaging method may be used at
a qualitative level to assess basic injector performance such
as the likelihood of water breakthrough or inefficiencies due
to channelling. In addition to this acknowledged benefit,
our study shows that CSEM has the theoretical potential to
track individual brines in the reservoir, provided they have
distinctly different chemical signatures (salinity-temperature
footprints). However, in the three cases we selected from
producing fields worldwide, only the low salinity water flood
case revealed a strong and detectable signal. It is probable
that water injected into an aquifer will not be detected by
seismic, but CSEM may stand a chance. If injection is into
the oil leg, the flood will be visible to both techniques. Indeed,
www.firstbreak.org © 2015 EAGE
technical article
first break volume 33, April 2015
any likely benefit will be strongly field and reservoir dependent. This finding is important for reservoir monitoring and
management as brine mixing and rock-fluid interaction is
critical to understanding the proper functioning of reservoir
flow. Whilst monitoring of connectivity can to some extent be
accomplished by time-lapse logging, and tracer studies using
production chemistry, no current method exists to be able to
directly detect the spatial distribution of the individual brines
in the hydrocarbon reservoir. Three dimensional imaging of
the formation, aquifer and injected brines stands as a compelling challenge to our current monitoring techniques.
In this study we have focused on the detection of brines.
However many other potential applications for CSEM have
been cited in the literature. Another beneficial area includes
the polymer flood, where for certain polymers the resistivity
of the polymer to chase injected water is favourable (Jun et
al., 2012). There is an opportunity, as the 4D signal for both
polymer and chemical floods is thought to be negligible and
unsuitable for 4D seismic monitoring due to the small acoustic
contrasts (Johnston, 2013). Other possible applications include
monitoring methane or CO2 injection. As more CSEM data
are recorded over producing hydrocarbon reservoirs, then the
detection limitations defined above will become clearer.
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Acknowledgements
We would like thank the sponsors of the Edinburgh
Time Lapse Project, Phase V (BG, BP, CGG, Chevron,
ConocoPhillips, ENI, ExxonMobil, Hess, Ikon Science,
Landmark, Maersk, Nexen, Norsar, Petoro, Petrobras, RSI,
Shell, Statoil, Suncor, Taqa, TGS and Total) for supporting
this research. We would also like to thank SchlumbergerGeoquest for the use of its Petrel and Eclipse software.
Olarinre Salako acknowledges financial support from the
Petroleum Technology Development Fund, Nigeria. Dennis
Obidegwu, Veronica Omofoma and Erick Mackay are
thanked for their technical advice and discussions.
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