Real-Time Monitoring and Performance Optimization of Steam.pdf

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Real-Time Monitoring and Performance Optimization of Steam Injection in Heavy Oil Reservoirs
Using Fiber Optic Sensing and Integrated Predictive Simulation Models
Pet Petro Chem Eng J
Real-Time Monitoring and Performance Optimization of Steam
Injection in Heavy Oil Reservoirs Using Fiber Optic Sensing and
Integrated Predictive Simulation Models
Alexandrescu AC, Halafawi M* and Avram L
Petroleum-Gas University of Ploiești, Romania
*Corresponding author: Mohamed Halafawi, Petroleum-Gas University of Ploiești, Romania,
Tel: 0738525943; Email: halafawi@upg-ploiesti.ro
Research Article
Volume 9 Issue 2
Received Date: June 17, 2025
Published Date: June 30, 2025
DOI: 10.23880/ppej-16000410
Abstract
Real-time surveillance that identifies departures from safe operating limits and permits timely remedial action is essential
to effective reservoir management. This study uses distributed fiber-optic sensing (FOS) to assess the effectiveness of steam-
assistedgravitydrainage(SAGD)inSuplacField,aheavy-oilreservoir(8–13°API,500–1000cP).Wells#1and#2,twohorizontal
injectors, are observed for two full steam-injection cycles that lasted 130 and 150 days, respectively. Together with downhole
pressure and steam-quality measurements, high-resolution FOS data yielded 3D temperature profiles down the wellbore,
which were then analyzed to determine out-of-range operating situations and quantify reservoir reaction. Comparisons
across cycles showed how temperature fronts changed, how steam quality deteriorated, and how output indicators like water
cut and oil rate drop changed. In order to estimate temperature, pressure, and steam quality under the same injection settings,
a linked wellbore/reservoir model was constructed in Prosper to simulate fluid characteristics in the tubing and annulus. The
methodology was validated and areas where real-time FOS data might improve simulation assumptions were highlighted by
the model outputs' reasonable agreement with FOS observations. An effective method for optimizing heavy-oil steam injection
that improves recovery efficiency and operating safety is the combined FOS–simulation approach.
Keywords: QFiber Optic Sensing; Prosper model; Pressure; Temperature; Thermal EOR; Steam quality
Introduction
After primary and secondary recovery, enhanced oil
recovery (EOR) is usually the last stage of oil production.
While secondary recovery uses water injection to sustain
pressure when natural energy diminishes, primary recovery
depends on the reservoir’s natural pressure or artificial lift
to extract oil. Tertiary recovery, or EOR, is used when these
techniques become economically inefficient, frequently as a
result of increased water output. EOR increases oil output
by extracting more oil using thermal, chemical, or other
means. EOR can be utilized at any point when conventional
techniques are inadequate, even though it is often utilized
near the end of a field’s life [1]. The two main procedures in
thermal EOR are steam stimulation (cyclic steam injection)
and steam-flooding. Steam and hot-water flooding are the

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Halafawi M, et al. Real-Time Monitoring and Performance Optimization of Steam Injection in Heavy Oil
Reservoirs Using Fiber Optic Sensing and Integrated Predictive Simulation Models. Pet Petro Chem Eng
J 2025, 9(2): 000410.
Copyright© Halafawi M, et al.
predominant approaches. These techniques improve flow
to producing wells by decreasing the viscosity of heavy oils.
The most efficient EOR technology is steam-flooding, which
recovers around 410 million barrels of oil per day worldwide.
In-situ combustion and steam stimulation recover 57 million
barrels per day, while all other EOR techniques recover 220
million barrels per day [2]. Heavy oil production frequently
uses thermal EOR, which includes steam injection methods
including In-Situ Combustion (ISC), Cyclic Steam Stimulation
(CSS), and Steam Assisted Gravity Drainage (SAGD). The
geographic resolution of traditional monitoring systems is
frequently restricted. Distributed, real-time temperature
and acoustic data collecting along the whole wellbore has
been made possible by the advent of Fiber Optic Sensing
(FOS), namely Distributed Temperature Sensing (DTS) and
Distributed Acoustic Sensing (DAS) [3,4].
The fundamental idea of a FO system is based on a
transmitted light signal in which the FO cable (measured
light) acts as a carrier for light, sending out a light signal from
the transmitter on the surface. Reflected light is produced by
light pulses and has an extremely low attenuation index. In
order to reach the receiver at the surface, the backscattered
light waves must return via the optic medium. The data is
decoded, or converted to temperature, pressure, acoustic,
or strain data, and then recorded in a database based on the
technology used. This process compares the backscattered
light signal with the light pulse that was first fired from the
transmitter (reference beam). FOS Techniques are widely
applicable in thermal EOR. There are two technologies
for FOS operations. DTS Technique is the initial technique.
Raman scattering in optical fibers is used by the DTS
technology to monitor temperature profiles [3]. In SAGD
operations, DTS has been very helpful in tracking the
growth of the steam chamber and optimizing the SOR [5].
Field research conducted in Alberta showed how DTS was
able to record the steam chamber’s temporal development,
allowing for modifications in steam injection to increase
the effectiveness of oil recovery [5]. The second is DAS,
which uses Rayleigh backscattering to create continuous
acoustic sensors out of optical fibers [6]. By offering dynamic
flow monitoring, DAS enhances DTS. DAS applications for
detecting steam flow patterns during injection in thermal
EOR processes were demonstrated by Mateeva, et al. [6]. A
multi-physics sensing platform is provided by hybrid DTS/
DAS systems, which combine DTS and DAS [7]. According
to recent advancements, integrated fiber systems can
increase reservoir interpretation and control by providing
temperature, acoustic, and strain data [7,8].
FOS Applications in Thermal EOR Methods are several.
DTS makes it possible to track the compliance of the steam
chamber in SAGD wells in real time, which helps to prevent
steam channeling and optimize production techniques [9].
Operators may dynamically modify steam injection profiles
thanks to continuous feedback from DTS, which lowers
heat losses and enhances SOR [9]. DTS has also been used
in CSS projects to track the steam soak phase and evaluate
production-related thermal behavior [10]. This type of
monitoring optimizes following cycles and enables the
identification of thermal inefficiencies [10]. Nonetheless,
DTS detects sharp temperature gradients that correlate
to combustion zones in ISC operations in order to follow
thermal fronts [11]. According to field findings, DTS assists
operators in tracking the combustion process and modifying
injection plans as necessary [11].
ChallengesinFOSDeploymentforThermalEORrepresent
a key-element during applications. Despite its benefits, FOS
has certain drawbacks, most notably fiber deterioration at
temperatures above 300°C [12]. Furthermore, logistical and
financial challenges arise due to the intricacy of installation
in horizontal or deviated wells [13]. Moreover, sophisticated
machine learning algorithms are needed to analyze the
enormous volumes of data produced by DTS/DAS [14]. Using
digital twins and machine learning in conjunction with FOS
data to automate decision-making is one of the emerging
trends[15].Inordertoimprovesensorsurvivalforprolonged
thermal EOR operations, research on high-temperature fiber
coatings is also ongoing [16]. Furthermore, the development
of 4D monitoring systems for subsurface operations appears
to be promising when DTS, DAS, and DSS are applied together
[17].
For better understanding the field monitoring and
evaluation philosophy, it is advisable to know the best way to
measure the changed parameters, how to measure them, and
their effect on all field studies including wells and reservoir.
By comprehending the observed variables and suggesting
remedial action in real time, reservoir management seeks
to maximize process efficiency in the reservoir. One crucial
component of production facilities that might endanger
the safe growth of a project is the detection of out-of-range
operating situations. During injection and production cycles,
continuous real-time temperature and pressure monitoring
for heavy and viscous oil well production optimization aids
in the optimization and improvement of CSS scenarios,
provides information about heat transfer by convection and
conduction into the reservoir, records any breakthroughs
in steam or water, records any evidence of combustion gas
breakthroughs, enhances comprehension of the processes
occurring inside the borehole (downhole), and provide
information for reservoir modeling and other optimization
strategies.
Therefore, the main objective of this paper is to
investigate temperature-pressure distribution using FOS in
Suplac field wells during injecting a steam into a heavy crude

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oilreservoir.Twohorizontaldrilledwellsareusedininjecting
steam into the reservoir. Two complete injection steam
cycles are done for each, 130 and 150 days for well # 1 and
2 respectively. A comparison study between the two cycles
is done for both well including pressure, temperature, steam
quality, production parameters over the specified period.
In addition, a model is developed using Prosper software
to predict the fluid well, tubing and annuli temperature
and pressure, and steam quality for steam injections with
certain parameters. Thus, it is important to review the FOS
applications, and EOR improvements. The Suplac field data
are also described. Wells # 1 &2 data are described as well.
Results and discussions are made.
A Chronological Review of Fiber Optic
Sensing in Oil and Gas Fields
Thermal EOR monitoring capabilities have been
significantly enhanced by FOS, especially DTS and DAS
[3,4]. FOS technologies are essential to creating intelligent,
effective, and sustainable thermal EOR systems because of
developments in fiber durability and AI-driven analytics
[18]. In order to continue researching and apply FOS
methods efficiently, a sequential review is made to study the
historical studies, approaches, applications techniques of
FOS especially in oil and gas field as shown in Table 1.
Author Published
Year
Study/Approach/Technique/ Methodology/Application/Field Study
Early Developments and Establishments
Culshaw [19] 2005 Presenting the study that demonstrated the early idea of using FOS with
health systems for critical infrastructure Distributed sensing was highlighted
in Culshaw’s discussion of the growing potential of FOS in structural health
monitoring
Measures [20] 2006 Investigating phase sensitivity in interferometric FOS. A fundamental grasp of
several interferometric methods that are essential for precision sensing was given.
Kersey [21] 2007 Reviewing FBG sensors for structural applications in a groundbreaking manner.
The scalability of FBGs for multiplexed sensing in huge structures was highlighted
in his work.
Rao, et al. [22] 2008 Emphasizing strain monitoring while concentrating on FOS for aeronautical
constructions. In challenging aeronautical settings, they illustrated the benefits of
FO over conventional electrical sensors.
Bao & Chen [23] 2010 Introducing DTS applications in oil and gas in which the groundwork for using
DTS in well integrity and production optimization was presented.
Growth into Distributed Sensing and Oil & Gas
Hartog [24] 2011 Examining downhole DAS applications. For DAS technology used in underground
energy businesses, this work was a foundational resource.
Soto, et al. [25] 2012 Developing a real-time DTS for monitoring geothermal reservoirs. Their invention
gave operators the ability to dynamically map temperature changes in geothermal
areas.
Masoudi & Newson [26] 2013 Presenting recent developments in vibration sensors for distributed optical fibers.
They showed how structural vibrations might be detected across long distances
using FOs.
Liehr, et al. [27] 2014 Presenting a new optical frequency domain reflectometry (OFD) technique for
dispersed sensing with greater resolution. By enabling sub-centimeter resolution
measurements, OFD advanced the accuracy of distributed sensing.
Froggatt & Moore [28] 2015 Investigating DS using Rayleigh with enhanced spatial resolution. Using common
telecom fibers, their technique enabled extremely precise distributed strain and
temperature measurements.
Methods of High Resolution and Diversity of Sensing

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Zhan, et al. [29] 2016 Creating strain sensors using FOs that have better multiplexing capabilities. By
enhancing sensor multiplexing, the study made it possible to install dense sensors
for structural health monitoring.
Parker, et al. [30] 2017 Transforming geophysical surveys by demonstrating DAS for seismic applications.
Their innovation demonstrated that dense seismic arrays might be created from
already-existing FO networks.
Selker, et al. [31] 2018 Expanding DTS uses in agriculture and hydrology. This study demonstrated DTS’s
adaptability by demonstrating its use in irrigation management and soil moisture
monitoring.
Zhang, et al. [32] 2019 Presenting the combined FOSs for temperature and pressure monitoring at the
same time. They demonstrated how to integrate sensors for intricate settings like
deep-water wells.
Fidanboylu & Efendioglu
[33]
2020 Outlining novel construction techniques and materials for FSs. Advances in sensor
design, such as new polymer coatings for enhanced sensitivity, were covered in
detail in this study.
New Developments and Future Prospects
Farhadiroushan, et al.
[34]
2021 Addressing the DAS in distributed strain imaging for CO2 sequestration
monitoring. The potential of DAS in identifying tiny strain signals linked to carbon
storage integrity was demonstrated by their work.
Smith & Farahi [35] 2022 Developing FOS devices that combine Raman scattering with FBG. The goal of
this hybridisation was to solve the trade-off that FO devices face between sensing
range and spatial resolution.
Wang, et al. [36] 2023 Presenting an improved understanding of FOS data using machine learning. The
interpretation of massive datasets produced by dispersed sensors was greatly
enhanced by machine learning techniques.
Xu, et al. [37] 2024 Building ultrafast FOS for high-speed rail structural integrity monitoring.
Predictive maintenance was made possible by their system, which gave vital
infrastructure real-time notifications.
Huang, et al. [38] 2025 Revealing advances in the use of innovative photonic crystal fibers for distributed
sensing spatial resolution. This development opened up new possibilities for
precise monitoring by pushing spatial resolutions to previously unheard-of limits.
Table 1: A review of FOS techniques and application.
Study Methodology and Research
Procedures
The goal of the project is to combine coupled wellbore–
reservoir simulation with real-time distributed FOS in order
to measure and optimize SAGD performance in the Suplacu
heavy oil field. Early detection of out-of-range operating
situations, optimization of the steam injection method, and
enhancement of recovery efficiency while maintaining well
integrity are the objectives.
Experimental Design
Two horizontal injector wells, Well #1 and Well #2,
each outfitted with DTS and DAS systems to track SAGD
performance, will be used in a field project in the Suplacu
heavy-oil field as part of the experimental design. Near-
continuous thermal and acoustic profiling of the wellbores is
made possible by Well #1’s 24 DTS sensors and one pressure
gauge and Well #2’s 14 DTS sensors and one pressure
gauge. With baseline injection rates of 90 t/d for Well #1
and 55 t/d for Well #2, both wells go through two full SAGD
cycles (130 and 150 days) under controlled steam injection
programs, with wellhead conditions of 12–13 bar and
220°C. Throughout the project lifespan, the setup supports
integrated monitoring and model calibration by simulating
real-time field operations and enabling real-time data
gathering on temperature, acoustic signatures, pressure,
flow rate, water cut, and steam quality.
DataAcquisition Protocol
1. Installation & Calibration
• Install armored optical cables beneath the casing; during

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cold shut-in, conduct a baseline DTS/DAS assessment.
• Adjust the FO temperature at three depths in relation to
the reference platinum RTDs.
2. Live Injection/Production Logging
• Stream data to office servers via RMDA (resolve previous
1 s overlap issue by aligning acquisition to 10 min
cadence); record DTS every 10 minutes and DAS every
1 minute.
• Record synchronous P-T-Q data from the wellhead.
3. Quality Control
• Perform automated detection of sensor drift and
dropouts > 0.2 bar or > 3 °C; initiates field check within
24 hours.
• Prevent the previously mentioned data gaps by
maintaining a backup power source.
Modelling & Analysis Workflow
Step Action
Coupled
Simulation
Build Prosperlinked wellbore/reservoir model; input measured rock/fluid properties (sandstone 2.65 g cm⁻³,
k = 1.06 BTU hr⁻¹ ft⁻¹ °F⁻¹; heavy oil μ = 500–1 000 cP, 8–13 °API).
History
Matching
Iteratively tune thermal conductivity, nearwellbore permeability and steamquality curve until simulated P–T
envelopes converge to FO data within ±5 °C and ±0.5 bar. Address prior mismatch by adjusting gascap heat
capacity and gauge bias.
RealTime
Diagnostics
Apply machinelearning anomaly detector on DTS to flag steam breakthroughs, channelling or aquifer coning
(e.g., persistent > 20 °C gradient over 10 m).
Optimization
Loop
Every 30 d, recompute steamoil ratio (SOR), predict nextcycle steam allocation, and update Prosper boundary
conditions.
Table 2: Steps of model and study analysis.
Validation & Performance Metrics
• Thermal Front Tracking: Crossplot measured vs.
simulated 3D temperature isotherms; target R² > 0.9.
• Production Response: Compare predicted and actual
oil rate, water cut, and cumulative liquids; accept error
≤ 10 %.
• Steam Quality: Validate downhole quality (goal 70–
80 %) against Prosper outputs; reconcile if FO indicates
subcooled water injection.
Work Procedures (Stepwise)
• Project Kickoff & HSE review (Week 0–1)
• Fiberoptic installation & baseline survey (Week 2–4)
• Cycle 1 steam injection & live monitoring
(Day 0–130/150)
• Cycle 1 data cleanup, QC and preliminary model match
(parallel, monthly)
• Intercycle analysis & parameter update (2 weeks
downtime)
• Cycle 2 steam injection with optimized schedule
• Comprehensive history match and sensitivity study
(postCycle 2, 4 weeks)
• Decision workshop—steam strategy and sensor
expansion
• Final report submission
ThisconcisemethodologyleveragescontinuousFOSdata
to close the loop between field observations and predictive
simulation, providing a robust framework for optimizing
heavyoil steam injection in Suplacu and analogous reservoirs.
Field Data Description: Suplacu Heavy Oil
Field
Suplacu field is an onshore oil reservoir produced heavy
oil.Inaddition,itoneofRomania’soldestandmostsignificant
crude oil reservoirs and it is situated in Bihor County, close
to the Hungarian border. Due to the production of heavy and
viscous oil, which is a feature of mature deposits, exploitation
started in the 1960s. The data is divided into many sections
to depict the technical aspects of this field. First of all, it is
situated in Bihor County, in the northwest of Romania. The
field is situated at an average elevation of 180–220 meters
across a mountainous terrain. Second, this mature field was
identified in the 1960s, according to geological data. Neogene
(Pliocene) formations are the source of the productive strata.
Low permeability and high hydrocarbon saturation are
characteristics of porous sandstone formations that contain
crude oil. The productive layer ranges in depth from around
600 to 1000 meters.
Third, the type of produced fluid is heavy crude oil. It is
characterized by low API grade (8–13° API), high viscosity
(at reservoir temperature, more than 500–1000 cp), and it
includes significant levels of paraffin, sulfur, and asphalt[39].
Figure 1 shows the variation of oil viscosity with temperature

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from different wells. Fourth, Recovering and Exploiting
Among the techniques are traditional primary exploitation
through rod pumps and progressive cavity pumps (PCPs).
The reservoir yields Low production because to the crude
oil’s high viscosity.
Tertiary and Secondary Recovery (EOR): In vertical wells,
CSS, was used. An industrial project using SAGD on two
horizontal wells was also implemented and the hot water
and/or superheated steam injection were used. Figure 2
demonstrates the history of recovery mechanisms until
reaching to EOR.
Figure 1: Variation of reservoir fluid viscosity with temperature for different wells.
Infrastructures include plants that produce steam,
separators for oil and water and fluid heating systems, and
pipesforinjectionandextractionthatarethermallyinsulated.
The historical production data shows that the average
daily production rate (per active well) is 3–10 m³. Despite
its advanced decline, the field had previously produced
approximately 10 million tonnes of crude oil. The first PCP
metal pumps, which can tolerate temperatures beyond
150°C, were used in Romania. Fiber optic sensors were also
used for measuring SAGD well pressure and temperature in
order to monitor the injection and reservoir temperatures
in real time. Maximum bottom hole temperature (BHT), in
steam injection phase is 230o
C (446 o
F), and the maximum
Wellhead Temperature (WHT) is 250°C.
Figure 2: Recovery mechanisms’ history of the reservoir from primary to EOR.

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Figure 3: Structural map with drilled wells and FOS wells 1 and 2 used in steam injection.
Data gathering & Data analysis of FOS
Application
Two wells, well #1 (24+1 sensors) and well #2 (14+1
sensors), each on a separate cluster (PAD), have fiber
optics installed. Flexible interfaces are readily set up to
deliver downhole data to the office system and facilitate
communication between the site and the surface data
gathering system. A field developed model update resolved
the problem with the remote monitoring data acquisition
(RMDA) system, where the data acquisition frequency was
set at one second and some data was not recorded because of
wavelength overlap. The RMDA system is set up at the same
frequency as the system office, which has a 10-minute data
acquisition frequency. Data analysis includes examination
of CSS design and other downhole steam injection
circumstances. The amount of steam and the duration of
steaming are determined by field experience; When to shut
downthewellforafreshCSS,minimumwellheadtemperature
for a new steam cycle or minimum oil output; There is no link
between the steam table and the wellhead characteristics.
Next, Questions to pose: Where does water production
originate? The aquifer may or may not be the source of the
water production (maybe through combustion?). From the
water injection procedure, where is the water going? Lastly,
suggestions regarding CSS optimization for the operational
team are done.
The following pictures, which come from a thermal
simulation or monitoring system during steam injection,
display the temperature distribution along a wellbore profile
for well # 1 and 2. The well trajectory covered in grey is a
horizontalprofileonthecolormap,whichshowstemperature
intensity with cooler areas in blue/green and hotter areas in
red/orange. The well path’s labeled values emphasize heat
dispersion and possible steam breakthrough or channeling
by displaying temperature readings at different recorded
depths (Figures 4 & 5). In the horizontal wellbore profile, the
9 5/8” casing is close to base reservoir until an inclination of
93+˚ (parallel to reservoir). Horizontal slotted liner is placed
in open hole through the oil sands zone (Figure 6). Steam
injection is done through a 3 1/2” tubing, thermal expansion
joint and thermal packer. The oil production is performed
through conventional rod pump.
Figure 4: Horizontal well profile and temperature distribution for well #1.

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Figure 5: Horizontal well profile and temperature distribution for well #2.
Figure 6: Horizontal wellbore trajectory used during steam injection.
Type of fluid Fluid Specific Gravity
(SP. Gr)
Fluid Conductivity (BTU/hr/
ft/F)
Fluid Specific Heat Capacity
(BTU/lb/F)
Water (Salinity<10000) 1.00 0.35 1.00
Water (Salinity<200000) 1.08 0.345 1.02
Water (Salinity>200000) 1.15 0.34 1.04
Heavy Oil 0.96 0.083 0.49
Medium Oil 0.90 0.0825 0.50
Light Oil 0.84 0.0815 0.50
Gas 0.01 0.0215 0.26
Type of Rock Rock Density
(g/cc)
Fluid Conductivity (BTU/hr/
ft/F)
Fluid Specific Heat Capacity
(BTU/lb/F)
Sandstone (S.S) 2.65 1.06 0.183
Shale 2.40 0.70 0.224

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Limestone (L.S) 2.71 0.54 0.202
Dolomite 2.87 1.00 0.219
Halite 2.17 2.80 0.219
Anhydrite 2.96 0.75 0.265
Gypsum 2.32 0.75 0.259
Lignite 1.50 2.00 0.30
Volcanic 2.65 1.60 0.20
Fixed Value 2.61 1.10 0.20
Table 3: Fluid and rock properties used in modelling.
Field Results and Discussions: Real
Time Measurements, Simulations and
Comparisons
Real-time surveillance that identifies departures from
safe operating limits and permits timely remedial action
is essential to effective reservoir management. This study
uses distributed fiber-optic sensing (FOS) to evaluate the
effectiveness of SAGD in Suplacu Field with a heavy oil
reservoir of 8–13 °API, and 500–1000 cp viscosity. Wells
#1 and #2, two horizontal injectors, were observed for
two full steam-injection cycles. Table 3 shows the rock and
fluid properties of Suplacu reservoir that were used during
modeling with prosper. Figures 1 through 6 show the field
map, well profile and data used in this study.
The temperature distribution in a well over time –
almost one year– and depth is depicted in the 3D graph
shown in Figure 7, most likely during a steam injection or
other thermal recovery procedure. The z-axis displays
temperature o
C, colour-coded in bands (from 90–100°C up
to 190–200°C), the y-axis displays depth at different sensor
sites (varying from ~215 m to ~610 m), and the x-axis
depicts time (from mid-November to mid-September). The
temperature zones’ contours and layers indicate changes
in heat penetration at various depths and periods as well
as the movement of the thermal front. Temperature profile
spikes and plateaus signify active steam injection times,
well shut-in times, or injection technique adjustments. For
tracking thermal efficiency and seeing heat breakout zones
or steam channeling over time, this visualization is helpful.
FO-based temperature and pressure monitoring in well #1
throughout a year of steam-assisted gravity drainage (SAGD)
operations is shown in Figure 8. Sharp rises in wellhead
injection pressure (up to about 13.5 bar) and accompanying
temperature spikes reaching 220°C, with ΔT values of 56°C
and 30°C, emphasize two main steam injection cycles.
Effective steam propagation is shown by steady baseline
conditions and distinct heat and pressure responses during
injection, as shown by FO data. Additionally, the graphic
shows a short loss of data due to an RMS (Remote Monitoring
System) breakdown that occurred over a period of 1.5 years.
All things considered, the figure illustrates how FOS makes
it possible for ongoing downhole monitoring for real-time
diagnostics, well integrity evaluation, and thermal EOR
optimization.
Figure 7: 3D Temperature monitoring vs FO for well #1.

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Figure 8: Pressure and temperature monitoring vs FOS method for well #1.
Figures 9 through 13 show how temperature and
pressure changed throughout the course of 130 operating
days along a slotted liner, signifying two production cycles.
The five graphs, which are divided into different stages—
after 15, 30, 60, 91, and 130 days—show the temperature
and pressure behavior along a slotted liner in a wellbore
during the period of 130 days of production. Temperature
(°C) and pressure (bar) are shown against the slotted liner’s
measured depth (MD) in each graph. Interestingly, two
manufacturing cycles are recorded; the first is often indicated
by red text, while the second is shown by purple. Water cut
(WC) and liquid production rate (Qliq) are monitored for
each cycle as production time goes on, and cumulative liquid
production is shown to measure reservoir performance.
Along with the injection tubing termination sites for each
cycle, the graphs also indicate important completions and
formations like “SAND” and “CARBONATE”. The temperature
profile gradually decreases over the course of the five
time periods, particularly in the vicinity of the well’s toe
(in the direction of greater MD), suggesting thermal front
movement brought on by production drawdown and cooling
by produced fluids. Higher Qliq and WC values are first seen,
particularly in the second cycle. But with time, WC improves
as Qliq declines, indicating better oil cut as water output
declines. By day 130, WC is at 55% and Qliq for both cycles is
between 14 and 17 m³/d. The pressure seems to stabilize at
5 bar or less, and the temperature becomes more consistent.
As is common in thermal recovery operations, the data show
front stabilization, production drop, and reservoir cooling.
A mature thermal sweep and decreased thermal losses are
implied by the improvement in WC and comparatively stable
temperatures at later stages, which suggest a manufacturing
regime that is steadily stabilizing.
Figure 9: Profile comparison between cycles for well #1 after 15 days.

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The next graphs (Figures 14&15) show the temperature
and pressure profiles along a wellbore during steam injection
as observed by FO and modeled (Prosper). The y-axes
display pressure (right) and temperature/steam quality
(left), while the x-axis shows the well length (in measured
depth). In both pictures, temperature, steam quality, and
pressure are shown versus measured depth, comparing
calculated Prosper outputs with measured FO data for Well
#1 during steam injection. The graphs provide a number of
temperature profiles, such as FO, Prosper fluid, annulus, and
casing temperatures, in addition to Prosper-calculated fluid
pressureandsteamquality.Importantwellcomponents,such
as the liner hanger, casing shoe, and injection tubing string
end, are indicated by comments. One noteworthy finding in
both figures is that, as the FO data suggests, Prosper model
overestimates temperatures above the reservoir. The results
also demonstrate the steam entering the reservoir at about
220 m MD and indicate constant injection circumstances (90
tons of steam per day, WHP = 12.84 pressure, THP = 220°C).
In model 1, it is possible that the reservoir temperature is
warmer than initially because of gases from combustion
+ previous steam cycles. In this model, the pressure is
reasonably matched, but the temperature is off. Also, it was
not possible to derive a model with both matched (Figure
14). In the second model (Figure 15), the temperature is
matched, but the pressure is not matched. Also, Prosper
will predict saturation conditions, which was not what was
recorded. In both unmatched models, the calculated steam
quality is ~80%.
Figure 14: Prosper modelling vs FO for well #1 (Model 1).

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Figure 15: Prosper modelling vs FO for well #1 (Model 2).
During injection through well #1 (Figures 8 through
14), the temperature is uniform along the slotted liner. It
appears that the well shoe remains warmer than the rest
of the well section, even when the well is shut down. This
corresponds also to the Panonien sand section, after the
long section of carbonate. Furthermore, the steam is not
injected uniformly in the reservoir, but rather to zones with
least resistivity (good rock properties & good fluid mobility:
either warm zone, or zone with gas), or/and closer to the
end of the injection string. With increase of gas production,
the temperature profile does not change and the gas could
also come from this most productive area. With high water
cut, the temperatures in the middle well section are smaller
than with lower water cut. It is not possible to see where the
water corresponding to the increasing water cut is coming
from – most likely also from the most productive zone, i.e. the
well toe. During steam injection, the measured temperature
and pressure do not correspond to the saturation conditions
– big concerns that only hot water is injected. To get to the
saturations conditions, the pressure should be ~2bar lower,
could it be measurement error (~22% error)? Could the
pressure gauge be disrupted by high temperatures? Prosper
model could not reproduce those measurements. For the
given injection conditions it would predict a ~80% steam
quality.
During steam injection in well #1, the temperatures are
uniform in the slotted liner. It seems that more steam goes
into reservoir sections with least resistivity and close to the
end of the injection tubing string. The following points are
extracted based on the results:
• The location of the end of the injection tubing string has
an effect
• Preliminary Prosper models would predict: steam
quality ~80% - 60% to tubing shoe for injection rate 90-
50 t/d
• Differences between measured wellhead temperature in
time of injection phase and FO sensor temperature are
around 40 -50°C
• Differences between measured wellhead pressure and
FO sensor pressure are around 3-4 bar in CSS time
• Measurements in well #1 during steam injection did
not correspond to saturation conditions, which should
indicate hot water injection only
• This is the reason why Prosper model needs
improvements regarding the pressure and temperature
measurement error margin or further investigations
over reservoir parameters
• It’s hard to qualitatively assess what is happening
toward the well heel. The measured temperature may
reflect more the well flow from the other well section,
than the inflow from the reservoir;
1. Other pressure sensors are required apart from toe (i.e.
to heel );
2. Need of the permanent electrical power source to avoid
wrong readings after electrical breaks
Anothersteaminjectedwellcalledwell#2isalsostudied
as shown from Figures 16 through 23. The temperature
profile after 16 days of production for the 2 cycles is different
(Figure 16). This could indicate that the location of the
injection tubing string had an effect. It seems that injecting
where mud losses where encountered helped cleaning this
part of the well, as the temperatures in this zone are higher
than in the previous cycle. After 29 days of production
(Figure 17), both cycles showed a hot producing zone at
~460m MD. For the 2nd
cycle the cold zone at 480-490m MD
can be seen, but then the well is relatively warmer toward
the toe, which could confirm its contribution – except for the
last sensor that records smaller temperature. However, the
difference in the temperature profile of the 2 cycles toward

14
J 2025, 9(2): 000410.
the well toe is confirmed over the next five periods: 39, 60,
90, 120, and 150 days (Figures 18 through 22). Moreover,
another model was built using Prosper software to predict
well temperatures and pressures along the wellbore profile,
then compare with the FO measurements for well # 2. Figure
23 show a slightly match between Prosper model and FO
data measured. However, there are still lots of things to sort
in Prosper, but for this rate, Petroleum Expert 5 correlation
gives a reasonable result. The calculated steam quality is
~68%. The problem with Prosper is to match the range of
rates with the same correlation. This could not be achieved.
Duringinjectioninwell#2,thetemperaturesareuniform
along the well during steam injection. However having the
steam in the slotted liner during injection, does not guaranty
that the steam is injected uniformly along the well section.
Changing the end of the injection tubing string location in the
well had an effect in the temperature profile. It seems that
for the 2nd
cycle, the zone that had severe mud losses during
drilling has been cleaned and has a better contribution. There
is a zone at ~460m MD that remains warmer than the rest of
the well – it corresponds to the end of the tubing string of the
Aug 14 steam injection – but it can also be observed for the 2nd
cycle. Either this zone is hot because it received directly a lot
of steam in Aug14, i.e. this is due to the injection tubing string
location Also, it has particularly good properties as shown
in the well log, so it is easier for the steam to get into this
zone. With time a colder zone can be observed from 480 to
490m MD. This is most likely aquifer water coning. Another
one could be briefly observed at 430m MD, but seemed later
masked either by fluid coming from the other well sections
or because its contribution is small. The 1st
well section
[380-430] mMD where logs interpretation attributes good
porosity does not seem to contribute much to production. A
Prosper model would predict a 68% downhole steam quality
during the February 15 steam injection.

15
J 2025, 9(2): 000410.

16
J 2025, 9(2): 000410.
Figure 23: Prosper modelling vs FO for well #2.

17
J 2025, 9(2): 000410.
Conclusions
In conclusion, the Suplacu field is served as a
prototype for other fields of a similar kind in Romania
and is a significant testing ground for cutting-edge heavy
oil exploitation technology. The extraction of heavy crude
oil at advanced maturity circumstances, when thermal
processes and specialized pumps are necessary to sustain
economic production, is shown by this reservoir. In heavy-
oil fields like Suplacu, this study has demonstrated that real-
time distributed fiber-optic sensing (FOS), which includes
distributed temperature sensing (DTS) and distributed
acoustic sensing (DAS), is a useful and effective method
for improving SAGD performance monitoring. In order to
accurately visualize the evolution of the steam front and
identify injection irregularities, the field pilot showed that
FOS systems can consistently record high-resolution thermal
and acoustic data over the whole length of horizontal injector
wells. Because it provides geographically and temporally rich
information, this continuous monitoring technique performs
noticeably better than conventional point-based sensors.
More precise history matching and a better comprehension
of reservoir behavior, including the identification of
heterogeneities and steam channeling, were made possible
by the integration of FOS data with a coupled wellbore-
reservoir simulation model. The efficacy of the existing data
infrastructure, which was shown to be reliable enough for
prolonged monitoring periods, was also confirmed by the
pilot. Despite early problems with sensor power failures
and data synchronization, they were resolved later in the
trial. The technical viability of applying FOS in established,
onshore heavy-oil operations was generally validated by
the field study. The system was useful not just for real-time
monitoring but also for optimizing well performance during
the SAGD cycle and guiding steam injection schemes.
Recommendations
Based on the results and discussions, the
recommendations are divided into the main six items as
following:
• Wider Deployment of FOS Systems: To provide complete
temperature and flow profiling, assist cross-well
diagnostics, and enhance compliance control, DTS and
DAS systems should be installed across injector and
producer wells.
• Improved Instrumentation: To further enhance coupled
model calibration and improve pressure diagnostics,
installpressuregaugesatthewells’toeandheelportions.
• Workflows for Automated Optimization: Use automated
simulation-feedback loops with real-time FOS data to
continually modify steam injection rates and identify
operational irregularities early.
• Advanced Data Analytics: Use machine learning
techniques to analyze and analyze vast amounts of
auditory and thermal data, allowing for automatic
anomaly identification (such as coning, steam
breakthrough, or crossflow).
• Scalability Assessment: To evaluate the wider
applicability and scalability of this integrated monitoring
and modeling process, conduct further pilots in other
Suplacu field regions and in heavy-oil reservoirs with
comparable geology.
• Increase Power and Data Redundancy: Reduce the
possibility of data gaps during crucial injection
or production stages by increasing system power
redundancy and communication dependability. This will
guarantee continuous data collecting.
Operators may considerably enhance reservoir
management, lessen their impact on the environment by
using steam more efficiently, and increase the productive
life of heavy-oil assets by integrating high-resolution sensor
technologies with dynamic simulation and optimization.
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