Crude oil transportation through pipelines presents danger to communities along its path. In the Niger Delta region of Nigeria for instance, pipeline vandalism occurs indiscriminately and regularly, such that every segment of a pipeline network becomes a potential target and possibly source of oil spill hazard. In terms of pipeline hazard and risk distribution, the oil plume’s ability to migrate freely in wetlands and encroachment on pipeline right of ways by people increases chances of wider contact and exposure opportunities to inhabitants and the environment. Despite several efforts to mitigate pipeline hazards in the oil and gas sector, none has been effective in Nigeria partly due to paucity of data in public domain and poor public participation. Therefore considering the environmental and human health challenges associated with oil spills, an alternative method was developed using multi-criteria decision analysis to model 1) pipeline hazard zones, 2) potential pipeline impact radius, and 3) high consequence areas with four attribute layers, i.e. land cover, population, river and pipeline to encourage public participation. The model identified land use areas, communities and rivers likely to be susceptible to pipeline hazards and areas requiring regular monitoring and possible intervention. Meanwhile the model sensitivity test indicated that the river layer was most sensitive, while transferability was limited to similar criteria variables. The model can stimulate public participation in pipeline hazard management while policy makers and regulators would find it relevant in oil spill impact mitigation.
Pipeline accidents are caused by mechanical and operational failure, natural hazard and third party interference [
Pipelines are important components of crude oil transportation and even play significant role in shaping political and economic landscape of global energy supply and transmission [
According to the Pipeline and Hazardous Material Safety Administration (PHMSA) of the US Department of Transportation, discharge from pipeline failure can affect human health and safety; it can also cause environmental degradation and damage to properties. As a result, experts in pipeline safety developed the concept of “pipeline Impact Radius” and “High Consequence Area” to determine places where pipeline hazard can cause significant adverse effect. Thus, a designated PIR buffer is an estimated distance beyond which humans and ecological receptors have about 90% chance of survival [
where: r = impact radius in feet, pp = pipe pressure in pound per square inch, pd = is pipe diameter in inches, and 0.69 = a constant for natural gas.
The United States Pipeline Safety and Regulatory Certainty Act of 2011, require operators to maintain up- to-date record of pipelines in high consequence area (HCAs) by calculating PIRs along their pipelines, and identifying the population within the impact radius [
Region | Product | Failure Rate 1000 km/year | Year |
---|---|---|---|
United States | Gas | 1.18 | 1984-1992 |
United States | Oil | 0.56 - 1.38 | 1984-1992 |
Europe | Gas | 1.85 | 1984-1992 |
Europe | Oil | 0.83 | 1984-1992 |
Western Europe | Oil | 0.43 | 1991-1995 |
Western Europe | Gas | 0.48 | 1971-1997 |
Canada | Oil & Gas | 0.35 | N/A |
Hungary | Oil & Gas | 4.03 | N/A |
Nigeria | Oil | 6.4 | 1976-1995 |
Niger Delta (Nigeria)* | Oil | 1.14 | 1999-2005 |
*Achebe et al. (2012).
marcation representing areas where population, source of domestic water and sensitive ecological resources intersect with the PIR. Thus for proper designation of areas of concern, the US Department of Transportation employs “HCA” to describe potential impact areas located within pipelines buffers.
In this paper, an alternative method is developed with Multi-Criteria Decision Analysis in the context of Analytical Hierarchy Process (MCDA-AHP) to model potential risk area and compensate for data paucity, because pipeline data are not easily obtainable in the public domain in Nigeria, this has hindered public participation and scrutiny of pipeline operating standards in the country [
The integration of Multi-Criteria Decision Analysis (MCDA) method in GIS has improved conventional method of map overlay in decision analysis [
Over the years the multi-attribute method is integrated in GIS using Weighted Linear Combination (WLC) [
The AHP method drives weight for attribute layers in combination with other attributes [
1) Criteria selection: identification of relevant data layers for problem solving, which can be presented in thematic layers of specific features and attributes.
2) Criterion score standardization: allow data to be measured on similar unit or scale. By standardization, the data layers are converted to comparable units so that beneficial factors can be presented “on a scale that gives high value to high benefit and low value to low benefit” [
3) Weight allocation: reflects relative importance of the layer to a specified goal and objective e.g. Allocation of the highest weight to an attribute considered most important/significant.
The application of analytical methods involving human behaviour, socio-economic and environmental factors in decision making are integrated in geospatial analysis using MCDA to evaluate geographically define alternatives [
To improve this, [
The paper investigated network of major oil pipelines in the Degema oil field area of south-western part of Rivers state of Nigeria. The area covers approximately 1939 km2 with about 374 communities and a population of around 1.26 million distributed across eight (8) local government areas of the state i.e. Abua/Odual, Akuku Toru, Asari Toru, Degema, Emuoha, Portharcourt, Okirika and Obio/Akpor. The land is generally between 2 to 5 meters above sea level and the vegetation comprises of mangrove forests and fresh water swamps. It is a wetland area with several parts exhibiting seasonal inundation during raining seasons (
A SPOT satellite image of the area was obtained for generating spatial datasets through onscreen digitisation and supervised classification in ArcGIS 10. Community point and boundary shapefiles was obtained from commercial vendors in Lagos state, Nigeria while population data was collected from the National Population Commission also in Nigeria for the purpose of the paper. The criteria/alternatives considered relevant for achieving the goal of the paper, which was to delineate pipeline impact and hazard areas in the study area. The identified objectives were 1) source of impact (oil facility) and 2) land-use land cover attributes, from these alternatives like a) proximity to pipeline, b) proximity to river, c) land cover and d) population density were derived to highlight the existence of pollutant linkages and potential risk of exposure to pipeline hazard.
The methodology for the model is outline in
The first step in the AHP process is the 1) AHP hierarchical design, 2) pairwise comparison of elements, and 3) priority rating/ranking [
A factor is a deliberate condition imposed on a variable to enhance its capability to satisfy established goal or
objective, while constraint is a condition imposed on a variable to limit its ability to satisfy the same goal or objective. For example, conditions excluding an area based on distance, absence, or availability of a feature. The constraint and factor conditions delimiting proximity of pipelines, river, population (settlements), and land cover to source of hydrocarbons discharge are listed in
Weights were allocated via pairwise comparison matrix in
In allocating the weights, values in each column was summed and used to generate normalised score for criterion in stage 2 of
SNo. | Map layer | Description |
---|---|---|
1 | Land cover (Supervised Classification) | This represents the natural vegetation and land resources available for land-use benefits, consists of mangrove forest, forested fresh water swamp, rain-fed agriculture land, grazing field etc. ( |
2 | Pipeline(Digitisation) | This consists of series of connected pipelines used to transport crude oil to various destinations, represents the main source of petroleum hydrocarbon discharge in the area ( |
3 | Communities (Supervised classification) | Is used as a surrogate for population distribution marked in point density describing population distribution per square kilometer ( |
4 | Rivers and creeks (Supervised classification) | This represents the major and minor rivers (creeks). River (water) transport a common means of transportation and source of domestic water supply and other economic purposes like fishing. This layer is an important variable in the socio-economic life of the rural Niger Delta and yet it influences the migratory capacity of oil spills ( |
Degree of importance/significance of attributes | ||||||||
---|---|---|---|---|---|---|---|---|
Equal | Equal to moderate | Moderate | Moderate to strong | Strong | Strong to very strong | Very strong | Very to extremely strong | Extremely strong |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
Criterion | |||
---|---|---|---|
Factor (in) | Constraint (out) | Procedure | |
Proximity to pipeline | ≤4 km | >4 km | Buffer at 0.5 km interval |
Proximity to river | ≤4 km | >4 km | Buffer at 0.5 km interval |
Land cover | N/A | N/A | All land cover |
Population | N/A | N/A | Point density |
Criterion | Stage 1: Score | Stage 2: Standardisation | |||||||
---|---|---|---|---|---|---|---|---|---|
PP | PR | LC | P | PP | PR | LC | P | ||
Proximity to pipeline (PP) | 1 | 3 | 5 | 9 | 0.61 | 0.66 | 0.54 | 0.50 | |
Proximity to river (PR) | 1/3 | 1 | 3 | 5 | 0.20 | 0.22 | 0.32 | 0.28 | |
Land cover (LC) | 1/5 | 1/3 | 1 | 3 | 0.12 | 0.07 | 0.11 | 0.17 | |
Population (P) | 1/9 | 1/5 | 1/3 | 1 | 0.07 | 0.04 | 0.04 | 0.06 | |
Total | 1.64 | 4.53 | 9.33 | 18 | 1.00 | 1.00 | 1.00 | 1.00 | |
Example 1:1/1.64 = 0.61. (Stage 2).
Criterion | Stage 3: weight calculation | Weight | |
---|---|---|---|
Proximity to pipeline (PP) | [0.61 + 0.66 + 0.54 + 0.50]/4 | 0.58 | |
Proximity to river (PR) | [0.02 + 0.22 + 0.32 + 0.28]/4 | 0.26 | |
Land cover (LC) | [0.12 + 0.07 + 0.11 + 0.17]/4 | 0.12 | |
Population (P) | [0.07 + 0.04 + 0.04 + 0.06]/4 | 0.05 | |
Total | 1.00 | ||
The CR is determined by dividing consistency index (CI) by number of criteria (n), thus if the RI value is greater than the RI allocated to particular number of criteria, the weight allocation is considered inconsistent. Random inconsistency (RI) is an index for measuring the consistency of weight allocation. A good CR for 4 criteria according to [
Lambda (λ) is obtained by dividing the sum of values from step 5 by the total number of criterion (4), for example:
where (λ) = 4.11, n = 4; thus
Here CI = 0.04, and RI = 0.90 (from
Since a CR of 0.04 was obtained from the weights distribution in this analysis, it can be adjudged to be consistent because it is less than 0.9.
The overlay was done with WLC to produce a hazard map indicating distance from the centre of potential source of hazard (oil pipeline), the closer a criterion is to the source (origin) the higher the score given. Land cover and population were considered purely on economic factor; while population was treated the same so that all human population receive equal scores irrespective of size and distribution as in
The model builder tools in ArcGIS 10 provide means for automating spatial analysis. The model structure shown in
N | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 15 |
---|---|---|---|---|---|---|---|---|---|---|
RI | 0.00 | 0.00 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.59 |
Criterion | Stage 4: | Stage 5 | ||
---|---|---|---|---|
Proximity to pipeline (PP) | 2.41/0.58* | 4.18 | ||
Proximity to river (PR) | 1.06/0.26 | 4.16 | ||
Land cover (LC) | 0.47/0.12 | 4.03 | ||
Population (P) | 0.21/0.05 | 4.07 | ||
*Divide by criterion weight from
Criterion Categorisation | Subhead Scale | Subhead Attribute Weight | Consideration |
---|---|---|---|
Proximity to Pipeline (km) | |||
0.0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0 3.0 - 3.5 3.5 - 4.0 | 9 8 7 6 5 4 3 1 | 0.58 | Physical Distance |
Proximity to River (km) | |||
0.0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0 3.0 - 3.5 3.5 - 4.0 | 9 8 7 6 5 4 3 2 | 0.26 | Economic Distance Physical |
Land Cover | |||
Agric-Cultivation Fadama Plantation Minor River Major River Rain-Fed Agriculture Forested Freshwater Swamp Minor Urban Mangrove Forest Major Urban | 8 6 3 2 7 6 4 8 4 | 0.12 | Economic Environment |
Population Density | |||
Less-500 501 - 5000 5001 - 15,000 15,001 - 20,000 25,001-over | 9 9 9 9 9 | 0.05 | Economic Social Environment |
Scale: 9 = Extremely high, 8 = Very very high, 7 = Very high, 6 = Moderately high, 5 = Moderate, 4 = Moderately low, 3 = Very low, 2 = Very very low, 1 = Extremely low.
ture to show the type of tools and steps executed (i.e. blue = criteria map layers, yellow = tool, and green = output of a process).
Three outputs were generated in
where: ehh = extremely high; vhh = very high; and hh = high hazard zones respectively.
All settlements and land features of economic interest within the PPIR buffer (
Rivers and creeks surrounding these settlements may serve as pathways to migrating hydrocarbons and point of contact with the people thereby posing significant risk to human health.
In the light of the above, a HCA is an area with potentials to cause risk or damage to properties and exposure to human beings through land use activities e.g. farming, hunting, water consumption etc. However, the intensity of exposure may decrease due to contaminant’s loss of concentration with distance from source, while exposure can increase as people come in direct contact with the source point. Hence:
1) properties like farms, fishing grounds etc. that are outside the HCA would not be affected;
2) people working in and living in settlements within the HCA are likely to be the most affected; and
3) people working or living outside the HCA may not be affected.
The percentage (number) of settlements, population, rivers and land cover identified in the HCAs and non- HCAs are shown in
Sensitivity analysis examines how changes in criterion weight would affect the model, this is important for judging the influence of weight allocation that is based on subjective and or personal preference [
There has been wide-range application of MCDA in different disciplines, e.g. urban and regional planning, nature conservation, natural hazard risk management, and in transportation [
Category | HCA (%) | Non-HCA (%) |
---|---|---|
Settlement | 231 (61.9%) | 142 (38.1%) |
Population Est. No. of Household3 Male Female Under 14 years | 909,519 (69.4%) 113,689.9 410,464.2 499,054.8 333,496.2 | 401,178 (30.6%) 50,147.2 181,052.1 220,125.9 147,231.7 |
River (sq.km) Major Minor (Creeks) | 134,470.9 (39.2%) 101,099.3 (sq.km) 33,371.6 (sq.km) | 208,680.3 (60.8%) 162,621.5 (sq.km) 46,058.8 (sq.km) |
Land Cover (sq.km) Fadama Plantation Freshwater Swamp Grazing Field Mangrove Forest Mixed-Cultivation Others | 765,275.5 (39.9%) 16,417.9 (sq.km) 63,037.7 (sq.km) 9734.9 (sq.km) 628,752.7 (sq.km) 27,646.3 (sq.km) 19,686 (sq.km) | 1,154,097.9 (60.1%) 26,592.7 (sq.km) 147,929.6 (sq.km) 9506.3 (sq.km) 565,756.4 (sq.km) 35,014.5 (sq.km) 369,298.4 (sq.km) |
S No. | Criterion | Weight in Percentage (%) | |||
---|---|---|---|---|---|
Original | 1st Test | 2nd Test | 3rd Test | ||
1 | Land Cover | 12 | 58 | 26 | 5 |
2 | Population Density | 5 | 26 | 12 | 58 |
3 | Proximity to River | 26 | 5 | 58 | 12 |
4 | Proximity to Pipeline | 58 | 12 | 5 | 26 |
From literature, the original PIR demarcation is dependent on pipeline parameters that are not easily available in the public domain in Nigeria for security reasons, according to the oil companies. The model developed herein provides a new approach using easily available data and expert knowledge that can be acquired in the public domain to provide means of public participation in pipeline hazard management in the area. The method (MCDA) established the PPIR from the interaction of social, economic and physical attributes to identify land use areas, river systems and communities likely to be affected directly or indirectly by oil pipeline spill hazard. These areas represent places where pipeline failure is likely to cause significant adverse impact on human population, source of domestic water, and ecologically sensitive receptors. [
From the weights derivation, proximity to pipeline has the highest score of 0.58, proximity to river 0.26, land cover 0.12, and population 0.05. It is obvious that proximity to pipeline is a very significant (important) criterion being the main source of hydrocarbon discharge in the area. Proximity to river was second; considering the behaviour of crude oil on water and the land being a wetland area. The rise and fall of water levels due to seasonal inundation can promote easy migration of free and trapped hydrocarbons into adjacent water bodies and land use areas through vertical spreading and surface run-off [
The HCAs in the context of land use are settlements from where human movement originates, river and creek networks that serve as fishing ground, source of domestic water and abstraction points, and land cover on which farming, hunting, and wild gathering is conducted
An automated tool that can be used to monitor human (settlement) susceptibility to pipeline hazards in wetland areas has been developed for public participation and collaboration in pipeline hazard management and
・ Help decision makers since the platform (model) has the ability to maintain itself and update existing database of HCAs each time it is run. This would provide updated information on population growth and land use expansion relative to pipeline network.
・ The information generated from desktop assessment would improve collaboration with new communities that just qualified as HCAs, thereby reducing operational cost and facilitate direct community base participation in the fight against oil pollution and pipeline interdiction in the area.
・ The regular updating of high consequence communities as new ones emerge would help in equitable distribution of response facilities and resources for mitigating risk of prolong exposure to impact of pipeline hazards.
・ The identification of land use areas in HCAs can provide useful information for land use planners and policy makers in the oil and gas sector as well as provide impetus for driving environmental sustainability in oil production activities in the region.
・ It is also recommended that responsible government agencies like the National Oil Spill Detection and Response Agency (NOSDRA), the Department of Petroleum Resources (DPR) and the Nigerian National Petroleum Corporation (NNPC) and the Federal/State Ministry of Environment to be equipped with the capabilities (employing experts and technology) to deploy this technology/technique in pipeline hazard risk management and assessment in the oil producing areas of Nigeria.
The authors would like to thank the Department of Geography of the University of Lagos, and individuals like Mr. Albert Ndubizu, Chief I. C. Okoro, Mr. Olumiyiwa for their assistance during data collection. Special thanks to Prof. Nathanail C. Paul and Dr. Robert Abrahart and the Petroleum Technology Development Fund (PTDF) Abuja for funding the research.
ShittuWhanda,YahayaSani,GadigaBulus, (2015) Modelling of Potential Pipeline Impact Radius and High Consequence Area in a Wetland Sub-Region of Nigeria. Journal of Geographic Information System,07,692-709. doi: 10.4236/jgis.2015.76056