Model-driven software
1. INTRODUCTION
Pipelines face threats of leaks, damage, and breaks, which can be
caused by aging equipment, lack of adequate maintenance,
extreme weather, natural disasters (such as earthquakes), or even
deliberate sabotage [
10
]. For underground pipelines, problems are
difficult to detect, locate, and repair. Surface pipelines detection
and repair can be easier, but remote locations increase the
challenge. Leaks, damage, and breaks cause product loss and flow
stoppage, resulting in environmental and financial damage.
Financial damage can include fines for environmental incidents,
failure to meet refinery contractual obligations, the need to
purchase additional product to replace lost product, cleanup of
spills, and damage to reputation. While this approach is still
relatively new, hardware and software technology along with
experience and knowledge have evolved so that there is now the
understanding of how to reliably and cost-effectively assemble the
pieces to create solutions that optimize operations and help to
avoid or quickly identify and resolve problems. This ultimately
helps to reduce costs, increase safety, and improve industry’s
overall performance. Some similar approaches are also developed
for downstream operations, specifically in the area of pipeline
leak detection [
17
]. Pipeline operators face similar challenges as
upstream operators in terms of operational scale and remoteness
and the need to quickly make sense of huge volumes of available
data. For operators, vast networks of pipelines move oil and
natural gas from producing sites to refineries and manufacturing
plants and on to markets for distribution. These pipelines meander
through densely populated urban areas and extend through
boundless, remote regions, in some cases easily accessible from
the surface, in other cases located underground. Existing
technology for pipeline leak detection, such as sensors and
network infrastructure, can gather necessary data and even
provide some useful information about cause. However similar to
early upstream solutions workers do not have the tools necessary
to help make sense of all the data, to bring it all together in a
comprehensive view so they can quickly identify, locate, and
resolve problems. None of these models considered the effects of
anticipatory anomalies, differential topologies, pipe dimensions or
corrosion allowance on pipeline-leak frequency [
21
]. This paper
therefore proposes an integrated designs using Google earth
platform that can adequately monitor and detect pipeline leaks, it
is a fundamental approach to oil and gas leaks detection that could
lead to a conceptually real-time pipeline leak frequency model for
oil and gas pipelines location monitoring and leak detection
system [
7
]. The aim is essentially to present a design framework
that will eventually lead to the development of a software based
management system for oil and gas pipelines monitoring and leak
detection, which will be applicable to fundamentally all
carbonsteel pipelines exposed to serious corrosion attack, either internal
or external and leakages and transporting fluids. It can be useful in
evaluating the technical and capital alternatives in pipeline design,
operation, and maintenance.
2. ENVIRONMENT AND SCOPE
A network of pipelines is used to transport oil and gas from areas
of crude oil production to oil consumers has recently broadened,
especially in Nigeria. In Nigeria today, increases in fuel
consumption has led to a comparative rise in the development of
pipelines and its attendant risks such as leakages [
2
]. It became
necessary, therefore to develop an information system that would
help stakeholders make managerial decisions promptly, and to
provide environmental monitoring of pipeline construction and
maintenance areas and to ensure a quick and effective response to
emergency [
9
]. This design approach is a distributed leak
detection system that is responsible for coordinating the
communication between geo-stationary space satellite, the oil and
gas physical pipeline network images, Google Earth station and
the internet cloud [
8
]. Information in the design leads to the tasks
of developing a software interface that will be able to
communicate with the monitoring models via to provide trending,
diagnostic data, and detailed schematics on the oil fields pipeline;
a system capable of detecting leaks in pipelines using Google
Earth Platform. The system will include software based
georeferenced geological landscape and topographical maps and data,
obtained through Google Earth Remote Satellite Imaging
(GERSI). High resolution images, obtained from GERSI, can be
used to compile large-scale environmental maps that serve as a
base for monitoring compilations and would permit users to trace
spatial and temporal landscape variations in pipeline damage
impacted area and will give an instantaneous overview of
problematic areas. The system can identify potential and actual
damage or leak-related incidents, including those caused by
accidental or malicious human intervention. This data is a crucial
component of the system [
11
]. The Google Earth tool will have
direct connection to the earth geostationary satellite in space from
where life and real time images can be sourced. The solution will
also use other data sources in addition to the main data source,
including pipeline operations data and historic data. The solution
will create its own knowledge base, a data store for the results of
trend analysis, pattern recognition, event detection, and other data
analysis. This federated data approach which merges together
multiple disparate data sources into a virtual database is necessary
for the fast detection and response required for this solution,
enabling operators to build and run various workflows that
leverage software based tools for advanced data analysis [
5
].
3. RELATED WORK
This section presents a review of leak detection methods with the
aim of providing the major themes in this line of research. Jun
Zhang [
6
] in the work on designing a cost effective and reliable
pipeline leak detection system discussed the ATMOS PIPE
statistical pipeline leak detection system, incorporating advanced
pattern recognition functions. It is a cost effective real time leak
detection system with a very low false alarm rate. Discussions on
this methodology supported the design direction of the present
proposed design for effective leak detection and monitoring of oil
and gas pipelines. Leak detection is a very important step in the
production of reservoirs. It is needed to check tightness
specifications and to ensure other installation specifications are
fulfilled. In the light of this assertion, N. Hilleret [
7
] described a
method emphasizing on techniques used for particle accelerators;
in it, the most widely used leak detector will be presented with its
different types in the context of the accelerator operation.
3.1 Leak Detection Methodologies
Due to so much damage caused by oil and gas pipeline leaks to
the environment, many more strict constitutional rules are
introduced in oil and gas producing countries to minimize the
effects of such damages [
4
]. To this end many of such countries
and industry players are seeking for very reliable and cost
effective methods for detecting leaks to enable them correct and
ease the level of damages caused to the environment and
mankind. In this paper some pipeline leak detection
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methodologies are studied to come up with a design structure that
could ease pipeline leak detection and monitoring via Google
earth [
3
]. These include hardware and software based methods
having the capabilities for leak sensitivity, location estimate
capability, operational change, availability, false alarm rate,
maintenance requirement and cost. A common difficulty
identified for most of these methods is the aspect of generating a
leak alarm when the pipeline is under normal operation, such
irregularities found in most methodologies reduce the confidence
operators have in a system because in most cases a real leak may
be overlooked. A few leak detection methodologies are as
follows: Halogen leak detector method, ATMOS PIPE, The spark
coil technique, and Pressure change method. Halogen leak
detector method as exemplified in figure 1 is based on the
increased emission positive potassium and sodium ions because of
the sudden presence of the halide composition [
6
]. The detection
process follows the amount of current flowing through the ions,
i.e. the higher the current the more the leak size; it is therefore
more suited for use in rough, medium and high vacuum pipelines.
ATMOS PIPE is a statistical pipeline leak detection system,
incorporating advanced pattern recognition functions. It has been
developed at Shell applying advanced statistical techniques to
flow and pressure measurements of a pipeline [
18
]. Variations
generated by operational changes are registered and a leak alarm
is generated only when a unique pattern of changes in flow and
pressure exists [
12
]. This statistical method does not use
mathematical models to calculate flow or pressure in a pipeline
but it detects changes in the relationship between flow and
pressure using measurement data available. As the system
monitors a pipeline continuously it learns about continual changes
in the line and flow/pressure instruments. The spark coil
technique uses a high voltage and sparkling point to create the
electromagnetic radiation which causes the generation of glow in
non-metal glass and plastic elements [
16
]. Drawing the leak
antenna along the tested element the defect spot is very clearly
marked and the inner pressure can be estimated by a skilled
personnel. Pressure change method as illustrated in figure 2 uses
pressure gauges which are ordinary used to monitor the system
performance. Suspected leak sites can be sprayed with a solvent
while watching the gauge for a pressure rise that occurs when the
solvent enters the leak.
4. DESIGN OF THE SOFTWARE BASED
DETECTION AND MONITORING SYSTEM
Some factors taken into consideration in this design approach are
the fact that uniform industry standards for leak detection and
monitoring do not exist, especially in relation to
noncomputational pipeline monitoring approaches to leak detection.
Metrics to capture sensitivity and reliability should be normalized
to provide more relevant information for pipeline operators.
Integrating all these issues into a common design will achieve
communication between the various integrating elements [
19
].
Channeling the resultant software based communication through
Google Earth tool can process the signals and project same for
effective detection and monitoring, readability, and interpretation
purposes. A model is generated that can receive and send spatial
data from the pipeline network via Google Earth to monitor and
detect uncomplimentary activities within the pipeline
infrastructure. Google Earth tool pick up geographic information
system (GIS) spatial data, amplify and transmit them into the
internet cloud from where it can be channeled to software based
monitoring client. Earth Geo-stationary satellite intercepts area
images and signal, routes to routing devices in Google Earth
stations for upward real time relay on the internet cloud.
Conditioned spatial data is processed by server and projected to
the network monitoring software which is projected for display.
This represents bulk of computing activity considering the
variables, such as: Spatial data variables, Pressure drop leading to
independent development of dynamic model, Activity change
along pipelines, and Data variation between normal spatial data
and spatial data with changed activity. The design is made up of
elements that can handle the spatial data and the on-site data by
integrating both to generate values to be feed into the software
based system. From the schematic view of the real time Google
Earth GIS capture system presented in figure 3 it can be seen that
the Geo-stationary space satellite takes photograph of the areas
covered on the Earth [
20
]. The images captured are transmitted to
the earth satellite and into the Google Earth Station from where it
is retransmitted into the internet cloud to be accessed by client
machines which processes the data.
Geo-stationary
Space Satellite
Image
Earth
INTERN
ET
Earth