Spatial Temporal Patterns of Bayelsa State Spdc Related oil Spill from 2011 to 2019

International Journal of Research and Innovation in Applied Science (IJRIAS) | Volume VI, Issue III, March 2021|ISSN 2454-6194

Spatial Temporal Patterns of Bayelsa State Spdc Related oil Spill from 2011 to 2019

Sam Mercy David¹ and Okujagu Diepiriye Chenaboso^1,2
1Department of Geology, University of Port Harcourt, Nigeria
²Center for Petroleum Geosciences, University of Port Harcourt, Nigeria

ABSTRACT
Oil spills is a global issue which causes serious pollution to the environment. In this study, spatio-temporal models of the distribution of oil spills in Bayelsa State, Nigeria, from 2011 to 2019, were analyzed using ARCGIS software based on available data from the SPDC JIV report. The results showed a total of 234 spills in Bayelsa State from 2011 to 2019. The estimated spill volume is 25,501.30 barrels, with 2015 having the highest spill volume of 22% and 2019 the lowest spill volume of 3%. The spill affected an area of 14,789,570.02 m2, with the year 2012 having the most affected area (65.5%) and 2017 the lowest (1%). Swamp recorded the most spill incidents at 67% followed by land at 29%, water 3% and land / swamp at 1%. The main causes of the spills are sabotage (66.5%), operational failures (30.7%), others (2.3%) and the mysterious spill 0.5% (spills whose cause is not established). The main oil spill facilities such as oil pipelines, flow lines, well head, delivery line, flow station, manifold, trunk line, and buckline, accounted for 60% of the spill and other facilities accounted for 40% of spills. The main oil spill leak points are hack saw cut 35%, crude oil theft, 16%, corrosion 9%, well head tampering6% and the rest (points leakages) 34%. This study presents a spatio-temporal map of the distribution of oil spills in Bayelsa State from 2011 to 2019 on the basis of oil spills related to SPDC.

Keywords: Bayelsa State, oil spills, impact area, causative factors, leak point, terrain, facilities, and volume.

INTRODUCTION

Background Since the discovery of crude oil in 1956 by Shell British petroleum known now as Royal Dutch Shell in Bayelsa State at Oloibiri village (Anifowose 2008, Onuoha 2008) there has been enormous issues regarding oil spillage, which has improved the discuss on how to tackle oil spills.The role of SPDC in Shell Nigeria’s oil/gas family is usually limited to physical production and crude oil extraction/natural gas: oil spills in the Niger Delta wetlands and marine ecosystems during SPDC’s E and P activities in Nigeria The incident has been reported in the public oil spill documents since 2010, which is the main cause of the oil spill. According to reports, these leaks were caused by theft (refuelling), operational failures, and equipment at wellheads, pipelines and other facilities. As many as 400,000 barrels of oil are spilled every day, and more than 1,010 barrels of oil are spilled, of which 110,535 barrels of oil are equivalent to SPDC, which is said to have lost 17.5 million litres. Since 2011, this continuous environmental degradation has led to the establishment of the National Office of Oil Spill Detection and Response (NOSDRA) in 2006. This study aims to analyse Bayelsa State oil spill related to the SPDC JIV report from 2011 to 2019 using Geographic Information System (GIS) to map the spatiotemporal nature of the spill across the state as it relates to causative factors, spilling facility, spill terrain, spill leak points, spilled volume and area impacted by spill.

Literature review
Many scholars have conducted research on various aspects of Bayelsa State oil spills and its impacts on the environment. Some of the highlights of these researches include: Luiselli & Akani 2002 assessed oil pollution, diversity and the functioning of turtles in the Niger Delta, Nigeria. The study found a significant reduction in the specific variability of the turtles with 50% loss after oil spill, with a reduced number of turtle that were able to survive the oil spill. Ordinioha & Sawyer 2008 investigated Food Insecurity, Malnutrition and Crude Oil Spillage in a Rural Community in Bayelsa State, South-South Nigeria. The results show that the crude oil spill may exacerbate household food insecurity and child malnutrition. Ordinioha & Sawyer 2010 probed the acute health effects of a crude oil spill in a rural community in Bayelsa State, Nigeria. The results confirmed that exposure to the fog and fumes caused by the crude oil spill has had some health effects, although they are mild and temporary.

SPDC Geomatics 2012 mapping spill incidents in the Niger Delta. The findings suggest that oil spills around the Kolo Creek area are due to corrosion of pipelines and tankers (accounting for 50% of all spills), sabotage (28%), oil production operations (21%), with a 1% leakage for inadequate or impractical production equipment. The International Union for Conservation of Nature (IUCN) Kolo Creek Biophysical Report 2013 shows that Kolo Creek in Bayelsa’s Ogbia Local Government Area (LGA) in southern Nigeria’s Niger Delta is experiencing stress, which is likely to occur from the regular oil spill in the area. It could also be viewed as an indication that the environment and ecosystems are not fully recovered and it may take some time to fully recover. kabiamaowei et; al 2017 examined the impact of oil operations in the Epebu community. Results, found that oil operations had negative effects on the environment and on the lives of host communities, such as: oil spills, water contamination, crises between communities and within the community, reducing economic activities, etc.

The Bayelsa State Oil & Environmental Commission Interim Report 2019 shows that over 50 years oil company activities and the associated impacts have caused endless disasters in the Bayelsa State. Umar et; al 2019 has developed a spatial database for oil spill pollution affecting the water quality system in the Niger Delta. The results of the study found that the three states of the Niger Delta (Bayelsa Rivers and Delta) were the ones where oil spills and distribution were the most, and were confirmed with ER charts showing correlations between sources of pollution pathway and receptors.

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