GIS in pipeline route selection

Abdul-Lateef Balogun Doctoral research student, Universiti Teknologi PETRONAS(UTP), Malaysia

Abdul Nasir Matori Associate Professor for Geomatics/ Geoinformatics, Department of Civil Engineering, Universiti Teknologi PETRONAS (UTP) Malaysia

Khamaruzaman Yussof Assoc. Professor, Universiti Teknologi PETRONAS, Malaysia

Dano Umar Lawal PhD candidate, Universiti Teknologi PETRONAS, Malaysia

Imtiaz Ahmed Chandio Assistant Professor, Mehran University Technology Mehran University, Jamshoro, Pakistan

Burgeoning energy needs in the 21st century are driving demands for more petroleum products. These needs were hitherto met by using pipes to transport required energy products over long distances within countries and across borders, from their sources to respective destinations (Chai et al, 2006; Yildirim et al, 2007; Dubey, 2009). However, increase in global population has triggered an increase in demand for essential products such as natural gas, crude oil and finished petroleum products, etc. For instance, regional and global demand on the Persian Gulf’s hydrocarbon reserves has increased dramatically in the last decade (Degermenci, 2001). Other regions are also experiencing similar demands for their petroleum resources and existing pipelines are rather insufficient to efficiently transport the huge volume of required energy resources. Moreover, many of the existing pipeline systems are old and face the risk of failure sooner than later. It is also generally considered risky to depend solely on single, ageing pipe networks/routes for product transportation (Nord Stream, 2012). Thus, it is imperative to develop new pipeline routes in-order to augment existing ageing ones. According to Smith (2012), planned pipeline construction, which is to be completed in 2012 increased by 6.7% when compared with the previous year’s figure. Relying on data from the world’s pipeline operating companies, it was projected that 8,887 miles of pipelines will be installed in 2012.

Pipeline route planning and selection

Route selection is a foremost and crucial step in the overall pipeline design and construction process (Feldman et al, 1995; Yildirim et al, 2007). Proper pipeline route selection is a strategic component of a company’s pipe laying activities when laying new pipelines or expanding the existing pipeline network. Selection of an appropriate pipeline route is perhaps the single most important element in the development of transmission lines (Khene, 1997; Degermenci, 2001). The pipeline route selection process focuses on achieving the optimal location for a pipeline. Optimization of route selection brings about risks and cost reduction, as well as a better decision making process. The selected route can significantly affect the success or failure of a project. The importance of selecting an appropriate route is evident when the increased pipe length and higher cost of an unnecessarily longer, meandering route is considered (Wilburn et al, 1995).

Reports from two European Union (EU) commissioned studies on pipeline transportation out of Caspian Sea producing areas concluded that such pipeline systems are technically, economically, and environmentally feasible but depend critically on route selection (Degermenci, 2001). These findings further underscore the significance of proper route planning and selection in any pipe installation project. It is thus essential to endeavor to always identify routes which are technically feasible, are constructible at reasonable costs, cause minimum damage to the environment, and create minimum resistance from the public (Feizlmayr et al, 1999). Basically, critical issues affecting route selection are a societal risk, have an environmental impact, and require engineering and operational efficiency. Other route selection considerations include physical, political, economic, and regulatory concerns (Carpenter et al, 1984; Ryder, 1987; Feldman et al, 1995; Montemurro et al, 1998).

Pipeline route planning and selection is usually a complex task involving simultaneous consideration of several factors. It is more complicated than simply laying pipes from the oil source to the final destination. Natural and manmade barriers along probable routes have to be considered and the likely influences of these barriers on the pipes after installation are also taken into account. Accurate determination of the impact of these factors on pipeline routes is usually a tedious task requiring a skilled and dedicated approach (Oil and Gas, 1993). Generally, pipeline route selection procedures are implemented using two techniques: Traditional route selection process and GIS-based (automated) route selection process.

Traditional route selection process

Traditional routing procedures usually begin with a start to end (source to destination) plan. A large area within the start and end points is identified and detailed data within this whole area is acquired. Typical data sources include maps, field surveys, aerial photographs, or other sources that provide information on routing obstacles which must be overcome. If the proposed route presents insurmountable physical obstacles, environmental constraints or other barriers, a new route must be explored and the data collection process begins again (Oil and Gas, 1993; Humber, 2004; Berry et al, 2004). The manual method is characterized by small scale paper maps, hand delineation, and manual topographic map overlay (Jankowski, 1995; Price, 2009). There is limited use of technology in this process and feasible results obtained from the data acquisition process serve as a preliminary pipeline corridor on which decisions are based. The core strength of the traditional route selection method lies in its reliance on experts’ experiences, interpretations, and judgments in selecting the final pipeline route (Jankowski, 1995; Matori et al, 2009). However, the process has been criticized for its inaccuracies and resources wastage (Feldman et al., 1995; Humber, 2004). It is claimed that this manual procedure is usually tedious, time consuming, and lacks details (price, 2011). It is further argued that it lacks a defendable, documented procedure that clearly demonstrates the constitution of a best route (Matori et al., 2009).

GIS-based route selection process

Finding an optimal pipeline route using GIS can be accomplished using a least cost path (LCP) algorithm. The LCP method for determining the optimal pipeline route between the product source and destination is an established GIS technique (Husdal, 2001; Humber, 2004; Berry et al., 2004). Four parameters are required for a least cost path analysis – source raster, cost raster, cost distance measures, and an algorithm for deriving the least accumulative cost path (Chang, 2010). Finding the least accumulative cost path is an iterative process based on Dijkstra’s algorithm (Chang, 2010). In 1959, Dijkstra developed what is arguably today’s most popular shortest path algorithm. It is also probably the most widely used (Husdal, 2001). The iterative process starts by activating cells adjacent to the source cell and by computing costs to the cells. The cell with the lowest cost distance is chosen from the active cell list, and its value is assigned to the output raster. Subsequently, cells adjacent to the primary cell are activated and included in the active cell list. Whenever a cell is reactivated, its accumulative cost has to be computed again. The lowest accumulative cost is then assigned to the reactivated cell. The process will be repeated until all the output raster cells are assigned with their least accumulative costs to the source cell (Chang, 2010). A remarkable feature of Dijkstra’s algorithm is that it guarantees the optimal solution to a shortest path or least cost path problem. Benefits of GIS-based route selection are well documented in existing literatures (Humber, 2004). GIS least cost path (LCP) analysis has been effectively used to determine suitable oil and gas pipeline routes in several real-life projects (Feldman et al.,1995; Montemurro et al.,1998; Dupuis et al., 2004; Iqbal et al., 2006; Yildirim et al., 2007). A GIS-based route selection process has the potential to reduce project costs by about 15 %-30% (Delavar, 2003; Humber, 2004; Exprodat, 2012). When properly used, GIS techniques are faster, better, and more efficient than traditional/manual techniques (Price, 2009 and 2011).

In a comparison of a GIS-generated pipeline route and a manually generated route shown in figure 1 above, Matori et al., (2009) concluded that the GISdeveloped route facilitated greater reduction in pipeline construction cost.

In spite of its efficacy and cost effectiveness, the use of GIS in route selection and utility mapping by companies and consultants is still limited. There is a reluctance to shift from the traditional method and holistically adopt GIS techniques and analysis (Scott et al, 1998; Humber, 2004).

Challenges of GISbased route selection

One reason GIS remains under-utilized in pipeline route selection projects is ‘project panic’, which is an intense fear experienced by project personnel who are skeptical of procedural change or a shift from the status quo (Humber, 2004). They are comfortable with the way things were done in the past and are satisfied with results from previous projects utilizing manual route selection methods. To this group of people, adopting a GIS-based route selection process could be a costly gamble.

Another growing concern in recent times is the reliability of GIS, especially when dealing with multi-participant and multi-criteria problems like pipeline route selection. A lot of researchers have expressed concerns that GIS is a limited tool in spatial decision-aid domain. This is primarily attributed to its lack of more powerful analytical tools which makes it challenging to reliably deal with spatial problems involving several diverse groups with conflicting objectives/criteria (Janssen and Rietveld 1990; Carver 1991; Fischer and Nijkamp 1993; Laaribi et al., 1996; Malczewski 1999; Chakhar et al, 2003). It is argued that decision makers’ preferences, represented by criteria weights are not accurately represented by current GIS (Chakhar et al, 2003). This limitation fuels the skepticism of companies and consultants who are originally not keen on utilizing the technology.

Current trend

In order to overcome the limitations identified in previous section, continuous efforts are being made to integrate GIS with other analytical tools to produce robust decision support systems (DSS) capable of effectively solving multi-criteria and multiparticipant problems like pipeline route selection (Chakhar et al, 2003). Specifically, Multi Criteria Decision Making (MCDM) tools have been successfully used for various applications since the 1960s and many of these tools are working well with GIS too. Though, MCDM is divided into two broad groups: Multi Attribute Decision making (MADM) and Multi Objective Decision making (MODM), MADM tools are more commonly integrated with GIS (Malczewski, 2006).

While GIS is a powerful tool for managing spatially referenced data, MADM tools offer powerful techniques for modeling spatial problems (Chakhar et al, 2003). Collectively, they form very powerful decision-making tools capable of solving complex decision problems (Jankowski, 1995; Malczewski, 2006).

Lin (2006) listed major types of MADM techniques as shown in Table 1.

So far, GIS and some of the MADM methods have been effectively combined to solve real-world pipeline route selection problems. Nonis et al (2007); Wan et al (2011) ) have successfully integrated GIS-MADM for the determination of optimal pipeline routes.

Summary and conclusion

Development of new oil and gas pipeline routes in coming years is inevitable and selection of an optimal route is crucial to the success of any pipeline routing project. An optimal route will minimize economic loss and negative socio-environmental impacts, in addition to enhancing the pipes’ sustainability and prolonging its lifespan. Empirical evidences suggest that GIS-based route selection is more efficient than manual route selection. However, the use of GIS for this purpose is relatively low. Many researchers have argued that GIS has limitations when dealing with multiple criteria and multiple participant related projects like pipeline route selection. Consequently, it is recommended to integrate GIS with other analytical tools for optimal performance.

GIS and MADM have complementary features and GIS-MADM integration has the potential to tremendously enhance group decision-making processes like route selection. So far, not all MADM techniques can be properly integrated with commonly available GIS softwares. For future research, it is necessary to develop platforms that will support the integration of GIS with virtually all the established MADM techniques. This will provide project managers and other pipeline routing stakeholders the opportunity to choose suitable GIS-MADM methods from the wide range, on a project–by-project basis.

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