Integrating GIS with risk assessment
Data Processing
The risk assessment software reads data directly from the GIS and other stand-alone databases to
extract all the data required for risk assessment. All the data is referenced by a common location hierarchy,
namely station series and stationing. The risk assessment software performs a true dynamic segmentation
of the data (described in more detail later in this paper) and performs all of the calculations described in the
risk assessment algorithms. The results are stored in a separate risk database used by the risk assessment
software. The separate risk database allows the user the ability to reprocess or even temporarily change the
data when performing what-if scenarios without compromising the integrity of the actual data. The risk
results can in turn be accessed by a GIS viewer type application and displayed side by side with GIS data or
on maps, drawings or alignment sheets.
Specialized risk analysis and output capabilities
Dynamic Segmentation
Dynamic Segmentation is a term that is used often but has different meanings for different users.
Dynamic Segmentation, as it is used here, is an automated process that uses high-resolution facilities
information to define segments of like pipe, i.e., lengths of pipe where all the pipeline attributes (or at least
the ones used in the risk model) are the same throughout its length. GLGT recognized the limitations of
using valve section segments, namely that they are too large and encompass too many different pipeline
conditions (pipe types, soil environments, or operating parameters) to be accurately characterized by one
set of pipeline attributes. Many of the valve sections would be more accurately modeled as several smaller
sections, due to changes in pipeline attributes within the valve section, such as wall thickness, class
location, terrain, soil conditions, or other factors considered in the risk model.
The difficulty in using large, predefined segments is how to describe specific pipeline attributes
accurately. For example, how do you classify the soil type and the effectiveness of the cathodic protection
if 3 miles within a 12-mile-long valve section are in a wetland where conditions are good, but the balance
of the segment is in rocky soil where conditions are not as good. The dynamic segmentation process would
solve this problem by making at least three segments out of the valve section: one segment representing
the wetland, and two other segments representing dryland portions upstream and downstream of the
wetlands. This is an illustration of just one of the many types of issues that must be considered when
choosing a segmenting strategy.
Dynamic segmentation is performed by systematically processing data according to the stationing
values, denoting points at which pipeline attributes change. Stated in simplified terms, the software starts
at the beginning of the pipeline and reads the initial set of pipeline attributes (variables). It then ‘moves’
down the pipeline checking all variables until any one of them changes. Upon detecting a change, the
program defines the endpoint of the first segment and starts a new segment. The process is repeated along
the entire length of the pipeline, or more accurately stated, the process is conducted through the entire
database.
Dynamic segmentation can be performed on databases that contain pipeline attribute data of
different resolutions, i.e., both valve section resolution and higher resolution. Dynamic segmentation
performed on a database of valve section resolution data will return only the valve section segments.
However, when at least one pipeline attribute (or better yet more than one) is defined at a resolution higher
than the valve section level, dynamic segmentation will return smaller, more accurately defined segments
that will enhance the risk assessment. Operators usually have data at different resolution levels, typically
the valve section level and higher levels, such as start to end survey station locations. In the GLGT system,
dynamic segmentation of the high resolution GIS facilities information resulted in a number of segments,
allowing them to more easily isolate areas of higher risk.
POE analysis
In-line inspection results can be ranked and prioritized using a statistical or probabilistic approach
as opposed to simply ranking the anomalies reported by the vendor by either depth or predicted failure
pressure. The approach, known as Probability of Exceedance (POE) analysis, has been adopted by several
operators and its use is increasing over time. The POE analysis methods evaluate the probability that an
anomaly of any indicated size could be large enough to threaten the integrity of the pipeline. It also
accounts for the sizing inaccuracy or bias in the tool. The POE approach can provide the operator a
number of advantages over the typical schemes for prioritizing anomalies, not the least of which is a
method to evaluate the benefits of further excavations versus reinspecting the pipeline. POE also provides
a means for summarizing in-line inspection (ILI) results over a segment of pipeline. By applying a
conservative corrosion growth rate to the ILI results, future maintenance and inspection activities can be
evaluated and planned.
In order to conduct a system-wide POE analysis in conjunction with dynamic segmentation,
location information had to be assigned to each anomaly in the database. The POE results were used to
calculate a cumulative POE value for the segment, a single value that is based upon the POE values of each
anomaly located within the segment. The cumulative POE calculation had to be performed after dynamic
segmentation, which defines the endpoints of the segments. The risk software automatically performed the
dynamic segmentation and extracted the POE values of each anomaly located within the segment to
compute the cumulative POE value. The actual ILI data, including depth, length, location, run date, and
other information, are stored within the program and can be used along with POE results in what-if
scenarios.
What-If Scenarios
Aside from calculating risk index scores for segments, the risk assessment software is also used to
evaluate the effectiveness of various maintenance alternatives. Maintenance alternatives are simulated by
changing the appropriate pipeline attributes, or variables, and recalculating the risk index scores for the
segment or segments involved. These recalculated results, along with some fundamental cost information,
can be used as the basis of a cost-benefit analysis, allowing the operator to find the most effective
mitigation alternative for the lowest cost.
The what-if analysis can range from relatively simple to complex. An example of the more
complex analyses performed in the risk assessment software are the POE scenarios which involve modeling
the excavation and repair of specific anomalies and the impact of these activities over time. Other
relatively simple scenarios involve simulating a hydrostatic test or recoating project.
Future goals
Data integration
The pipeline attribute data is stored locationally in the GIS by station value, and the risk
assessment software that reads and processes this data maintains the location information during the risk
assessment processing so that the segment locations and risk assessment results can be used with the GIS
display tools. M.J. Harden is developing a GIS viewer application which will allow GLGT to view the
risk assessment results graphically, overlaid on a map of the system. The display tool will allow the user to
turn on certain features of interest, such as rectifier locations, class location, structures, or any other data set
used in risk assessment or contained in the GIS.
Currently, several operators, all using the ISAT data model, are working together to standardize a
format for storing pipeline integrity-related data. One of the long-term goals of this effort is to be able to
develop software applications that seamlessly interface with the integrity data. Applications will include
risk assessment, integrity-related alignment sheet generation, and a GIS viewer, among others. GLGT is
working with this group to set the standard for data integration techniques that will optimize the use of GIS
and integrity-related information to improve safety and reliability.
