Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs

IMPERIAL COLLEGE LONDON
Department of Earth Science and Engineering
Centre for Petroleum Studies
Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs
By
Shwan Dizayee
A report submitted in partial fulfilment of the requirements for the MSc
and/or the DIC in Petroleum Engineering.
September 2016

ii Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs
DECLARATION OF OWN WORK
I declare that this thesis “Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs” is entirely
my own work and that where any material could be construed as the work of others, it is fully cited and
referenced, and/or with appropriate acknowledgement given.
Signature: …………………………………………………………..
Name of student: Shwan Dizayee
Names of supervisors: Professor Martin Blunt (Imperial College London)
Marie Ann Giddins (Schlumberger)

Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs iii
Abstract
Single-Well Chemical Tracer (SWCT) tests offer an in-situ method for determining the Residual Oil Saturation (ROS) of a
reservoir that has numerous advantages, compared to more conventional methods such as core analysis and well logging. It can
also be used to enhance the understanding of heterogeneity in the subsurface. Over the past 50 years numerous SWCT and IWCT
(Inter-Well Chemical Tracer) tests have been conducted in fields around the world. Most of these tests focused on measuring
the ROS as an aid to planning improved oil recovery processes, and to understand the results of pilot tests.
In this work, tracer responses to different heterogeneities are analysed using numerical modelling. Two methods are applied:
use of a multi-component reservoir simulation model, with chemical reactions to represent the reactive hydrolysis behaviour of
the tracer; and a simplified approach using tracer tracking in a conventional black oil simulation model. The models are validated
by back-calculating the ROS from the simulated tracer response, and it is shown that the simulation results are consistent when
varying permeability and keeping other parameters unchanged. When different rock regions are introduced, numerous peaks are
observed, illustrating delays in tracer arrival times due to flow irreversibility.
The reservoir simulation models can be used for forward modeling and sensitivity studies to design SWCT tests, and for
interpreting saturation measurements obtained in such tests. They can also be used for characterisation of well heterogeneities,
in conjunction with other data sources such as well logs.

iv Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs
Acknowledgments
In the name of Allah, the Most Gracious and Most Merciful, all praise to Allah for the strength and blessings he bestowed
upon me in completing this thesis.
I would like to start by expressing my warmest gratitude to my supervisors Marie Ann Giddins (Schlumberger) and Professor
Martin Blunt (Imperial College London) for their invaluable guidance, support and supervision throughout the duration of this
study.
I also would like to extend my gratitude to my colleagues Coriolan Rat and Mohamed Ahmed Elfeel for their help, and
availability in providing feedback during the study. I also wish to acknowledge the resources provided by Schlumberger
without which this study would have not been possible.
I am ever thankful to my parents for their unwavering faith in me, and their constant encouragement, prayers and support
throughout this MSc program.
I dedicate this piece of work to them, my sister, and my fiancée.

Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs v
Table of Contents
Title Page ....................................................................................................................................................................................... i
DECLARATION OF OWN WORK ............................................................................................................................................ ii
Abstract........................................................................................................................................................................................ iii
Acknowledgments........................................................................................................................................................................ iv
List of Figures.............................................................................................................................................................................. vi
List of Tables .............................................................................................................................................................................. vii
Abstract......................................................................................................................................................................................... 1
Introduction................................................................................................................................................................................... 1
Background................................................................................................................................................................................... 1
Implementation ............................................................................................................................................................................. 3
Tracer Model............................................................................................................................................................................. 3
Chemical Reaction Model......................................................................................................................................................... 3
Radial Model............................................................................................................................................................................. 4
Validation...................................................................................................................................................................................... 4
Numerical Models..................................................................................................................................................................... 4
Homogeneous Reservoir Case .................................................................................................................................................. 4
Test Design Sensitivity ................................................................................................................................................................. 6
Sensitivity to Soaking Period.................................................................................................................................................... 6
Partition Coefficient.................................................................................................................................................................. 6
Heterogeneous Model Description................................................................................................................................................ 7
Test Schedule................................................................................................................................................................................ 7
Results and Analysis ..................................................................................................................................................................... 8
Stratification.............................................................................................................................................................................. 8
Anisotropy................................................................................................................................................................................. 8
Residual Oil Saturation ............................................................................................................................................................. 9
Rock Types ............................................................................................................................................................................... 9
Water Mobility.........................................................................................................................................................................11
Water Mobility and Permeability.............................................................................................................................................12
Conclusions..................................................................................................................................................................................15
Recommendations for Further Study ...........................................................................................................................................15
Nomenclature...............................................................................................................................................................................15
Subscripts.................................................................................................................................................................................16
References....................................................................................................................................................................................16
Appendix A..................................................................................................................................................................................17
Critical Literature Review........................................................................................................................................................17
Appendix B ..................................................................................................................................................................................26
Tracer Theory: Analytical Model.............................................................................................................................................26

vi Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs
List of Figures
Figure 1 Schematic representation of test procedure. ................................................................................................................... 2
Figure 2 Tracer concentration profiles marked with tracer arrival times. ..................................................................................... 2
Figure 3 Aerial view of the radial model (r-direction).................................................................................................................. 4
Figure 4 Cross-sectional view of the radial model (z-direction)................................................................................................... 4
Figure 5 Tracer concentration profiles using the compositional simulator (left) and the black oil simulator (right).................... 5
Figure 6 Tracer concentration profile outputs from both simulators for the alcohol (left) and ester (right). ................................ 5
Figure 7 Tracer concentration profiles with back calculation using the compositional simulator (left) and using the black oil
simulator (right). ........................................................................................................................................................................... 5
Figure 8 Calculated ROS vs. soaking period. ............................................................................................................................... 6
Figure 9 Tracer concentration profiles for the ester (left) and the alcohol (right)......................................................................... 7
Figure 10 Calculated ROS vs. partition coefficient (for a model with a ROS of 0.2)................................................................... 7
Figure 11 Pore volume vs. partition coefficient............................................................................................................................ 7
Figure 12 Tracer concentration profiles for the stratification case................................................................................................ 8
Figure 13 Cross sectional view of tracer propagation for the stratification case........................................................................... 8
Figure 14 Tracer concentration profiles for the ester (left) and the alcohol (right) for the anisotropy case using the
compositonal simulator................................................................................................................................................................. 9
Figure 15 Tracer concentration profiles for the ester (left) and the alcohol (right) for different ROS.......................................... 9
Figure 16 Rock types populated in the radial grid. ....................................................................................................................... 9
Figure 17 Sample of relative permeability curves for ROS of 0.2 (left) and 0.2 (right). .............................................................10
Figure 18 Tracer concentration profiles for the ester (left) and the alcohol (right) for the different rock types case. .................10
Figure 19 Alcohol concentration profile for the rock types case. ................................................................................................10
Figure 20 Cross sectional view of tracer propagation for the case with a ROS of 0.01 in the BH region ...................................10
Figure 21 Cross sectional view of tracer propagation for the case with a ROS of 0.16 in the BH region ...................................10
Figure 22 Ratio of the peak magnitudes vs. the difference in the ROS. ......................................................................................11
Figure 23 Relative permeability curve.........................................................................................................................................11
Figure 24 Tracer concentration profiles for the ester (left) and the alcohol (Right) for the water mobility case .........................11
Figure 25 Cross sectional view of tracer propagation for the case with a Krw of 0.1..................................................................12
Figure 26 Cross sectional view of tracer propagation for the case with a Krw of 1.0..................................................................12
Figure 27 Relative permeability curve.........................................................................................................................................12
Figure 28 Tracer concentration profiles for the ester (left) and the alcohol (right) for the water mobility case (varying Krw in
the HROS region).........................................................................................................................................................................12
Figure 29 Cross sectional view of tracer propagation for the case with a Krw of 0.1 (in the HROS region). .............................12
Figure 30 Cross sectional view of tracer propagation for the case with a Krw of 1 (in the HROS region). ................................12
Figure 31 Tracer concentration profiles for the ester (left) and the alcohol (right) for water mobility-permeability case (in BH).
.....................................................................................................................................................................................................13
Figure 32 Tracer concentration profiles for the ester (left) and the alcohol (right) for water mobility-permeability case (in TH).
.....................................................................................................................................................................................................13
Figure 33 Back calculation schematic for the ROS of 0.4 and 0.1...............................................................................................14
Figure 34 Krw vs. ROS for Sandstone Reservoirs.......................................................................................................................14
Figure 35 Krw vs. ROS for Carbonate Reservoirs.......................................................................................................................14

Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs vii
List of Tables
Table 1 Components in Chemical Reaction Model....................................................................................................................... 3
Table 2 QC for ROS of 0.4. .......................................................................................................................................................... 6
Table 3 QC for ROS of 0.2. .......................................................................................................................................................... 6
Table 4 Summary of the tracer injection and production schedules applied................................................................................. 7
Table 5 Permeability stratification. ............................................................................................................................................... 8

Shwan Dizayee
Imperial College supervisor: Professor Martin J. Blunt
Industry supervisor: Marie Ann Giddins, Schlumberger
Abstract
Single-Well Chemical Tracer (SWCT) tests offer an in-situ method for determining the Residual Oil Saturation (ROS) of a
reservoir that has numerous advantages, compared to more conventional methods such as core analysis and well logging. It can
also be used to enhance the understanding of heterogeneity in the subsurface. Over the past 50 years numerous SWCT and IWCT
(Inter-Well Chemical Tracer) tests have been conducted in fields around the world. Most of these tests focused on measuring
the ROS as an aid to planning improved oil recovery processes, and to understand the results of pilot tests.
In this work, tracer responses to different heterogeneities are analysed using numerical modelling. Two methods are applied:
use of a multi-component reservoir simulation model, with chemical reactions to represent the reactive hydrolysis behaviour of
the tracer; and a simplified approach using tracer tracking in a conventional black oil simulation model. The models are validated
by back-calculating the ROS from the simulated tracer response, and it is shown that the simulation results are consistent when
varying permeability and keeping other parameters unchanged. When different rock regions are introduced, numerous peaks are
observed, illustrating delays in tracer arrival times due to flow irreversibility.
The reservoir simulation models can be used for forward modeling and sensitivity studies to design SWCT tests, and for
interpreting saturation measurements obtained in such tests. They can also be used for characterisation of well heterogeneities,
in conjunction with other data sources such as well logs.
Introduction
Tracer tests were first developed in the early 1900s for application in monitoring the movement of groundwater. Tracer
applications in reservoir studies have been reported since the mid-1950s (Du et al. 2005). Deans (1971) proposed the
functionality of SWCT tests in reservoirs. The first field application of SWCT tests was coordinated by Deans and his colleagues
at Esso Production Research Company in the East Texas Field in 1968 (Deans and Carlisle 1986). The research they conducted
focused on utilising the chromatographic separation of tracers. This involves the injection of a tracer into the formation and
through monitoring the arrival times of the different tracers during production a ROS measurement can be attained. It provides
a more economical alternative than IWCT tests, which involve the deployment of two wells, an injector and a producer that
often cover large distances of the field. SWCT tests enables near wellbore measurements over shorter testing periods and avoid
complexities associated with connectivity of flow between wells.
The demand for SWCT tests increased due to the increasing need for reservoir characterisation and application of enhanced
oil recovery techniques. Numerous published papers described the use of SWCT testing to enhance conventional methods of
determining fluid saturations such as core analysis and well logging, due to its in-situ nature and ability to access a broader
volume of the reservoir (DeZabala et al. 2011; Skrettingland et al. 2011, Jin et al. 2015).
Previous SWCT tests involved using it as a means to measuring the ROS in reservoirs due to its importance in ascertaining
which recovery method will help achieve maximum recovery (Pathak et al. 2011; Teklu et al. 2013, Cubillos et al. 2015). Its
function of measuring heterogeneity has been studied to a lesser extent with few recent papers that focus on analysing different
tracer responses to variances in heterogeneity (Descant et al. 1989). More recent papers seem to indirectly test heterogeneity
when implementing SWCT tests along with EOR techniques in heterogeneous reservoirs such as carbonates (Abdulla et al. 2013,
Fahad et al. 2015). Heterogeneity is an important parameter that needs more in-depth analysis.
Numerical modelling can be used to simulate SWCT tests in reservoirs. Modelling of SWCT tests can use chromatographic
separation of tracers in conjunction with a tracer reaction model (Tomich et al. 1973) or in conjunction with a fluid drift model
(Tomich and Deans 1975; Descant et al. 1989, Al-Shalabi et al. 2015).
In this paper reservoir simulation is used as a tool for forward modeling to investigate dynamic tracer responses to variations
in reservoir properties in the near-wellbore region, such as permeability heterogeneity and relative permeability curve
parameters. The paper is organised as follows: first a brief background theory and application of SWCT tests is provided,
followed by a description of the implementation and validation of numerical methods. Then a number of sensitivity analysis
cases are introduced to explore SWCT responses to different heterogeneities. Finally, we discuss the results and present our
concluding remarks.
Background
In SWCT tests, the tracers injected are often inert and have no impact on the chemical aspects of the subsurface. There is a
strong reliance on the in-situ hydrolysis of the tracer to recover interpretable results. It involves tracer injection into an oil-
bearing formation where one of the phases is mobile (water) and the other phase is immobile oil (Deans 1971). In SWCT
operations, a primary tracer bank consisting of about 1% by volume (Deans and Carlisle 1986, de Zwart et al. 2011) of the
partitioning tracer - an ester such as ethyl acetate or ethyl formate - is dissolved in formation water and injected into the reservoir
at residual conditions (Tomich et al. 1973). The mobile phase is the chosen carrier fluid (Cooke 1971).
Imperial College
London

2 Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs
An ester is desirable due to several of its characteristics such as being soluble in both the water and oil phases (Abdulla et al.
2013; Al-Shalabi et al. 2015, Khaledialidusti et al. 2015). This is followed by a bank of tracer-free water (Fig. 1). This tends to
be from the formation being tested to avoid disrupting the wettability of the reservoir (Deans 1971). This pushes the tracer slug
a desired distance into the formation and is often referred to as the ‘push volume’. The well is then permitted to shut in, allowing
a portion of the ester to hydrolyse, forming an alcohol - ethanol -, which is the secondary, non-partitioning tracer (Tomich et al.
1973, Jerauld et al. 2010). An important characteristic of the alcohol is that it is only soluble in the water phase.
Alcohol Formation 𝐸𝑠𝑡𝑒𝑟 + 𝐻2 𝑂 → 𝐴𝑙𝑐𝑜ℎ𝑜𝑙 + 𝐴𝑐𝑖𝑑
The acid is produced as a by-product of hydrolysis but is not observed as it is consumed in the reservoir (Deans 1971). An
important assumption is that hydrolysis occurring during injection is kept at a minimum to avoid flow reversibility effects.
Chromatographic retardation in different regions is achieved through differences in partition coefficients (Cooke 1971). The
degree of retardation is dependent on the pore sizes and is subsequently a function of the saturation of the immobile fluid. This
forms the fundamentals from which SWCT tests are based on, resulting in different tracer arrival times.
As the alcohol produced is only soluble in the mobile phase, it travels deeper into the reservoir than the ester and at a faster
rate within a homogeneous environment. This leads to an earlier breakthrough of the alcohol at the well and is the result of the
chromatographic separation of the tracers in the reservoir. Flow reversibility can occur during back-production which reverses
this separation meaning the tracers will back produce at the same time. We ran a simple test whereby a partitioning and non-
partitioning tracer is injected into the reservoir. The non-partitioning tracer travels further into the reservoir but the concentration
profiles corresponding to the two tracers shows that they overlap because they are being produced at the same time.
When there is a pressure gradient within the reservoir due to observation wells close to the test well, fluid movement in the
formation may be induced which is known as fluid drift (Descant et al. 1989). Tomich and Deans (1975) implemented fluid drift
in a numerical model to measure the ROS.
Conventionally the esters used in SWCT tests are more soluble in the oil phase and this is expressed by the partition
coefficient, KP (Deans and Carlisle 1986).
𝐾𝑃 =
𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑒𝑠𝑡𝑒𝑟 𝑖𝑛 𝑜𝑖𝑙
𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑒𝑠𝑡𝑒𝑟 𝑖𝑛 𝑤𝑎𝑡𝑒𝑟
...............................................................................................................................................(1)
The partition function is the ratio of tracer that has partitioned into the oil phase to that which has partitioned into the water
phase, at equilibrium. A higher partition ratio indicates that more of the tracer has partitioned into the oil phase. Equation 1 is
valid under the assumption that instantaneous equilibrium is achieved for the tracer between the two phases at residual
conditions. The partition coefficient for different tracers has to be measured in the laboratory at reservoir conditions (Deans and
Carlisle 1986). The reported 𝐾𝑃 values measured on a volume fraction basis typically range from 2.0 to 10.0 (Deans and Carlisle
1986, Jerauld et al. 2010).
An analytical model (Tomich et al. 1973, Deans and Carlisle 1986) can be
used to back calculate the ROS of a reservoir using the arrival times of the
partitioning and non-partitioning tracers. This can be used as a form of
validation of the results generated from a numerical model.
𝑆 𝑜𝑟 =
𝑡 𝑝−𝑡 𝑛𝑝
𝑡 𝑝+𝑡 𝑛𝑝(𝐾 𝑝−1)
....................................................................................(2)
Equation 2 uses the 𝐾𝑃 and breakthrough time which is defined as the time
of flight of the concentration profiles for each tracer (Fig. 2), to calculate the
ROS of the reservoir. The definition of tracer concentrations is often linked
to surface volumes rather than reservoir volumes in black oil reservoir
simulators. In this case, the formation volume factors of the phases should be
considered in Equation 2 for a more accurate calculation of the ROS:
𝑆 𝑜𝑟 =
𝑡 𝑝−𝑡 𝑛𝑝
𝑡 𝑝+𝑡 𝑛𝑝(𝐾 𝑝
𝐵𝑤
𝐵𝑜
−1)
................................................................................................................................................................(3)
Figure 2 Tracer concentration profiles
marked with tracer arrival times.
Figure 1 Schematic representation of test procedure.

Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs 3
Implementation
For our numerical models two methods were applied.
1. Tracer Model: A commercial black oil reservoir simulator is used with an intrinsic tracer model. The hydrolysis reaction
cannot be accounted for hence a workaround was implemented using two simulations. In the first one, only the injection
stages are simulated whereby two partitioning tracers with the same partitioning ratio are injected. The second one starts
from the last timestep of the previous simulation and models the production stage in which the partitioning ratio for one
of the tracers is set to zero to represent the alcohol.
2. Chemical Reaction Model: An industry standard commercial compositional reservoir simulator was used which allowed
for user defined reaction modelling. The tracers are modelled as water components (Schlumberger 2015).
The commercial black oil and compositional simulators are formulated to model up to three phases, oil, water and gas. The
main assumptions made when using these two simulators is that flow is isothermal and that mass transfer within each gridlock
of the model is instantaneous (Fanchi 2006). The Chemical Reaction model takes a much longer simulation time in comparison
to the Tracer Model. It is necessary to tune the chemical reaction rates to match the partitioning tracer behaviour correctly.
Tracer Model
In the black oil reservoir simulator, tracers are modelled as environmental tracers. These are passive tracers whose flow
through a porous media is assumed to have no influence on the flow of reservoir fluids and other tracers. It is assumed that zero
adsorption of tracer occurs within the formation to ensure full recovery of injected and produced components. The tracer
concentration is solved using a mass conservation equation at the end of each time step, having determined phase flows
(Schlumberger 2015). The governing equation for an environmental tracer in a single phase is:
𝑑
𝑑𝑡
(
𝑉𝑆𝐶
𝐵
) +
𝑑
𝑑𝑡
( 𝑉𝜌 𝑟
𝐶 𝑎 1−𝛷
𝛷
) = ∑ [
𝑇𝑘 𝑟
𝐵𝜇
( 𝛿𝑃 − 𝜌𝑔𝐷 𝑧) + 𝐷𝐹𝐷 𝑐 𝑆𝛿𝐶] + 𝑄𝐶 − 𝑉
𝑆
𝐵
𝜆𝐶 ...............................................................................(4)
In order to model partitioning tracers that exists in two phases, the mass conservation equation is modified. The two phases
are referred to as the ‘free’ (water) phase which is the reference phase for the tracer and the ‘solution’ (oil) phase.
𝑑
𝑑𝑡
( 𝑉(
𝑆 𝑓
𝐵 𝑓
𝐶 𝑓 +
𝑆 𝑠
𝐵 𝑆
𝐶 𝑠) +
𝑑
𝑑𝑡
( 𝑉𝜌 𝑟
𝐶 𝑎
(𝐶 𝑠)
1−𝛷
𝛷
) = ∑[𝐹𝑓 + 𝐹𝑠] + 𝑄 𝑓
𝐶 𝑓 + 𝑄 𝑠
𝐶 𝑆 − 𝑉
𝑆 𝑠
𝐵 𝑠
𝜆𝐶 𝑠 ......................................................................(5)
𝐾𝑝 =
𝐶 𝑆
𝐶 𝑓
.......................................................................................................................................................................................(6)
Equation 5 incorporates the assumption that the total reactive tracer velocity consists of two velocities corresponding to the
tracers in each phase. The effects of adsorption, decay and diffusion were not accounted for in this model.
Chemical Reaction Model
To reproduce the behaviour of partitioning tracers within the compositional reservoir simulator a chemical reaction model
must be applied. This model is based on the partitioning of tracers between two or more fluids. The velocity of a tracer depends
on the stream it has partitioned into. A major assumption in this model is that the reservoir is at residual oil conditions (Deans
1971).
Partition Equilibrium Stoichiometric Equation 𝐸𝑠𝑡𝑒𝑟(𝑤)
𝐾 𝑃
⇔ 𝐸𝑠𝑡𝑒𝑟 (𝑜)
Hydrolysis Reaction Stoichiometric Equation 𝐸𝑠𝑡𝑒𝑟(𝑤)
𝐾 𝐻
→ 𝐸𝑠𝑡𝑒𝑟 (𝑜)
The chemical reaction model consists of a chemical equilibrium which represents the partitioning of the tracer and is
governed by the partition coefficient (𝐾𝑃), and a hydrolysis reaction which forms the non-partitioning tracer.
Forward Reaction Stoichiometric Equation 𝐸𝑠𝑡𝑒𝑟(𝑤)
𝑅1
→ 𝐸𝑠𝑡𝑒𝑟(𝑜)
Backward Reaction Stoichiometric Equation 𝐸𝑠𝑡𝑒𝑟(𝑜)
𝑅2
→ 𝐸𝑠𝑡𝑒𝑟(𝑤)
Chemical Reaction Stoichiometric Equation 𝐸𝑠𝑡𝑒𝑟(𝑤)
𝑅3
→ 𝐴𝑙𝑐𝑜ℎ𝑜𝑙(𝑤)
The equivalent model that is input into the compositional simulator
model can subdivide the partition equilibrium into a forward and backward
reaction with the reaction rates of R1 and R2 respectively. The partitioned
tracer in the oil phase is represented as a solid component that is fully
suspended in oil (Table 1). This is in chemical equilibrium with the
partitioned tracer in the water phase which is expressed as a water
component (Table 1). The non-partitioning tracer that is produced in-situ is
also presented as a water component which is exclusively in the water phase
because alcohol is only soluble in water (Table 1).
𝐾𝑃 =
𝑅 𝑟1
𝑅 𝑟2
×
𝑉 𝑤,𝑟𝑒𝑠
𝑉𝑜,𝑟𝑒𝑠
~
𝐴 𝑟1
𝐴 𝑟2
×
𝑉 𝑤,𝑟𝑒𝑠
𝑉𝑜,𝑟𝑒𝑠
.................................................................................................................................................(7)
Chemical Reaction Model Components
Phase Components Component
Identifier
OIL
Oil C1
Gas C2
Solid/Partitioned tracer in oil C3
WATER
Fresh Water C4
Water/Partitioned tracer C5
Water/Non-partitioned tracer C6
Table 1 Components in Chemical Reaction Model.

The equilibrium reactions can be coupled through their reaction rate constants (Ar1and Ar2) in order to ensure that a partition
equilibrium is achieved within each cell of the model. The equivalent model uses the Arrhenius equation with the addition of a
sink term.
𝑅 𝑟 = 𝐴 𝑟 × 𝑉𝑏 × 𝑒
−
𝐸 𝑟
𝑅1 𝑇1 × ∏ 𝑐 𝑟𝑖
𝑛 𝑟𝑖
≈ 𝐴 𝑟 × 𝑉𝑏.............................................................................................................................(8)
The concentration has an effect but this can be ignored for the basis of this test along with the activation molar energy.
Therefore the reaction rate in a cell can be approximated as the product of the cell bulk volume and the reaction rate constant.
The first step is to calculate the number of moles of partitioning tracer (C5) that is injected:
𝑛 𝐶5
𝑖𝑛𝑗𝑒𝑐𝑡𝑒𝑑
= 𝑄𝑠𝑡𝑟𝑒𝑎𝑚
𝑖𝑛𝑗𝑒𝑐𝑡𝑖𝑜𝑛
× ∆𝑇𝑠𝑙𝑢𝑔 × 𝑥 𝐶5
𝑠𝑡𝑟𝑒𝑎𝑚
×
𝜌 𝐶5
𝑀 𝐶5
.....................................................................................................................(9)
The product of this is then input into equation 8 to calculate the forward reaction rate constant (Ar1). An important assumption
in this calculation is that the partitioning equilibrium is achieved almost instantaneously within each cell (tequilibrium). The
forward reaction rate constant is then calculated as follows:
𝐴 𝑟1 =
𝑅 𝑟1
𝑉 𝑏
=
𝑛 𝐶5
𝑡 𝑒𝑞𝑢𝑖𝑙𝑖𝑏𝑟𝑖𝑢𝑚×𝑉 𝑏
.......................................................................................................................................................(10)
The backward reaction rate constant (Ar2) can then be calculated by rearranging formula 7 and inputting the reservoir fluid
volumes. Subsequently, the rate of the forward reaction (Rr1) is calculated by substituting Ar1 into equation 8. The rate of the
backward reaction is similarly calculated using Ar2. To ensure that perturbation of the partition equilibrium is mitigated, the
hydrolysis reaction rate constant is set to an arbitrary value which also allows for the gradual rate of
hydrolysis during the soaking period. This provided a set of reaction rate constants for each ROS.
Radial Model
A radial grid was used in our numerical models (Figs. 3, 4) to ensure we effectively capture
heterogeneity within the near wellbore region. A sensitivity analysis was conducted on the number
of 2-D grid cells, with a coarse grid with dimensions 20×1×10, an intermediate grid of dimensions
50×1×10 and a finer grid with dimensions 105×1×10. It was concluded that the intermediate model
ensured reduction of numerical dispersion and compared well to the finer grid profiles, whilst
requiring less cells. The outer radius of the model is 50 m with an inner radius of 0.1 m to ensure
that the tracer propagates out radially a sufficient distance into the reservoir to capture near-
wellbore heterogeneity without reaching the bounds of the reservoir to avoid pressure
fluctuations. The average radius of investigation in this study is 8 m (26 ft.), which can
vary slightly depending on the heterogeneity present. Another sensitivity was conducted
on the sizes of the grid blocks in the radial-direction. The geometric progression of cells
in an outwardly direction provides a better support for radial inflow. The reservoir pressure is
250 bar and is maintained in the model through the placement of a secondary well in the outer
radius of the reservoir which injects and produces at the same rate as the testing well. The wells are completed for the whole
reservoir starting at 2000 m, for a depth of 10 metres with all layers being perforated.
Validation
Numerical Models
The tracer and chemical reaction models can be validated against the analytical model through back calculating the ROS
using simulation outputs to try and match it with the actual ROS of the reservoir. This is achieved using equations 2 and 3. It is
also important to ensure that the results from the chemical reaction model matches those from the tracer model and this can be
validated through matching the tracer concentration profile results from the compositional simulator to those from the black oil
simulator.
Homogeneous Reservoir Case
A homogeneous model of dimensions 50×1×10 was created with isotropic permeability and a thickness of 1m in each layer.
The model consists of sandstone lithology with a uniform porosity of 0.2. A Kv/Kh ratio of 0.01 was used as in most fields the
permeability in the horizontal direction tends to be greater than the permeability in the vertical direction. The permeability in
the horizontal plane is 200 mD whereas the permeability in the vertical direction is 2 mD.
The homogeneous model was created to analyse the production profiles within a fully homogeneous environment. This
would act as a good benchmark from which a better understanding of tracer behaviour can be deduced when applied to more
complex structures such as a heterogeneous reservoir.
The anticipated results for a homogeneous case would be the observation of smooth curves for the alcohol and the ester
whilst the alcohol back produces more quickly (connoted by a shift to the left). This can be observed when looking at the tracer
concentration profiles (Fig. 5).
Figure 3 Aerial view of the
radial model (r-direction).
Figure 4 Cross sectional view
of the radial model (z-direction).

Figure 5 Tracer concentration profiles using the compositional simulator (left) and the black oil simulator (right).
Validation of this model comes in two fold. First results from the compositional simulator are benchmarked against those
from the black oil simulator. From these results the peaks show good alignment (Fig. 6). The alcohol production profile suggests
that more is being produced in the compositional model judging from the area underneath the peak. The reason for this is that
the chemical reaction model is implemented in the compositional simulator model whereas in the black oil simulator a work-
around has been applied. The hydrolysis of the ester is governed by a set of calculated reaction rate constants whereas in the
tracer model it is assumed that wherever the ester propagates to, alcohol also appears. This is achieved through changing the
𝐾𝑃 of one of the partitioning tracers to zero in the second simulation. The ester production profiles show a perfect match
supporting the claim that the difference in the alcohol profiles is due to the work around as the same amount of ester has been
injected in both cases. Both cases use the same grid resulting in similar numerical dispersion effects which can hence be ruled
out as the cause of this difference.
Figure 6 Tracer concentration profile outputs from both simulators for the alcohol (left) and ester (right).
The second validation step involves a comparison study between the numerical and analytical models. This can be achieved
through back calculating the ROS to try and match it with the simulation input. The input ROS was 0.2. For the compositional
simulator, the calculated ROS was 0.18, using equation 2 (Fig. 7). For the black oil simulator, the calculated ROS was 0.19,
using equation 3 (Fig. 7). The calculated ROS for the chemical reaction and tracer models gives values which match well with
the actual residual of the model. The formation volume factors used in the back calculation equation for the tracer model are
1.0132 rm3
/sm3
for the water and the 1.2 rm3
/sm3
for the oil.
Figure 7 Tracer concentration profiles with back calculation using the compositional simulator (left) and using the black oil simulator
(right).

Test Design Sensitivity
Sensitivity to Soaking Period
As a rule of thumb, when calculating the ROS using tracer arrival times, the soaking period is required to be greater than
twice the transit time (Tomich et al. 1973) in order for equation 2 to be valid.
𝑡 𝑠𝑜𝑎𝑘 > 2 × 𝑡𝑡𝑟𝑎𝑛𝑠𝑖𝑡 ...............................................................................................................................................................(11)
The transit time is defined as the addition of the time it takes for the ester production profile to reach its peak and the injection
time. The above formula was checked by varying the soaking period in 9 different cases using the homogeneous chemical
reaction model. Two different ROS of 0.4 and 0.2 were tested to increase the reliability of the results.
Table 2 QC for ROS of 0.4. Table 3 QC for ROS of 0.2.
A constant 𝐾𝑃 value of 10 was used in both tests. Table 2 shows the results for varying the shut in period along with the value
of the calculated ROS using equation 2. For a ROS of 0.4, the reaction rate constants used were; Ar1 = 28294, Ar2 = 4244,
and Ar3 = 0.01. Table 3 shows the results for a ROS of 0.2 and reaction rate constants of Ar1 = 35368, Ar2 = 14145, Ar3 =
0.01. When the ratio of the soak time to the transit time is below 1, a poor match is observed between the calculated ROS and
the actual ROS in the model (which is governed by the relative permeability curves). When the soak period is around twice the
transit time (ratio of 2.91), the calculated ROS matches well with the actual ROS. This affirms the relationship stated by Tomich
et al. (1973). This is because a longer soak period permits more time for hydrolysis to occur, leading to a greater amount of
alcohol being produced compared to that being produced during the injection. This increases the distance between the alcohol
and ester peaks as more alcohol is being produced at a faster rate, hence increasing the accuracy of the ROS calculation. It must
be noted that this relationship is a rule of thumb and is not 100% accurate as when the soak is less than twice the transit time,
there seems to be a relatively good match between the calculated ROS and the actual ROS.
Figure 8 illustrates the relationship between the calculated ROS and the length of the soaking period. Both ROS values show
a similar trend of plateauing out once the soaking period is much greater compared to the transit time.
Partition Coefficient
The partition coefficient determines the amount of tracer that partitions into the oil and water phases, as discussed previously.
This is an important parameter as it directly affects the amount of recoverable ester and alcohol that is produced. This was
modelled using the homogeneous chemical reaction model. This was to ensure that the model has only one variable to show the
true extent of varying 𝐾𝑃. The anticipated results are that we would observe a shift in the peaks as the partition ratio is increased.
The concentration profile for the esters (Fig. 9) shows effects of flow reversibility as the peaks overlap. A noticeable trend is
that as the partition coefficient is increased, the maximum concentration of ester produced marginally increases. This is because
an increase in 𝐾𝑃 denotes more of the ester has partitioned into the oil phase, rendering it unavailable for hydrolysis, and is hence
back produced as ester. As for the alcohol, an increase in the partition coefficient seems to cause the peaks to shift to the left
hand-side as anticipated and the magnitude of the peaks reduces as we increase 𝐾𝑃 from 2 to 10.
The tracer propagation in the grid cells for the case with a partition coefficient of 2 showed that during injection, the ester
travels further into the reservoir as less ester has partitioned into the immobile phase and hence more of it is available for
hydrolysis. This also means that during production, it takes much longer to back produce all of the alcohol due to the distance
travelled into the reservoir. For the case with a partition coefficient of 10, it showed that during injection the ester does not travel
as far into the reservoir because more of it has partitioned into the immobile phase, so the ester in the mobile fluid will propagate
Residual Oil Saturation QC
Tsoak/Ttransit
Sor
(calculated) QC
0.01 0.125 X
0.07 0.138 X
0.15 0.166 X
0.29 0.293 X
0.73 0.363 X
1.02 0.374 X
1.45 0.388 
2.91 0.394 
7.27 0.399 
Residual Oil Saturation QC
Tsoak/Ttransit
Sor
(calculated) QC
0.01 0.061 X
0.07 0.075 X
0.15 0.089 X
0.29 0.129 X
0.73 0.165 X
1.02 0.176 X
1.45 0.182 
2.91 0.188 
7.27 0.194 
Figure 8 Calculated ROS vs. soaking period.

at a lower speed hence keeping it within close vicinity of the well, where it would hydrolyse to form the alcohol. This is then
back produced at a much faster rate due to it being a shorter distance from the well. As less ester is available for hydrolysis at a
higher 𝐾𝑃 value, the maximum concentration of alcohol produced decreases as the partition coefficient is increased.
This study can be furthered by correlating the calculated ROS to the partition coefficient (Fig .10). A general trend is that
as 𝐾𝑃 increases an improved match between the actual and calculated ROS is observed. Another analysis (Fig. 11) shows that
as the partition coefficient is increased, the radius of investigation (a marker of the pore volume) decreases because more ester
partitions into the immobile phase. This is in line with the trends observed in the tracer concentration profiles (Fig. 9).
Figure 11 Pore volume vs. partition coefficient.
Heterogeneous Model Description
A heterogeneous model was created with the same input parameters as the homogeneous model but with variations in
heterogeneity. A number of different permeability arrangements were applied and tested in order to ascertain whether any
interpretable results can be collected. All the cases that have been tested used a Kv/Kh ratio of 0.01 unless stated otherwise. The
different heterogeneities applied in the model were absolute permeability heterogeneity, anisotropy, varying the ROS, rock types
and water mobility. This was modelled for a reservoir consisting of a sandstone lithology at a uniform porosity of 0.2. In this
study both SWCT test models were used and validated through back calculation of the ROS.
The anticipated results for a heterogeneous case would be the observation of multiple peaks – broader than seen for a
homogeneous case - for both the alcohol and the ester whilst the alcohol back produces at a higher rate (connoted by a shift to
the left).
Test Schedule
It was important to ensure that the results obtained from
these tests were representative of field conditions. The
timescale of SWCT tests in the field tends to range between
10 to 20 days. The injection of the ester bank in formation
water requires less time because as mentioned previously the
ester typically makes up about 1% by volume of the first
injection. This is followed by a bank of tracer-free water that
is injected in large amounts at the same rate as the tracer bank
to ensure complete hydrolysis of the injected tracer. This also
ensures that the tracer is swept deep into the reservoir to ensure viable measurements and results. The total injection continues
until a volume of around 318 sm3
(2,000 barrels) is displaced into the formation (Deans 1971). The soaking period is important
as this governs the amount of secondary tracer produced. The well is shut-in for a duration of 1-6 days depending on the
reactivity of the ester deployed and the reservoir temperature (Deans and Carlisle 1986). In practice the shut-in period can
range between 6-8 days to ensure complete hydrolysis of the ester (Fahad et al. 2015). It is required to be long enough for the
Tracer injection and production summary
Procedure Duration (days)
Injection of ester bank + formation water 0.2
Injection of formation water 3.5
Shut-in 7
Production 30
Table 4 Summary of the tracer injection and production
schedules applied.
Procedure Duration (days)
Injection of ester bank + formation water 0.2
Injection of formation water 3.5
Shut-in 7
Production 30
Table 4 Summary of the applied tracer injection and production
schedules.
Tracer injection and production summary
Figure 9 Tracer Concentration profiles for the ester (left)
and for the alcohol (right).
Figure 10 Calculated ROS vs. partition coefficient (for
a model with a ROS of 0.2).

hydrolysis reaction to proceed from 10% to 50% completion (Deans and Carlisle 1986, Jerauld et al. 2010). In this test the well
was shut-in for 7 days after which the well is allowed to back-produce. The rate of production in literature is around 650 bbls/d
which roughly equates to 100 sm3
/d (Deans 1971). This is the rate applied in our tests to ensure all of the injected partitioning
tracer and produced non-partitioning tracers in the formation are produced. To ensure the back production of all of the injected
and produced tracers, the production time in this test has been prolonged to 30 days (Table 4).
Results and Analysis
Stratification
A range of permeabilities were populated in the z-direction using the heterogeneous chemical
reaction model (Table 5). These permeabilities ranged from 5-600 mD and were introduced in a
stratified sequence to ascertain whether the tracer response could be calibrated on a layer-by-layer
basis as shown by Descant et al. (1989). We expected several peaks within the concentration profile
where each peak is associated to a specific layer. However, flow reversibility means that SWCT test
results are usually neutral to permeability heterogeneity, unless there is a significant fluid drift in the
reservoir. It must be noted that Descant et al. (1989) used a fluid drift model to induce flow
irreversibility.
The profile of the peaks (Fig. 12) is similar to that of the homogeneous model and the expected
protruding peaks cannot be observed. These results seem to illustrate the manifestation of flow
reversibility as the tracers back produce at the same time at the wellbore. The propagation of tracer
within the grid cells (Fig. 13) showed that for layer 6 (600mD) the tracer propagates further into
the reservoir whereas for layer 5 (5mD) the tracer remains within the near wellbore region.
Transmissibility (Kh) is a measure of the conductivity of the formation and can be used in this case
to analyse the tracer flow into the reservoir. As the height of the layers is uniform, Kh is directly
proportional to the permeability, which further explains the differences in tracer distribution when looking at figure 13. The
back calculation of Sor gives 0.17 giving a good match with the actual ROS of 0.2.
Anisotropy
Anisotropy when applied to permeability is the ratio of permeability in the vertical direction over the permeability in the
horizontal direction (Kv/Kh). This is more explicitly called vertical permeability anisotropy.
This sensitivity case involved varying the anisotropy for the heterogeneous model used in the stratification sensitivity case.
The anisotropy was initially varied in small increments within the range of 0.01-1. This range was chosen due to its agreement
with actual anisotropies observed in the field. The permeability in the horizontal direction tends to be greater than the
permeability in the vertical direction in sandstone and shaly sand reservoirs.
Varying anisotropy would affect the flow of the mobile phase within the reservoir. A lower permeability in the vertical
direction would lead to horizontal flow becoming a more favourable pathway for flow.
The tracer production profiles (Fig. 14) show that the peaks for both tracers are well aligned for the different Kv/Kh cases
but vary marginally in magnitude. A general trend is that as anisotropy is reduced from 1 to 0.01, the magnitude of the peak
increases. This suggests that there is a delay in the production of alcohol as Kv/Kh increases. This was further analysed by
increasing Kv/Kh to 5 and 10 which showed a further decrease in the magnitude of the peaks. As Kv/Kh increases the
permeability in the vertical direction must increase meaning gravitational effects become more prominent. This results in a
greater degree of cross flow occurring which subsequently increases the distance the tracer has to travel, causing a delay in the
arrival times of the tracers during production. This was affirmed by checking the cumulative water production in each layer. As
Kv/Kh increases, the amount of water produced from the upper layers decreases whilst water production from the bottom layers
increases due to cross flow. In this case an increase in the magnitude of the peak does not indicate that we are producing more
as the overall material balance is the same for every case (we are comparing results from the same simulator). But the maximum
attainable concentration for each Kv/Kh ratio may differ.
Permeability Stratification
Reservoir
Layer Permeability
1 20
2 200
3 100
4 10
5 5
6 600
7 250
8 80
9 140
10 300
Table 5 Permeability
stratification.
Reservoir
Layer Permeability
1 20
2 200
3 100
4 10
5 5
6 600
7 250
8 80
9 140
10 300
stratification.
Figure 13:
Reservoir
Layer Permeability
1 20
2 200
3 100
4 10
5 5
6 600
7 250
8 80
9 140
10 300
stratification.
Figure 13:
Reservoir
Layer Permeability
1 20
2 200
3 100
Figure 12 Tracer concentration profiles
for the stratification case.
Figure 13 Cross sectional view of tracer
propagation for the stratification case.

Residual Oil Saturation
Using the homogeneous tracer model, the ROS was varied using end-point scaling of distinct relative permeability curves in
a number of different cases to ascertain whether a shift can be observed in the tracer production profiles. The anticipated results
would be a visually apparent shift of the peaks for both the alcohol and ester tracers as the ROS increases. A range of ROS from
0.1 to 0.4 was tested. Figure 15 shows the results of varying the ROS in multiple cases (each has a uniform Sor).
The concentration profiles for the ester (Fig. 15) shows that they are in perfect alignment. This is a manifestation of flow
reversibility where the chromatographic separation of the injected tracers is reversed during back production leading to the
production of the ester tracers at the same time for the different ROS cases. A shift to the left can be observed for the alcohol
concentration profiles as the ROS is increased from 0.1 to 0.4. The propagation of tracer in the grid cells shows that the tracer
injection for a ROS of 0.1 travels further into the reservoir in comparison to the case with a ROS of 0.4.
As introduced by Tomich et al. (1973) the retardation factor (β) which is the ratio of the number of moles of the partitioning
tracer in the oil phase over that in the water phase can be calculated using the ROS and the 𝐾𝑃. For a ROS of 0.4, β is much
higher as opposed to when the ROS is 0.1, meaning there is a larger number of moles of the partitioning tracer in the oil phase
and hence the tracer ends up propagating at a slower speed into the reservoir. This means that the tracer remains closer to the
well and when back produced, these tracers will produce first as they have a shorter distance to travel.
Rock Types
Implementing the chemical reaction model in the case of introducing various rock types is
complex. Hence, all subsequent simulations were performed using the tracer model. The ROS are
populated per grid cell within two regions (Fig. 16). In our models, the ROS is varied using the
relative permeability curves which is end-point scaled for different ROS (Fig. 17). The Corey
exponents used are those applicable for sandstone reservoirs (Corey oil: 3, Corey water: 4). This
sensitivity was coordinated through fixing the ROS in the TH (top half of the model) to 0.4 and
varying the ROS of the BH (bottom half of the model) from 0.1 to 0.4 in increments of 0.05. The
Figure 14 Tracer concentration profiles for the ester (left) and the alcohol (right) for the anisotropy case using the compositional
simulator.
Figure 15 Tracer concentration profiles for the ester (left) and the alcohol (right) for different ROS.
Figure 16 Rock types
populated in the radial grid.

water relative permeability endpoints (Krw) were fixed at 0.3 for the TH and 0.7 for the
BH. The expected result is a single peak for one rock region and a second peak protruding
out which represents the second rock region.
From the ester production profile (Fig. 18) it is evident that flow reversibility effects
can be observed. The alcohol on the other hand illustrates the shift that was observed in
the ROS sensitivity case, but there is also an apparent change in the profile of the peaks.
For the case where the BH has a ROS of 0.1, a skewed peak seems to protrude out from
the first peak. This can be labelled as the ‘double peak effect’. As the ROS in the BH of
the model increases from 0.1 to 0.2, the second peak is almost fully masked. From a ROS of 0.2
upwards, the homogeneous response of a single smooth peak can be observed, becoming
narrower as the ROS is increased whilst also increasing in magnitude. There seems to be an
obvious trend between the difference in ROS between the two regions and the prominence of the second peak which is interpreted
as a heterogeneity marker. This suggests that the second peak is associated with the BH of the model, and this is something that
can be validated through back calculation of the ROS.
Figure 18 Tracer concentration profiles for the ester (left) and the alcohol (right) for the different rock types case.
The next step was to ascertain whether an even lower residual in the BH can increase the prominence of the protruded peak.
Another two cases were explored where the ROS was changed to 0.01 and 0.05 and it was found that as the difference between
the residuals in the two regions increases, the prominence of the skewed second peak also increases.
To find the cut-off point at which this heterogeneity marker can no longer be visible, smaller intervals from 0.1 to 0.16 were
tested and it was found that as the ROS is increased from 0.1 to 0.15 a second peak is observable but is least prominent when
the ROS is at 0.15 (Fig. 19). The tracer propagation in the grid cells showed that for the case with a BH ROS of 0.01 (Fig. 20),
the tracer propagates deeper into the reservoir relative to the distance travelled in the TH, causing flow irreversibility which
leads to a second peak protruding out. On the other hand, for the case where BH ROS is 0.16 (Fig. 21), the tracer does not
propagate much further into the reservoir than in the TH and this is where only a single peak can be observed.
By plotting the ratio between the first peak and the second peak against the difference in ROS between the two regions (Fig.
22) we can try to establish a relationship that can be used to identify when the second peak could appear. It is possible to fit a
2nd
degree polynomial to the cases where a second peak protrudes out. The red line illustrates the boundary beyond which the
conditions for observing a double peak is satisfied, i.e. ∆𝑆 𝑜𝑟 > 0.24.
Figure 17 Sample of relative
permeability curves for ROS
of 0.4 (left) and 0.2 (right).
Figure 19 Alcohol concentration
profile for the rock types case.
Figure 20 Cross sectional view of tracer
propagation for the case with a ROS of
0.01 in the BH region
Figure 21 Cross sectional view of
tracer propagation for the case with
a ROS of 0.16 in the BH region.

Water Mobility
The previous rock type sensitivity case was extended through analysis of the effects of varying water mobility. The case
forwarded for study was where the TH had a ROS of 0.4 and the BH had a ROS of 0.1 and its tracer profile conveyed the ‘double
peak effect’. For the basis of this test, the TH will be labelled as the HROS (high residual oil saturation) region and the BH will
be the LROS (low residual oil saturation) region.
For the first case, the relative water permeability endpoint (Krw) for the HROS region was fixed at 0.2 but was varied for
the LROS region, from 0.1 to 1 in increments of 0.1 The Krw was varied through end point scaling of relative permeability
curves for different Krw endpoints on the y-axis (Fig. 23).
Looking at the results for the ester concentration profiles (Fig. 24) it is evident that flow reversibility effects take precedence
as the peaks overlap indicating that they are back producing at the same time in all the cases. The alcohol peak shows good
alignment between the first peaks and as Krw is increased the second peak protrudes out and increases in magnitude, becoming
more prominent. As the second peak increases in magnitude the first peak decreases. As Krw increases the second peak becomes
more prominent because the mobility of the LROS region becomes larger than the Krw in the HROS region. This means that
the carrier fluid and tracer travels further into the reservoir in the LROS region and hence there is a delay in its arrival time,
creating the second peak. The amount of tracer injected into the LROS region also increases as Krw increases because a more
favourable pathway is created leading to a higher flux of injection. By reporting the water production rate in each layer, it was
possible to ascertain the flux of the tracer flowing into each layer in a constant permeability environment. The tracer in the
HROS region remains in the close vicinity of the well due to a lower relative mobility and a higher retardation factor as seen in
the ROS sensitivity study, which hinders tracer injection and propagation through the formation. This leads to a reduction in the
maximum production concentration attained in the HROS region. It must be noted that for material balance purposes, the same
total amount of tracer is injected and produced in each case.
The above analysis can be strengthened when looking at the tracer propagation within the grid cells of the model. It is
apparent that for the case where Krw is 0.1 (Fig. 25), the tracers in both regions propagate an equal distance into the reservoir
meaning during back production flow reversibility effects reverses the separation of the two tracers which causes the single peak
in the alcohol production profile. On the other hand, for the Krw of 1.0 case (Fig. 26), it is evident that the tracer in the LROS
region propagates further into the reservoir due to the lower residual oil saturation and higher water mobility which causes the
“double peak effect” in the alcohol production profile.
Figure 24 Tracer concentration profiles for the ester (left) and
the alcohol (right) for the water mobility case.Figure 23 Relative permeability curve.
Figure 22 Ratio of the peak magnitudes vs. the difference in ROS.

The second case involves fixing the Krw for the LROS region at 0.2, and varying the Krw for the HROS region from 0.1 to
1 in increments of 0.1 (Fig. 27). The ester peaks overlapped again (Fig. 28). The alcohol profile on the other hand shows good
alignment of the peaks. The prominence and magnitude of the second peak seems to reduce as the Krw is increased from 0.1 to
1.0, which seems to show the reversal of the trend observed in the previous case.
This is because when the Krw for the HROS region is 0.1, this is lower than the Krw for the LROS region (0.2), hence the
carrier fluid and tracer has a higher relative mobility in the LROS region causing a delay in arrival times and hence illustrating
this as a second peak. A higher Krw in the HROS region means it has a higher relative mobility but as the residual oil saturation
is much higher than in the LROS region, the tracer propagates out to roughly the same distance in the formation and the tracers
from both regions are subsequently back produced at the same time.
This can be further analysed by looking at the tracer propagation in the grid cells. For the case where Krw is 0.1 (Fig. 29) in
the HROS region, this is lower than the Krw in the LROS region so the tracer propagates further in the LROS region as it has a
higher mobility. This explains why the second peak protrudes out in this case. The case where the Krw is 1 (Fig. 30) for the
HROS region shows that the tracer propagates further into the reservoir by a marginal difference because the Krw (HROS) >
Krw (LROS). The HROS has a ROS of 0.4 which means that the volume the mobile fluid can flow through is lower hence
impeding the flow of the tracer. Even though it has a higher Krw in this case, it is limited by the high ROS resulting in similar
arrival times for the two tracers from the two different rock regions. This explains why a single peak was observed at higher
Krw values in the HROS region.
Water Mobility and Permeability
These results lead us to the final sensitivity case whereby permeability heterogeneity was added to the case with different
rock types. The case that was forwarded was the water mobility case with a ROS of 0.4 and Krw of 0.2 in the TH and a ROS of
0.1 and Krw of 0.6 in the BH. The permeability was varied in accordance to the ratio of the permeability in the TH and the BH
of the model.
The first case that was explored involved increasing the permeability in the BH region and fixing the TH at a constant
permeability of 100 mD throughout. The ester production profile (Fig. 31) showed that the magnitude of the peaks decreased as
tracer propagation for the case with a
Krw of 1.0.
tracer propagation for the case with a
Krw of 0.1.
Figure 27 Relative permeability curve.
Figure 28 Tracer concentration profiles for the ester (left) and the alcohol (right)
for the water mobility case (varying Krw in the HROS region).
Figure 29 Cross sectional view of tracer propagation
for the case with a Krw of 0.1 (in the HROS region).
Figure 30 Cross sectional view of tracer propagation
for the case with a Krw of 1 (in the HROS region).

the permeability ratio was increased which is similar to the results that were observed for the anisotropy sensitivity case. The
general trend for the alcohol production profile was that as the permeability ratio was increased, the first peak became less
prominent and eventually smeared out. Increasing the permeability from 100 to 500 mD resulted in an increase in the magnitude
of the second peak, but beyond this range, from a permeability of 500 mD onwards, a slight reduction in the magnitude of the
second peak can be observed.
In the second case this was reversed and the permeability in the TH of the model was varied. For this case, the general trend
observed in the alcohol concentration profiles (Fig. 32) was that as the permeability ratio was increased, the second peak became
less prominent and smeared out whilst the first peak increased in magnitude. This shows that permeability heterogeneity does
have an effect on the arrival times of the tracers.
Discussion
The objective of this report was to model SWCT tests using a numerical approach to help understand the effects of
heterogeneity on tracer response, through forward modelling. This helps to mitigate uncertainties involved with the application
of SWCT tests in heterogeneous reservoirs, which are not fully addressed by analytical methods that have been proposed. The
numerical approach provides accuracy and simplicity in measuring tracer arrival times, which in reality could be difficult to
achieve due to severe fluctuations and inconsistencies in reservoir conditions and physical barriers to flow in the subsurface. It
also allows for test design optimisation. The test design sensitivity section showed that the soaking period can be adjusted in
accordance to the transit time of the tracer within the system without compromising on the accuracy of the ROS measurement.
The partition coefficient can also be varied whilst maintaining a good match between the calculated ROS and the actual residual.
Another important testing aspect is the distance the tracer propagates out into the reservoir. This was correlated with the different
partition coefficients to enable the optimisation of KP to achieve a certain radius of investigation. When applied in the field, this
means that these parameters can be optimised in line with test specifications. An example of this would be shortening the soaking
period to obtain quick results from a field or optimising the partition coefficient to use a more inexpensive tracer whilst maintain
the target radius of investigation.
The two numerical models integrated into our studies were a tracer model and a chemical reaction model, using the black oil
and compositional reservoir simulators respectively. An important step in this study was to ensure that the numerical models
matched the analytical methods, which would help in verifying them both. The chemical reaction model was successfully
benchmarked against the tracer model for both the homogeneous and heterogeneous cases giving a good match. Another
important step was to compare both models to the analytical tracer model as proposed by Deans (1971) and Tomich et al. (1973)
through back calculating the ROS. The calculated ROS for the homogeneous chemical reaction model matched well with the
actual ROS with a relative error of around 10%. The homogeneous tracer model also gave a good match with a relative error of
around 5%. It was also found that the calculation was sensitive to changes in the soaking period (as presented by the condition
set by Tomich et al. 1973) and the partition coefficient.
Permeability heterogeneity was varied in the heterogeneous model in the form of stratification and anisotropy. In both cases
the ROS was back calculated giving the same relative error of 10% as for the homogeneous chemical reaction model, which is
a good match. This shows that heterogeneity can be modelled whilst keeping in line with the analytical tracer model. Variations
in permeability heterogeneity seemed to have little effect on the tracer production profiles making it difficult to pick out a clear
signature of heterogeneity. A reported method for inducing flow irreversibility is through inducing different pressures in the
different stratified layers by varying the injection and production rates layer-by-layer (Descant et al. 1989; de Zwart et al. 2011,
Abdullah et al. 2013). Our system was in a semi-steady state whereby the pressure decreased at a steady state, with no drift
effects in the model.
A set of sensitivities were conducted on parameters related to relative permeability curves. These parameters seemed to have
no effect on the ester production profile, conveying evidence of flow reversibility. Inputting different ROS through end-point
scaling seemed to cause a shift in the alcohol concentration profiles. This is because a higher ROS has a higher retardation factor
meaning there is a larger number of moles of the partitioning tracer in the oil phase in comparison to the water phase, hence the
ester propagates into the reservoir at a lower speed which leads to it producing first as it remains closer to the well. Most
Figure 31 Tracer concentration profiles for the ester (left) and the
alcohol (right) for water mobility-permeability case (in BH).
Figure 32 Tracer concentration profiles for the ester (left) and
the alcohol (right) for water mobility-permeability case (in TH).

reservoirs have heterogeneities that can lead to different ROS and rock types. To simulate a SWCT test in such an environment
two different ROS regions were created and simulated using the tracer model. The results showed that the chromatographic
separation of the two tracers during back production was only observed when there was a large difference between the ROS in
the two regions. This was manifested as two peaks within the alcohol concentration profiles. The minimum point at which this
could be observed was when ∆𝑆 𝑜𝑟 > 0.24 (Fig. 22). The back calculation validation step can be applied to this model to further
analyse the results. This consisted of back calculating the ROS using equation 3. When back calculating for a case where a
second peak protrudes (Fig. 33) - in the case where the TH has a residual of 0.4, and the BH has a residual of 0.1 - the first peak
back calculates a ROS of 0.37 which gives a good match with a relative error of around 7.5%. The second peak has a ROS of
0.12 which also gives an acceptable match with a relative error of 17%. The overestimation observed for the second peak is due
to the addition of heterogeneity which is not considered in the analytical model. These results show that the first peak is
attributable to the TH whereas the second peak is representative of the ROS in the BH of the model.
The next parameter that was tested was the water relative permeability end-point (Krw). This is an important parameter in
determining fluid mobility at a pore scale level. Krw governs the flow of water relative to the other phases within the system.
The results showed that when increasing the Krw for the BH of the model, the second peak increases in prominence. On the
other hand, when increasing the Krw for the TH of the model, the second peak is smeared out with it only being visible when
Krw for the TH is 0.1.
When calculating the flux in the different layers it was found that when the Krw for the BH was 0.1, there was a higher flux
of tracer that was going into the HROS region due to its higher relative mobility. When Krw for BH was increased to 1, this was
reversed and flux of tracer injection was higher in the LROS region. This shows that a higher Krw does in fact lead to a higher
flux of tracer into the BH of the model which has the lower residual oil saturation of 0.1. The ratio of the total fluxes between
the two regions for any of the cases is equal to the Krw ratio between the two regions, showing that the simplification of Darcy’s
law is valid in our model. The tracer production is proportional to the flux.
From the above analysis we can conclude that when the flow of the mobile fluid is greater in the LROS region than in the
HROS region, and when the ROS of the two regions are far apart, it seems that these conditions permits the visibility of the
second peak.
𝐾𝑟𝑤[𝐿𝑅𝑂𝑆] > 𝐾𝑟𝑤 [𝐻𝑅𝑂𝑆]
For a single rock-type, the relative permeability for water reaches its maximum at the ROS, post imbibition. This means that
at residual oil the respective Krw is the maximum mobility of water that can be attained within that system for that specific rock
type. Capillary pressures were not considered in our tests due to the fact that a main assumption for this test is that we are
operating at residual conditions.
Field data (Bennion et al. 2002) for a range of different lithology such as Sandstone (Fig.34) and Carbonate (Fig. 35) were
collected to ascertain whether a relationship between the ROS, Krw and lithology can be established. Plotting Krw vs. Sor gave
very sporadic results from which a correlation could not be observed.
Introduction of permeability heterogeneity in the different water mobility sensitivity case seemed to mask the presence of
the peaks depending on which part of the model the permeability was being varied in.
Figure 33 Back calculation schematic for the ROS of 0.4 and 0.1.
Figure 34 Krw vs. ROS for Sandstone
Reservoirs.
Figure 35 Krw vs. Sor for Carbonate
Reservoirs.

When compared with results reported in literature (de Zwart et al. 2011, Abdulla et al. 2013), it is evident that the shift in
ROS and variation of Krw in the latter cases is similar to the results collected from SWCT tests to evaluate Low Salinity Water
Flood and Alkaline Surfactant Polymer EOR studies. The multiple peaks in our study showed similarities with the tracer
production profiles that were observed by Abdulla et al. (2013) in the Greater Burgan field, Kuwait where a first trial SWCT
test was conducted before and after low-salinity water flooding to measure the change in the ROS. The observed multiple peaks
were interpreted as characteristic responses from layers with different ROS.
The interpretation of the results compiled in this study was more difficult than expected as seen with the permeability
heterogeneity case which seemed to mask the effects of chromatographic separation, conveying flow reversibility effects. This
paper showed that most of the time the anticipated profile of the results is not always in-line with what actually occurs, illustrating
the complexity of this topic.
Conclusions
From the literature review it was found that little focus has been placed on implementing SWCT tests to aid in characterising
reservoir heterogeneity, with the last publication being made in 1989 (Descant et al. 1989). This prompted the compilation of
this study with a view to add a better insight into both the implementation of SWCT tests and its application in reservoir
characterisation. In terms of the numerical models, a good match exists between the commercial black oil simulator results and
those from the compositional simulator, with small variances due to the implementation of a work around in the tracer model.
Test design optimisation is possible on parameters such as the KP and soaking period without jeopardising the match between
the numerical and analytical models. It was found that variations in permeability heterogeneity in a semi-steady state model
could not induce flow irreversibility and hence could not convey heterogeneity markers in the tracer production profiles but the
calculated ROS still showed a good match with the actual residual. Variations in relative permeability curve parameters such as
ROS and Krw created the ‘double peak effect’ with each peak corresponding to a different rock region (different ROS). Post
imbibition, the ROS governs the maximum Krw for a certain rock type and as the mobile phase and carrier fluid in SWCT tests
is water, this drastically effects the movement of tracers within the reservoir and into each layer due to variances in the flux.
These results were comparable with those collected from field data.
Recommendations for Further Study
1. It is recommended to apply this study in reservoirs where oil is mobile as it is difficult to always ensure that the reservoir
is at residual and hence the effects of mobile oil will be of significance. The reaction model and analytical tracer model will have
to be modified to account for another mobile phase.
2. We also recommend expanding on this study through its application in fractured reservoirs and faults.
3. Having seen the results collected from small scale variances in heterogeneities, this should be applied to a more complex
heterogeneous reservoir model such as a carbonate reservoir to analyse tracer responses and benchmark them against those
collected in this study.
4. Investigation of the effects of fluid drift, to ascertain whether permeability heterogeneity can be observed in line with the
results found Descant et al. (1989).
Nomenclature
𝐴 = Area 𝑄1 = Darcy’s Flux
𝐴 𝑟 = Reaction Rate Constant 𝑅= Rate of reaction
𝐵(𝑖)= Formation volume factor of host phase (i) 𝑅1= Gas constant
𝐵𝑜 = Oil formation volume factor rm3
Reservoir Volume
𝐵 𝑤 = Water formation volume factor 𝑆(𝑖) = Saturation of host phase (i)
bbls/d Barrels per day 𝑆 𝑜𝑟 = Residual oil saturation
𝐶 𝑎
= Adsorbed tracer concentration sm3
/d Standard cubic meter per day
𝐶(𝑖) = Flowing tracer concentration in host phase (i) sm3
Surface Volume
𝐶 𝑝,(𝑖) = Concentration of partitioning tracer, phase (i) 𝑇 = Transmissibility
𝑐 𝑟𝑖
𝑛𝑟𝑖
= Component block concentration t(i) = Time of flight of tracer
𝐷𝑐 = Tracer diffusion coefficient 𝑡 𝑛𝑝 = Time of flight of the non-partitioning tracer
𝐷𝐹= Diffusivity 𝑡 𝑝 = Time of flight of the partitioning tracer
𝐷𝑧 = Cell center depth tsoak = Soak time
𝐸𝑟 = Activation energy 𝑡𝑡𝑟𝑎𝑛𝑠𝑖𝑡 = Transit time
𝐹 = Flow rate of host phase (i) 𝑇1 = Temperature
ft = Fractional Time 𝑉 = Block pore volume
𝑔 = Acceleration due to gravity 𝑉𝑏= Bulk volume
𝑘= Permeability 𝑉𝐶= Volume of control
KABS = Absolute permeability 𝑉(𝑖),𝑟𝑒𝑠 = Reservoir volume of fluid (i) in place
𝑘 𝑒𝑓𝑓 = Effective permeability 𝑣 𝑝= Velocity of the partitioning tracer

𝐾 𝐻 = Hydrolysis coefficient 𝑥 𝐶5
𝑠𝑡𝑟𝑒𝑎𝑚
= Stream fraction of component C5
𝑘 𝑝 = Partition coefficient 𝜌= Fluid Density
𝑘 𝑟 = Host phase relative permeability 𝜌 𝑟= Mass density of the rock formation
𝐿 = Thickness 𝛷= Porosity
𝑀= Molecular weight 𝜇 = Host phase viscosity
𝑛 𝐶5
= Number of moles of tracer (C5) injected 𝜇(𝑖)= Viscosity of the host phase (i)
𝑛 𝑝,(𝑖)= Number of partitioning tracers in phase (i) 𝛿 = Change in a certain quantity
𝑃1= Pressure 𝜆= Tracer decay constant
𝑃 = Host phase pressure ∆𝑇𝑠𝑙𝑢𝑔= Slug total injection time
𝑄(𝑖)= Production rate of host phase (i) 𝛽 = Retardation Factor
𝑄 𝑃= Volume of fluid produced at
Subscripts
𝐶5 = Water/partitioned tracer 𝑟1 = Forward reaction in equilibrium
𝑓= ‘Free’ host phase (water) 𝑟2 = Backward reaction in equilibrium
𝑛𝑝 = Non-Partitioning Tracer 𝑟3 = Hydrolysis reaction
𝑜, 𝑟𝑒𝑠 = Oil in place in the reservoir s ‘Solution’ host phase (oil)
𝑜= Oil Phase 𝑤= Water phase
𝑜𝑟= Residual oil 𝑤, 𝑟𝑒𝑠 = Water in place in the reservoir
𝑝 = Partitioning Tracer 𝑧 = Depth
𝑟= Rock formation
References
Abdulla, F., Hashem, S., Abdulraheem, B., Al-Naqi, M., Al-Qattan, A., and John, H., 2013. First EOR Trial using Low Salinity Water Injection in the Greater
Burgan Field, Kuwait. SPE 164341, proceedings of the SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain.
Al-Mutairi, F., Tiwari, S., Baroon, B., Abdullah, M., Pathak, A., and Gammiero, A., 2015. Simulation of Single Well Chemical Tracer Tests Conducted in
Carbonate Reservoir. SPE 17528, proceedings of the SPE Kuwait Oil & Gas Show and Conference, Mishref, Kuwait.
Al-Shalabi, E. W., Luo, H., Delshad, M., and Sepehrnoori, K., 2015. Single-Well Chemical Tracer Modeling of Low Salinity Water Injection in Carbonates.
SPE 173994, proceedings at the SPE Western Regional Meeting held in Garden Grove, California, USA, 27-30 April.
Bennion, D. B., Thomas, F. B., Schulmeister, B. E., and Ma. T., 2002. A Correlation of Water and Gas-Oil Relative Permeability Properties for Various Western
Canadian Sandstone and Carbonate Oil Producing Formations. PETSOC 2002-066, proceedings of the Petroleum Society’s Canadian International
Petroleum Conference, Calgary, Alberta, Canada.
Claude, C. E., Jr., 1971. Method of Determining Fluid Saturations in Reservoirs. U.S. Patent No 3,590,923.
Cubillos, H., Yuste, E., Bozorgzadeh, M., Montes, J., Mayorga, H., Bonilla, S., Quintanilla, G., Lezana, P., Panadero, A., and Romero, P., 2015. The Value of
Inter-well and Single Well Tracer Technology for De-Risking and Optimizing a CEOR Process- Caracara Field Case. SPE 174397, proceedings at the
EUROPEC 2015 held in Madrid, Spain.
De Zwart, A. H., Stoll, W. M., Boerrigter, P. M., van Batenburg, D. W., and Al Harthy, S. S. A., 2011. Numerical Interpretation of Single Well Chemical
Tracer Tests for ASP Injection. SPE 141557, proceedings held at the SPE Middle East Oil and Gas Show and Conference held in Manama, Bahrain.
Deans, H. A., 1971. Method of Determining Fluid Saturations in Reservoirs. U.S. Patent No. 3,623.842.
Deans, H. A., and Carlisle, C. T., 1986. Single Well Chemical Tracer Test Handbook, second edition. Laramie, Wyoming. Chemical Tracers, Inc., 2-19.
Deans, H. A., and Carlisle, C. T., 1986. Single-Well Tracer Test in Complex Pore Systems. SPE 14886, proceedings at the SPE /DOE Fifth Symposium on
Enhanced Oil Recovery held in Tulsa, Oklahoma, USA.
Descant, F., Blackwell, R., and Pope, G. A., 1989. The use of Single Well Tracer Testing to Estimate Heterogeneity. SPE 20303, SPE Journal, University of
Texas.
DeZabala, E., Parekh, B., Solis, H., Choudhary, M., Armentrout, L., and Carlisle, C., 2011. Application of Single Well Chemical Tracer Tests to Determine
Remaining Oil Saturation in Deepwater Turbidite Reservoirs. SPE 147099, proceedings at the SPE Annual Technical Conference and Exhibition held in
Denver, Colorado, USA.
Du, Y., and Guan, L., 2005. Interwell Tracer Tests: Lessons Learned From Past Field Studies. SPE 93140, proceedings at the Asia Pacific Oil & Gas
Conference and Exhibition held in Jakarta, Indonesia.
Fanchi, J R., 2005. Principles of Applied Reservoir Simulation, third edition. Golden, USA. Elsevier, Part 2, 141-160.
Huseby, O., Sagen, J., and Dugstad, Ø., 2012. Single Well Chemical Tracer Tests- Fast and Accurate Simulations. SPE 155608, proceedings at the SPE EOR
Conference at Oil and Gas West Asia held in Muscat, Oman.
Jerauld, G. R., Mohammadi, H., and Webb, K. J., 2010. Interpreting Single Well Chemical Tracer Tests. SPE 129724, proceedings of the ASPE Improved Oil
Recovery Symposium held in Tulsa, Oklahoma, USA.
Jin, L., Jamili, A., and Harwell, J. H., 2015. Modeling and Interpretation of Single Well Chemical Tracer Tests (SWCTT) for pre and post Chemical EOR in
two High Salinity Reservoirs. SPE 173618, proceedings at the SPE Production and Operations Symposium held in Oklahoma, USA.
Khaledialidusti, R.., Kleppe, J., and Skrettingland, K., 2015. Numerical Interpretation of Single Well Chemical Tracer (SWCT) Tests to Determine Residual Oil
Saturation in Snorre Reservoir. SPE 174378, proceedings at the EUROPEC 2015 held in Madrid, Spain.
Pathak, P., Fitz, D. E., and Babcock, P. K., 2011. Residual Oil Saturation Determination for EOR Projects in a Mature West Texas Carbonate Field. SPE 145229,
proceedings at the SPE Enhance Oil Recover Conference held in Kuala Lumpur, Malaysia.
Schlumberger Simulation Software Manuals 2015.1. 2015. Houston, Texas. Schlumberger.
Skrettingland, K., Holt, T., Tweheyo, M. T., and Skjevrak, I., 2011. Snorre Low Salinity-Water Injection- Coreflooding Experiments and Single-Well Field
Pilot”. SPE 129877, proceedings at the SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA.
Teklu, T. W., Brown, J. S., Kazemi, H., Graves, R. M., and AlSumaiti, A. M., 2013. Residual Oil Saturation Determination- Case Studies in Sandstone and
Carbonate Reservoirs. SPE 164825, proceedings at the EAGE Annual Conference and Exhibition incorporating SPE Europe held in London, UK.
Tomich, J. F., and Deans, H. A., 1975. Method to Measure Fluid Drift and Immobile Phase Saturation. U.S. Patent No. 3,902,362.
Tomich, J. F., Dalton, R. L., Deans, H. A., and Shallenberger, L. K., 1973. Single-Well Tracer Method to Measure Residual Oil Saturation. SPE 3792,
proceedings at SPE Symposium on Improved Oil Recovery held in Tulsa, Oklahoma, USA.

Appendix A
Critical Literature Review
Paper n Year Title Journal Authors Contribution
U.S. Patent
No
3,590,923
1971 Method of Determining
Fluid Saturations in
Reservoirs
U.S. Patents Deans, H. A. The first paper to present the
injection of partitioning tracers
into the reservoir to help in
collecting measurements of the
residual oil saturation (i.e. the
first paper to present SWCT
tests as it is understood today).
SPE 3792 1973 Single-Well Tracer
Method to Measure
SPE
International
Tomich, J. F.,
Deans, H. A.,
And
Shallenberger,
L. K.
First paper to present an
analytical method for describing
the process of SWCT tests. This
model is then proven by through
a comparison study with four
field tests.
SPE 20303 1989 The Use Of Single Well
Tracer Testing To
Estimate Heterogeneity
SPE
International
Descant, F.,
Blackwell, R.,
Pope, G. A., and
Sepehrnoori, K.
First paper to explore the
application of SWCT tests to
estimate permeability contrasts
in a layered reservoir.
SPE 8838 1980 Single-Well Tracer Tests
for Evaluating Chemical
Enhanced Oil Recovery
Processes
SPE
International
Sheely, Q. C.,
Jr., and
Baldwin, D. E.,
Jr.
First to propose the testing of an
enhanced oil recovery process
(surfactant injection) using
SWCT tests in the Muddy Field,
Wyoming. This involved the
injection of multiple reactive
tracers.
SPE
129877
2011 Snorre Low-Salinity-
Water Injection-
Coreflooding
Experiments and Single-
Well Field Pilot
SPE
International
Skrettingland,
K.,
Holt, T.,
Tweheyo, M.
T., and
Skjevrak, I.
First to utilise SWCT test field
pilots in measuring remaining
oil saturation post seawater
flooding and lowsal flooding on
the Snorre field.
SPE
174397
2015 The Value of Inter-well
and Single Well Tracer
Technology for De-
Risking and Optimizing
a CEOR Process-
Caracara Field Case
SPE
International
Cubillos, H.,
Yuste, E.,
Bozorgzadeh,
M.,
Montes, J.,
Mayorga, H.,
Bonilla, S.,
Quintanilla, G.,
Lezana, P.,
Panadero, A.,
and Romero, P.
A study involving the
implementation of ASP
injection (EOR) in the Caracara
Sur Field, Colombia. This
involved the application of
SWCT tests before and after the
surfactant injection to monitor
the residual oil saturation and
evaluate the effectiveness of the
EOR process.
SPE 28591 1997 Chemical Tracer Studies
To Determine Water
Saturation at Prudhoe
Bay
SPE
International
Deans, H. A.,
and Mut, A. D.
First to apply the SWCT method
in measuring the residual water
saturation in the Ivishak
reservoir, Prudhoe Bay.
SPE 14886 1986 Single-Well Tracer Test
in Complex Pore
Systems
SPE
International
Deans, H. A.,
and Carlisle, C.
T.
Development of a model that
reproduces the unique features
associated with SWCT tests in
carbonate formations. It

involved modifying the "dead-
end" pore model in order to
reproduce features from
different chemical tracer tests in
complex pore systems, such as
West Texas Dolomites,
Canadian Reefs and other
carbonate formations.
SPE 2152 1968 New Single-Well Test
for Determining Vertical
Permeability
SPE
International
William, A., and
Burns, Jr.
First to devise a well test for in-
situ measurements of vertical
permeability.
SPE 718 1963 Theory of Tracer Flow SPE
International
Bischoff, K. B.,
and Worcester,
D. A.
The first analysis of the theory
of the dispersion of tracers in
flowing streams.
A model was devised which
accounts for both molecular
diffusion and turbulent mixing.
ARMA-
87-0453
1987 Estimation of fracture
aperture using hydraulic
and tracer tests
U.S.
Symposium
on Rock
Mechanics
Smith, L., and
Mase, C. W.
First paper to estimate fracture
aperture using tracer tests.
SPE 5840 1976 Description of Field
Tests To Determine
by Single-Well Tracer
Method
SPE
International
Sheely, C. Q.,
Jr.
This details the field tests
conducted for Single-Well
Tracer Tests.
SPE
155608
2012 Single Well Chemical
Tracer Tests - Fast and
Accurate Simulations
SPE
International
Huseby, O.,
Sagen, J., and
Dugstad, Ø.
Simplification of the SWCT test
model. A fast post-processing
tracer simulation technique is
introduced to solve single well
tracer transport in real-life
reservoir cases.
SPE
174378
2015 Numerical Interpretation
of Single Well Chemical
Tracer (SWCT) Tests to
Determine Residual Oil
Saturation in Snorre
Reservoir
SPE
International
Khaledialidusti,
R.., Kleppe, J.,
and
Skrettingland,
K.
Numerical interpretation of
SWCT test after high salinity
water flooding in the Snorre
Reservoir.
- 1986 Single Well Chemical
Tracer Test Handbook,
second edition
Chemical
Tracers, Inc
Handbook
Deans, H. A.,
And Carlisle, C.
T.
A handbook expanding on the
analytical model presented by
Deans (1971). Explains the
structure and time frame of
SWCT tests and also compares
numerical results to case studies
on field tests.
SPE
129724
2010 Interpreting Single Well
Chemical Tracer Tests
SPE
International
Jerauld, G. R.,
Mohammadi,
H., and Webb,
K. J.
Analysis of SWCT tests in high
and low salinity water flooded
reservoirs.
SPE
124614
2009 Determining Reservoir
Properties and Flood
Performance From
Tracer Test Analysis
SPE
International
Shook, G. M.,
Pope, G. A., and
Asakawa, K.
Describes new analysis methods
developed recently. Compares
between analytical and
experimental data.

SPE
175282
2015 Simulation of Single
Well Chemical Tracer
Tests Conducted in
Carbonate Reservoir
SPE
International
Al-Mutairi, F.,
Tiwari, S.,
Baroon, B.,
Abdullah, M.,
Pathak, A., and
Gammiero, A.
This paper presents the findings
from a simulation conducted of
SWCT tests in a Carbonate
Reservoir and compares it to
results collected from SWCT
test that were carried out in
conjunction with water flood
and ASP EOR techniques on the
SAMA field in Kuwait.
PETSOC-
98-01-06
1998 Well-to-well Tracer
Tests and Permeability
Heterogeneity
Journal of
Canadian
Petroleum
Technology
Ghori, S. G.,
and Heller, J. P.
First paper to describe the
possibility of obtaining
quantitative information about
the permeability heterogeneity
of underground reservoirs from
well-to-well tracer tests.
IPTC-
14560
2012 Single-Well Chemical
Tracer Test Experience
in the Gulf of Guinea to
Determine Remaining
Oil Saturation
International
Petroleum
Technology
Conference
Romero, C.,
Agenet, N.,
Lesage, A. N.,
and
Cassou, G.
First paper to focus on the
results of the SWCT test carried
out on an offshore field in the
Gulf of Guinea and how the
information was used to
improve the assessment of the
reservoir's current residual oil
saturation.
SPE 77874 2002 Advance on the Tracer
Test Technology Among
Wells
SPE
International
Bingyu, J.,
Xinguang, S.,
Qinglin, W.,
Qun, L.,
Anjian, L.,
Tongjing, L.
First to review the development
of tracer test technology amongst
wells in oil fields.

U.S. Patent No 3,590,923 (1971)
Method of Determining Fluid Saturations in Reservoirs
Authors: Deans, H. A.
Objective of Paper:
To present a method for determining the residual oil saturation and water saturations of a reservoir through the chromatographic
separation of injected partition tracers.
Contribution to the understanding of Single-Well Chemical Tracer tests in Heterogeneous Reservoirs:
The first paper to present the injection of partitioning tracers into the reservoir to help in collecting measurements of the residual
oil saturation (i.e. the first paper to present SWCT tests as it is understood today).
Methodology used:
 Injecting a partitioning tracer into a reservoir at residual oil which partitions into the oil and water phases.
 A secondary tracer is produced in-situ during shut-in which is only soluble in water.
 Since both these tracers have different partition coefficients between the carrier fluid and the mobile phase, they are
chromatographically retarded in their passage through the formation by different amounts which is a function of the
saturation of the immobile phase.
Conclusion reached:
 A method is devised for determining relative amounts of two fluid phases in a subterranean reservoir formation.
Comments:
 Deans, H. A is regarded as the forefather of SWCT tests.
 An important paper which laid out the fundamentals of SWCT tests
 It gauged the application of tracers in reservoir operations to ascertain the residual oil saturation.

SPE 3792 (1973)
Single-Well Tracer Method to Measure Residual Oil Saturation
Authors: Tomich, J. F., Deans, H. A., and Shallenberger, L. K.
Objective of Paper:
To present a mathematical model that describes the functioning of SWCT tests and apply this to a numerical model whose results
was then compared with that gathered from the field.
Contribution to the understanding of Single-Well Chemical Tracer tests in Heterogeneous Reservoirs:
First paper to present an analytical method for describing the process of SWCT tests. This model is then benchmarked against
results collected from four field tests. Developed a single-well chemical tracer model for measuring the residual oi saturation.
Methodology used:
 Introduced mathematical model correlating the retardation factor to the velocity of the tracer in a specific phase. The
important assumptions made for this model to work were that the fluids are incompressible, the oil phase is immobile and
that the chemical reaction occurs only in the water phase.
 Enhanced on the idea of chromatographic separation of tracers using this mathematical model by showing that two tracers
that have different distribution coefficients will have different velocities, and will hence separate in a manner that is
analogous to that in a chromatographic column.
 This model was then applied to measuring the residual oil saturation of a field using the different arrival times of the tracers.
This was modelled in a numerical simulator and benchmarked against measurements that were taken from four different
field tests.
Conclusion reached:
 Development of a new single-well chemical tracer method/model that can measure the residual oil saturation and this has
been proved using field data.
 Realisation of the wide range of applications of SWCT tests,
Comments:
 An important paper in presenting the analytical method that is still being used to this day to validate numerical models of
SWCT tests and was subsequently relied upon in this study.

Single-Well Chemical Tracer Tests in Heterogeneous Reservoirs

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