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Case studies: Pipeline route planning


PIPEMON Pipeline route planning case studies

The route planning procedure, as developed and tested within the Project, directly incorporates EO data as part of the overall information base for constraint mapping, and consists of 4 main steps: 1. Undertake a coarse land cover analyses, update as required any available land use GIS, and incorporate overall constraint features. Undertake an initial ranking of routing options, in overview, to exclude difficult terrain and pre-select general route corridors. This process can usually be conducted using more generalized land cover / land use information, including lower cost / lower resolution EO data. 2. Identify viable route alternatives within those corridors identified in step 1. Pre-select routing options and spatially delineate critical corridor / route segments using constraint mapping of input data and information, including higher resolution EO data or digital airborne imagery. Constraint evaluations at this stage may also incorporate a combination of ground-based features/measures. The result of step 2 is a set of routing alternatives, such that each corridor / route segment alternative is associated with different numbers and different weightings of constraint features. 3. Select those route alternatives that are associated with fewer and/or lower weighted constraint features, and undertake further analyses. Step 3 involves further combination of EO and other spatial / constraint data / information using GIS modeling and expert knowledge tools. At this point, it is important that the analyses are driven by experts familiar with the routing selection criteria so that, as the investigation proceeds, there can be iterative refinements applied as constraint features are evaluated/re-weighted and any remaining information content requirements determined. Step 3 is then iteratively repeated, based upon feedback from experts / consultations, or the addition of new or updated information, for example, reassessed/updated engineering costing constraints. 4. Conduct one final round of analyses, incorporating all final GIS modeling rule-sets, appropriate spatial information and expert knowledge (as applied at the completion of step 3), to create final constraint maps. The final outputs which identify one or a few preferred routing option(s) then need to be checked and further considered by pipeline planning experts. Route planning Test Sites 1 and 2, as outlined below, illustrate the application of steps 3 + 4, and steps 1 + 2, respectively, as described in the paragraph above. Results from the two test sites demonstrate that hard data, such as land cover and GIS-based positions of existing pipelines, can be appropriately assessed and weighted along with manually input “soft information” – for example, information about interests expressed by adjacent land owners and local planners to undertake specific activities if certain conditions are met. Test Site 1 For a test site in Northern Germany, Ikonos imagery and other spatial data incorporated within a land-use GIS were used to help construct constraint mapping for a section of a proposed pipeline route (see figure below). Also contained within the project GIS for the northern German test site was spatial information about existing pipeline networks, as well as interpreted patterns of ground movement over time (derived from PSI analyses) within the vicinity of the proposed pipeline corridor.


Incorporation of EO and other data within an analytical GIS system, as a basis for constraint mapping of proposed pipeline routes.

The constraint mapping was undertaken using eCognition© software (see www.definiens.com); eCognition is an object oriented image analysis software which intuitively fuses EO data with thematic data like ESRI? shapefiles, so that derivative analyses can be performed. In a first step of the route planning constraint mapping, image objects are created containing all information of the EO data and the GIS plus their mutual relations. This enables the planning expert to later access all information for every area in the test site. Selection preferences/priorities as identified by pipeline planning experts were translated into sets of rules, which the eCognition software could then use, in a fuzzy logic way, to classify spatial extents into 5 constraint classes.

Test Site 2 For a second test site in Germany, two different levels of detail were analysed (see figure below).


Detailed analysis	Coarse analysis
Spatial representation of GIS output from coarse and detailed analysis of constraint features associated with a proposed pipeline route.

The coarse level 1 analysis, as shown in the above figure, uses publicly available data such as Landsat imagery and broad DEM data to generate a generalized overview over the test site. In the second step (see figure below), areas of interest were analysed using more detailed input data, such as aerial photo-imagery and spatially based land use and ownership data.


Application of route planning steps 1 and 2 to create constraint maps.

The following general statements were derived from pipeline operators who provided feedback, in relation to evaluation of route planning services: The Project’s route planning tool is a good support for the early phases of coarse planning. The consideration of existing pipeline tracks as planning parameter is essential. The consideration of planners’ knowledge in the automated decision process is essential. The approach itself shows a promising development for automated planning support. Service Strengths The following general statements were derived from pipeline operators who provided feedback: The service is especially suitable for sparsely populated remote areas where only spatial information is available. The service may use EO data and include non-EO data so that a full coverage of the terrain under investigation can be achieved. The service may produce results in a little time. Once basic data for an area is given different planning projects can be realized with less effort. Automated processing with low costs for repeated planning runs with changed parameters are possible. The service may be carried out during non-working hours. The service can be used to update and enhance existing non-EO data using the object recognition feature. In densely populated areas, a preliminary coarse planning phase may be obsolete because available corridors are obvious to the planner. In order to achieve the desired result in these cases, the service requires a lot of ancillary data to properly reflect the planning process. Service Opportunities The following general statements were derived from pipeline operators who provided feedback: The Project’s planning service gives a good opportunity for placing further EO based services within a service portfolio for the pipeline industry. The planning service is scalable and allows the user to initially use a simple version in a smaller project, and thereafter introduce enhancements for more complex data requirements or larger pipeline planning tasks. The Project’s methodology appears to be very useful in terms of addressing main technical threats for a route planning activity, but there are at least two issues that need further consideration: 1. Protection of customer sensitive data: Proposed future pipeline network configurations are sensitive strategic data for a pipeline operator, but also needs to be addressed / considered in the route selection process for an individual route / corridor. Perhaps there are ways to protect this larger corporate information while making portions protected but available for constraint mapping and route selection activities; certain services are already in use for third party interference information. 2. Reduce production costs (especially in relation to EO data and services costs): The general uptake of a planning service product requires adequate pricing. This requires low data costs, clearly defined costs associated with population of the GIS analytical tools, and iterative development of the constraints mapping components. There are perhaps several ways of reducing costs, such as service/data pooling, and these should be considered further.