Aplicações de Sensoriamento Subaquático para Exploração Pré-Sal Brasileira

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Deepwater Applications for Brazilian Pre-Salt Exploration Using Underwater Sensor Networks 
 
F. J. L. Ribeiro 1,2 A. C. P. Pedroza 2, L. H. M. K. Costa 1 
1 GTA/PEE/COPPE - DEL/POLI - Universidade Federal do Rio de Janeiro (UFRJ) 
2 PETROBRAS – Petróleo Brasileiro S.A. 
Rio de Janeiro - Brazil 
 
 
 
 
 
 
ABSTRACT 
 
This paper proposes an underwater positioning system built with 
sensors distributed over the submarine infrastructure responsible for the 
oil production, which will be located by trilateration of acoustic signals 
emitted by units (vessels and platforms) with known coordinates. 
However, the acoustic signals needed to the calculation of the position 
depend on the vessels within sensors range. Thus, this work 
investigates the system behavior, analyzing the acoustic signals 
available for sensors using the ONE (Opportunistic Network 
Environment) simulator and scenarios based on the Brazilian offshore 
oil exploration area. 
 
KEY WORDS: Underwater communications; acoustic; sensor 
networks; positioning; deepwater monitoring. 
 
INTRODUCTION 
 
The new frontier of oil exploration in Brazil is located in a region 200 
km off the coast called pre-salt which comprises an area of 
approximately 800 km in length and 200 km in width, encompassing 
three basins (Santos, Campos and Espírito Santo) (Carminatti et al., 
2008). The operation in water depths up to 3000 m is the challenge to 
be overcome, requiring technologies to support the operational control 
in this extreme environment. Therefore, new communication 
techniques should be used to obtain information of subsea 
infrastructure. 
 
Operation and maintenance of the infrastructure responsible for the 
uptake and distribution of oil in the underwater environment are 
extremely complex due to the harsh conditions. In addition to the 
adverse chemical conditions of the salty water, the seabed in Brazil is 
irregular in some places presenting an extreme slope, which exposes 
the structures of exploitation to great instability. 
 
Underwater monitoring networks can provide a continuous verification 
on the operating conditions of underwater infrastructure of oil 
exploration. The accurate measurement of underwater position can 
allow the detection of seabed instability. Therefore, the use of sensor 
networks on seabed for underwater positioning monitoring enables 
verification of submarine displacements that can cause damage to 
structures, helping to prevent oil spills in the marine environment. 
However, underwater communication is subject to several limitations 
that cause interference in the propagation of the acoustic signal, 
resulting in small binary transmission rates. Thus, the development of 
an architecture based on underwater communication with 
Delay/Disruption Tolerant Networks presented in Cerf et al. (2007) 
becomes a necessity due to the limitations imposed by that subsea 
environment. 
 
The development of underwater positioning system provides for the 
availability of the subsea mapping with references issued by units on 
the surface, which allows the location with geographic coordinates and 
depth. Therefore, the positioning will be useful in monitoring the 
activities of installation and operation of subsea structures such as the 
deployment of equipment at the head of oil wells, which should be 
installed in compliance with the perfect alignment for docking. In 
addition to allow monitoring the movement of remotely operated 
vehicles (ROV - Remotely Operated Vehicle) and autonomous 
underwater vehicles (AUV - Autonomous Underwater Vehicle), widely 
used in oil exploration activities. 
 
This paper proposes a deepwater positioning system based on a DTN 
(Delay / Disruption Tolerant Network) monitoring network, built with 
acoustic sensors installed over the subsea infrastructure, which will be 
located by trilateration of acoustic signals emitted by logistic-support 
vessels and oil production units. The sensors must receive at least three 
signals of points with known coordinates (vessels and platforms) in 
order to perform the trilateration of acoustic signals received. The 
objective is to analyze the feasibility and behavior of the system 
reproducing the movement of vessels in the ONE (Opportunistic 
Network Environment) simulator (Keränen, Ott and Kärkkäinen, 2009), 
demonstrating that in scenarios compatible with the offshore 
environment, the sensors will be able to calculate their position. 
 
The remainder of this paper is organized as follows: Section 2 presents 
related works and Section 3, the location methods used in positioning 
systems. Section 4 introduces the proposed underwater positioning 
system. In Section 5 we show the analysis of the monitoring system. 
Section 6 presents the results, while Section 7 concludes the paper and 
present topics for future work. 
 
RELATED WORK 
 
Urick (1983) presents the basic principles of underwater acoustic 
transmission, emphasizing the characteristics that influence the speed 
of sound in water such as the pressure (depth), density, temperature and 
salinity. The results presented by Sozer, Stojanovic and Proakis (2000) 
on underwater communication open the way for using networks of 
wireless communications in the underwater environment. The study of 
underwater communication performed by Stojanovic (2006) presents 
the underwater features that influence the transmission of data. 
 
The underwater communication challenges are described in Heidemann 
et al. (2006), where the difficulties imposed by the media and the 
constraints of the acoustic channel, such as interference, bandwidth, 
reflections, error rate and range are highlighted. An architecture for 
underwater networks and its requirements was proposed in Akyildiz, 
Pompili and Melodia (2007). The study identifies different approaches 
for medium access control, network and transport layers, showing an 
evaluation of different protocols. Another analysis of the problems of 
underwater sensor networks is also found in Liu, Zhou and Cui (2008). 
A routing protocol based on the hydraulic pressure for underwater 
sensor networks proposed by Lee et al. (2010) explores the levels of 
measured pressure to forward the data to buoys on the surface. 
 
A sensor network for coral reefs monitoring is presented in Vasilescu et 
al. (2005). This acoustic network utilizes AUVs to collect data, mixing 
short-range optical communication with acoustic communication. 
Penteado, Costa and Pedroza (2010) proposed a sensor network that 
obtains oceanographic data to monitor ocean currents. This network is 
composed of fixed acoustic sensors that communicate with a sink 
responsible for external communication. Recently, Ribeiro, Pedroza 
and Costa (2011) proposed an underwater monitoring system in a DTN 
network composed of sensors distributed over the subsea pipelines. 
Data collection is undertaken by vessels used for logistic support of the 
oil exploration. 
 
Some proposals based on the location determination use the 
characteristics of the signals propagation based on the position 
calculation. The GPS (Global Positioning System) is the best known 
example. This system is based on satellite radio navigation, consisting 
of 24 satellites, equally spaced in six orbital planes, located 20,200 km 
above the Earth, that transmit two coded signals, one for civilian use 
and one for military use (Kaplan, 1996). The satellites of the system 
transmit navigation messages to the GPS receiver, which calculates its 
position in 3D - latitude, longitude and altitude. 
 
Other proposals may not use satellites for location. The proposal of 
Song (1994) that performs the location using signals from the cellular 
system was the precursor for the study of location through GSM 
cellular system proposed by Varshavsky et al. (2006). Gunnarsson and 
Gustafsson (2005) discuss the basic possibilities associated with mobile 
positioning in wireless networks with a sensor fusion approach and 
model-based filtering. A tracking systemfor vehicular ad hoc networks 
is proposed in Boukerche et al. (2008), showing how to combine the 
techniques of vehicle location by data fusion to provide a more robust 
tracking system. 
 
Unlike the works presented, we propose an underwater positioning 
system composed of sensors that are located by trilateration of 
underwater acoustic signals emitted by vessels and platforms. Thus, we 
study the system ability to determine sensor location. No reference was 
found in the available literature that addresses the provision of a 
positioning system specific for deepwater environment considering 
mobility and the constraints of offshore oil exploration area. 
 
METHODS OF LOCATION 
 
The position identification of a mobile or fixed element in a coordinate 
system can be accomplished in several ways. Whatever the coordinate 
system adopted, these methods require knowledge of location of at least 
three reference points in the system and metrics to estimate the distance 
of the unknown node from the reference points and their coordinates. 
Thus, the location methods generally have two basic components: an 
estimation of the distance and computational calculation of the position. 
 
Estimating the Distance 
 
The estimated distance is obtained from the characteristics of the 
transmitted signal between two nodes. This estimate can be 
implemented based on the Received Signal Strength Indicator (RSSI), 
Angle of Arrival (AOA), Time of Arrival (TOA), or a combination of 
these methods. 
 
The method to estimate the distance by measuring the received signal 
strength (RSSI) is based on verification of the fading of the transmitted 
signal (Savvides et al., 2001). However, this indicator is highly 
influenced by noises, obstacles, and the type of antenna, which makes it 
hard to model mathematically. The main source of error is the effect of 
shadowing and fading caused by signal multipath. In systems where the 
power of underwater acoustic signal is controlled to save the battery of 
the sensors, the measurement can suffer changes (Stojanovic, 2006). 
 
The angle of arrival (AOA) of received signal can also be used by 
location systems (Niculescu and Nath, 2003). This angle is used to 
estimate the distance between the transmitter and receiver. The AOA 
estimation is done using directive antennas or a set of receivers 
arranged uniformly. The dispersion of the signal around the receiver 
and transmitter can change the measurement of the angle of arrival, 
limiting the range of the measuring devices. The need for extra 
hardware and interferences, restrict the use of this method. In 
underwater acoustic communication, transmissions are surrounded by 
sources of interference that affect the reception, making the approach 
AOA possibly impractical in this environment. 
 
The last technique of distance estimation uses the measurement of the 
signal propagation time (Savvides et al., 2001). This method can work 
with the difference in propagation time of multiple signals 
(Differentiate Time of Arrival - TDOA), or measuring the propagation 
time of a single signal (Time of Arrival - TOA). 
 
In TDOA approach, the differences in the arrival times for the 
transmitted two signals are used. These signals must have different 
propagation speeds, such as radio/ultrasound or radio/acoustic. This 
option can not be used in the underwater environment due to the lack of 
alternatives to the transmission of the second signal. 
 
The measurement by the TOA method estimates the distance between 
the transmitter and receiver finding the unidirectional propagation time. 
Geometrically, this gives a circle centered on the point of reference, 
where the receiver should be. In this case, the distance between two 
nodes is directly proportional to the time the signal takes to propagate 
from one point to another. Thus, if a signal was sent at time t1 and 
reached the receiving node at the time t2, the distance between the 
transmitter and receiver is d = c (t2 - t1), where c is the speed of signal 
propagation and t1 and t2 are the times when the signal was sent and 
received. The accuracy of this estimate type depends on the 
synchronization between the nodes. 
 
 
Calculation of Position 
 
The location methods use the measures of distances performed by the 
node up to three or more reference points, running a series of 
calculations to define the position. The process of defining the position 
depends on the used method, which may be based on trilateration, 
multilateration or triangulation. 
 
The trilateration method is the most basic and intuitive one, which is 
calculated considering the geographical location of a node through the 
intersection of three circles. To estimate a position, at least three 
reference points associated with the respective distances to the node 
(d1, d2, d3) (Savvides et al., 2001) are required. The circles formed by 
the position and distance of each point of reference, can be represented 
by the formula (x - xr)2 + (y - yr)2 = dr2, where (x, y) is the position that 
want to be computed, (xr, yr) is the position of the reference node r, and 
dr is the distance from the node to the reference point r. Thus, the 
location of the node is the point of intersection of three circles, 
considering the hypothetical case with no errors in the estimates of 
distances (Fig. 1 (a)) or in an area of intersection if considered the real 
case with measurement errors (Fig. 1 (b)). 
 
 
 
Fig. 1. (a) Hypothetical model of trilateration; (b) realistic model of 
trilateration; (c) multilateration; (d) triangulation. 
 
The multilateration is a generalization of trilateration (Patwari et al., 
2005), which can be used when there are more than three reference 
points (Fig. 1 (c)). In this case, the calculation uses a system of 
equations for determining the location. The number of operations is 
much higher, increasing the processing load on the node, which usually 
makes it difficult to use. 
 
The triangulation is applied when using the technique based on 
measuring the angle of arrival rather than distances (Savvides et al., 
2001). The node estimates its angle to each of the three reference 
points, and based on these angles and the reference positions (which 
form a triangle), calculates its position using simple trigonometric 
relationships (Fig. 1 (d)). 
 
Application Requirements 
 
Generally, to estimate a position, a node uses at least three estimates of 
distances, each with an associated error. Although desirable, the 
accuracy is not the only important feature in choosing the most 
appropriate method for the application. Other factors should be 
considered, such as cost, hardware, processing and energy. Thus, the 
method to set the position depends on the application requirements. 
 
Underwater positioning applications that work together with 
monitoring systems may use lower precision techniques. For these 
applications, the precise location of the sensor in some cases is 
unnecessary, being important only the neighborhood information. 
However, in underwater activities requiring precision, a specific 
positioning system fitted with close and dedicated reference points can 
be used, increasing the measurement accuracy. 
 
UNDERWATER POSITIONING SYSTEM 
 
The use of acoustic sensor networks for monitoring the position in 
underwater environments is possible since the sensor receives the 
coordinates of three reference points and is able to estimate distances. 
The mapping of the underwater position is particularly important in 
monitoring the release of new lines of submarine pipelines (Solano et 
al., 2007), in monitoring of installation maneuvers and movement of 
subsea structures (Chung, 2010). 
 
The Campos Basin area is approximately 115,000 km2 and can be 
divided into two regions, called transition and exploration regions (Fig. 
2). Transition region is a transition area that has logistic-support vessels 
moving between the coast and oil fields. Exploration region is the 
producing oilarea that concentrates most of the submarine 
infrastructure, responsible for the operation of the oil production fields. 
In this region, the logistic-support vessels perform a specific routine of 
units supply and anchoring. 
 
 
 
Fig. 2. Navigation area of logistic-support vessels. 
 
Logistic-support vessels have GPS system, radio communication, and 
in many cases, satellite link and their routes are coincident with the 
subsea infrastructure, becoming together with the production units 
(platforms) the most appropriate option to be reference points needed 
for determining the positioning of sensors. 
 
The long distances and dispersion of offshore installations affect the 
vessels density within sensors range. Thus, these vessels may not be 
reachable to sensors at all times, making the movement of vessels very 
important to the availability of reference points for the positioning and 
capture of information from the sensors. In this case, the use of logistic-
support vessels and the units of production can increase the chances of 
having at least three reference units for sensors. 
 
 
Underwater Communications 
 
The sound speed in water is about 1500 m/s, which is four times faster 
than the sound speed in air, but still five orders of magnitude smaller 
than electromagnetic waves speed in the air (Urick, 1983). This feature 
implies a latency of approximately 0.67 s/km. Thus, the speed of sound 
in the ocean is an important oceanographic variable that determines the 
acoustic wave propagation. Many empirical equations for calculating 
the sound speed in this environment have been developed using the 
values of salinity, temperature and pressure/depth of the water. A 
simplified expression for the sound speed in water is shown in Equation 
1 (Su, Venkstesan and Li, 2010), where c is the sound speed in water, T 
is the temperature (in Celsius), S is salinity (in parts per thousand) and 
z is the depth (m). For most cases, this equation is sufficiently accurate. 
 
c = 1449.2+4.6T–0.055T2+0.00029T3+(1.34–0.01T)(S–35)+0.016z (1) 
 
The acoustic waves are mainly interfered by noise caused by 
reflections, obstacles and turbulence, but the loss caused by absorption 
is the factor that most influences the propagation time. Still, it is 
possible to transmit information via acoustic modems which reaches up 
to 5 km range with transmission rates of 5 kbps [Stojanovic 2006]. 
 
Underwater Sensor Network 
 
Underwater sensor network is based on nodes equipped with sensors 
and acoustic modems also used in Vasilescu et al. (2005). The nodes 
can communicate with mobile nodes (vessels) or fixed nodes 
(platforms) to receive information from the coordinates of reference 
points. This network type, allows nodes to maintain an autonomous 
operation, where sensors are responsible for the position calculation 
and sending messages to the control center. Thus, sensors can be used 
to monitor positioning even in real time, but this operation must be 
planned to ensure longer life of the sensors. 
 
The positioning sensor network is part of the monitoring system 
proposed by (Ribeiro, Pedroza and Costa, 2011), where all mobile 
nodes can collect the position information in DTN (Delay/Disruption 
Tolerant Network) underwater domain. The sensors are installed in the 
subsea infrastructure and programmed to calculate the position 
whenever three reference points become available. Each of these 
samples is encoded into a data packet, usually around 1 kbyte. 
 
The positioning system uses a DTN sensor network, but the localization 
process is performed through a single communication hop between the 
reference points (vessels and platforms) and underwater sensors. This 
feature is related to the need to conserve battery life of the sensors. 
Thus, on the positioning process, the sensor only receives the signals 
and calculates the reference position not needing to transmit data, 
saving energy. 
 
Location Algorithm 
 
The representation of the localization process performed by underwater 
positioning system is shown in Fig. 3, where the references to 
positioning were given by a logistic-support vessel, a FPSO (Floating 
Production Storage and Offloading) and a platform. The point in the 
center of the triangle on the surface is the representation of underwater 
sensor position in geographic coordinates. This representation together 
with the depth information allows mapping underwater objects by 
following a well-known location model. 
 
 
 
Fig. 3. Deepwater positioning system. 
 
The sensor can be activated with a control message that initiates the 
process of calculating the position. Once the sensor is activated, it 
estimates the distance and calculates the position with the coordinates 
of the reference points. 
 
Estimates of the distances will be performed by the method TOA (Time 
of Arrival). According to Fig. 4, the underwater sensor P has estimated 
distances dpa, dpb and dpc of references A, B and C this point can be 
represented on the surface through the decomposition of triangles. This 
point on the surface will be in the same plane of the reference points, 
making it possible to determine their coordinates (x, y) by trilateration. 
 
 
 
Fig. 4. Process definition of coordinates and depth. 
 
The coordinates for points A, B, C and P are respectively (x, y), (xb, 
yb), (xc, yc) and (xp, yp). The distances from the point on the surface for 
the three reference points A, B and C are da, db, dc, and can be 
represented by Equations 2, 3 and 4. 
 
 (xp-xa)2 + (yp-ya)2 = da2 (2) 
 (xp-xb)2 + (yp-yb)2 = db2 (3) 
 (xp-xc)2 + (yp-yc)2 = dc2 (4) 
 
The distances da, db and dc are obtained by the decomposition of the 
triangles. So we have three equations for two unknowns, making it 
feasible to obtain the position of point P on the surface. Thus, it is 
 
possible to position an object underwater with geographic coordinates 
and depth. 
 
PERFORMANCE ANALYSIS 
 
The analysis of the proposed underwater monitoring system is done 
through a series of simulations designed to verify system performance 
during navigation of vessels in the transition and in the exploration 
regions. The goal is to show the network behavior in each scenario to 
verify the positioning system viability using the fleet of logistic-support 
vessels and the production units as points of reference. The underwater 
sensors must be able to get their position through these references, with 
a sampling period appropriate to the needs of the operation of the 
Campos Basin. 
 
To evaluate and compare the monitoring system performance the ONE 
simulator proposed by Keränen, Ott and Kärkkäinen (2009) was used. 
ONE uses a specific movement model that can be customized to 
represent the actual displacement of the vessels responsible for the 
logistical support of the oil exploration. These displacements depend on 
each region: 
 
• Transition Region: containing a total of 5 sensors, this equals 1 
sensor every 20 km over the subsea pipelines. In this area, the 
vessels often travel great distances without stopping and at almost 
constant speeds; 
 
• Exploration Region: containing a total of 20 sensors distributed in 
strategic points to take advantage of the production unit range, 
which in some cases already provide two reference points. In this 
area, the vessels travel shorter distances with constant stoppages in 
the production units (fixed platforms, semi-submersible platforms 
and FPSOs - Floating Production Storage and Offloading) and 
may remain anchored waiting for new operational plans. 
 
The simulations consider a network with 25 sensors, 54 production 
units and up to 400 mobile nodes (vessels) and a control center outside 
the network. The mobile nodes are randomly distributed over the 
network and they move according to the mobility model. The sensors, 
production units and the routes for each region are shown in Fig. 5. 
 
 
 
Fig. 5. Basic scenario of simulations. 
 
Currently, there are 254 logistical-supportvessels operating for 
Petrobras, but the expectation is that this number will increase to 465 
by 2013, reaching a total of 504 vessels by 2020. This information is 
based on the presentation of the Petrobras business plan 2010 - 2014 
made by Gabrielli and Barbassa (2010). As the Campos Basin is 
currently working with nearly 80% of Petrobras vessels fleet, we chose 
to use this number in the simulations. 
 
The simulations aim to verify the following system information: 
 
• Percentage of sensors reached by three references; 
• Contacts made in the network; 
• Average time to obtain a given position. 
 
To simulate a realistic monitoring scenario, we considered the specific 
characteristics of the each region, which usually influences the type of 
movement and the vessels density. Only production units (platforms), 
sensors and mobile nodes (vessels) can move. The sensors have a very 
limited movement profile to represent displacements caused by 
movement of the seabed. The vessels in a typical operation, traverse the 
transition region staying for long times in the exploration region. 
 
The parameters definition of the simulation was based on (Ribeiro, 
Pedroza and Costa, 2011), following the movement of vessels and the 
characteristics of acoustic transmission. The simulation period was 24 
hours, covering an area of 250 km x 250 km (transition and exploration 
regions). The range of the underwater communication was defined as 5 
km and the vessels moving at speeds that vary between 5 and 14 knots. 
 
SIMULATION RESULTS 
 
The performance of the positioning system depends on the availability 
of network resources (vessels). Therefore, the displacement of vessels 
can affect the calculation of the sensor position. Thus, simulations were 
performed to verify the system behavior in relation to the movement 
and to the increase of the fleet of the logistic-support vessels. 
 
As the vessels move according to the logistical support activities while 
providing a reference service to the positioning system, there is the 
unpredictability of contact which is an almost mandatory condition in 
this system. Therefore, checking the availability of three references by 
the network may indicate the probability of obtaining the sensors 
positioning. 
 
Each simulation was performed to verify the ability of vessels together 
with the production units to provide three references for sensors. The 
evolution of this information with the increasing number of vessels can 
be seen in Fig. 6. In this case, it is noteworthy that the system allowed 
100% of the sensors to obtain three references from 200 vessels, 
indicating that it is possible to verify the positioning of all the sensors 
in the network. However, for the network to be truly effective, this 
information must be provided with a frequency that allows the 
monitoring of real underwater conditions. Therefore, the number of 
contacts and the average time to obtain the positioning are important. 
 
 
100 100
44
68
10096
16
100100100
84
0
20
40
60
80
100
25 50 100 150 200 350 400
Vessels number
Pe
rc
en
ta
ge
 o
f s
en
so
rs
 a
ch
ie
ve
d 
- %
Sensors with one reference
Sensors with three references
 
 
Fig. 6. Percentage of sensors achieved. 
 
The increase of number of vessels in the network caused the increase of 
the number of network contacts (Fig. 7). This expected behavior 
indicates that the vessels number affects the system ability to provide 
three references for sensors, allowing more sensors to be able to 
calculate the position. However, this information is only relevant if it is 
associated to the average time that the sensor waits to obtain these 
references. 
 
73 210
970
1905
3399
7691
11061
7 31
175 381
748
1769
2655
0
2000
4000
6000
8000
10000
12000
25 50 100 150 200 350 400
Vessels number
Nu
m
be
r o
f c
on
ta
ct
s 
pe
r h
ou
r
Contacts with one referencee
Contacts with three references 
 
Fig. 7. Number of network contacts. 
 
The frequency with which the sensors update their positioning is 
essential to define what kind of application will be supported by the 
system. Fig. 8 shows that from the scenario currently found in the 
Campos Basin (200 vessels), the sensors wait about 48 min to obtain 
three references, allowing to monitor the position of underwater 
infrastructure. This condition may be improved reaching 20 min in 
2013 (350 vessels) and 14 min in 2020 (400 vessels). The values are 
high compared with conventional systems, but represent the minimum 
response time to detect any seabed slide. In this case, it would be 
possible to activate the emergency maintenance service in a short time 
if there was any oil spill, reducing the environmental damage. 
 
1143
4902
142048
94206
0
1000
2000
3000
4000
5000
6000
25 50 100 150 200 350 400
Vessels number
Av
er
ag
e 
In
te
rv
al
 to
 o
bt
ai
n 
th
re
e 
re
fe
re
nc
es
 (m
in
)
 
 
Fig. 8. Average interval to obtain three references. 
 
The performance of the positioning system is affected by the 
availability of reference units within sensors range. However, the use of 
fixed reference units (platforms) in certain areas allows sensors to be 
less dependent on vessels and in some cases require only a mobile 
reference point. It was verified that this behavior is exclusive to the 
exploration region, being the transition region dependent on vessels due 
to lack of fixed reference units. 
 
CONCLUSIONS AND FUTURE WORK 
 
This work proposed an underwater positioning system for the specific 
environment of the offshore Campos Basin. The objective was to verify 
the system behavior and to analyze the feasibility of using the logistic-
support vessels and platforms in the task of providing the necessary 
references to obtain the positioning of underwater sensors. 
 
Although vessels are not working specifically for the positioning 
system, it was noted that in conjunction with the production units 
(platforms) they can provide the necessary references so that the 
sensors are able to calculate their positions. 
 
Despite the unpredictable communication of system, it was possible the 
location of all sensors in the network with a sampling that allows the 
monitoring of displacements caused by the instability of the seabed in 
order to detect situations that may cause oil spill and consequently 
damage to the submarine environment. Thus, the general behavior of 
the system was satisfactory, with consistent results that demonstrate the 
feasibility of monitoring the positioning in current and future scenario 
found in the oil exploration area of Campos Basin. 
 
As future work, we intend to investigate the degree of precision 
provided by the underwater positioning system proposed. This 
precision will take into account the characteristics of underwater 
acoustic communication. 
 
ACKNOWLEDGEMENTS 
 
The authors would like to thank PETROBRAS, CAPES, CNPq, 
FAPERJ, and MCT/FINEP/FUNTTEL for partially funding this work. 
 
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