Spatio-temporal analysis of oil lake and oil-polluted surfaces from remote sensing data in one of the Libyan oil fields

Abstract

The study area, which is part of the Sirt sedimentary basin in the north-central part of Libya, is characterized by natural resources of important environmental value that need special attention as they are threatened by many human activities. The focus of this study was mainly on the production of high-resolution maps of oil-contaminated surfaces, and the series time maps of events resulting from oil pollution using multi temporal satellite data and validation of the results. Digital image processing techniques were used on satellite-based sensing, whether optical or radar data, which proved to be a cost-effective way to collect information on the volume of lake water, and to assess the depth and concentration of pollution in the study area rich in lakes taken from different periods (1972 to 2006). The area of the oil-contaminated lake, called produced water, was calculated from the 1972 Landsat MSS digital satellite imagery data and was about 1.8 km2 and then increased to 10.7 km2, during 2006 from Landsat digital image TM data. The size change in this area was due to the increase of the quantities of water production that continued to increase as the oil and gas fields reached maturity. The 2019 Landsat satellite imagery reveals a drastic shrinkage in the area of the lake attributed to the suspension of the produced water pumping as well as the cycle of evaporation that resulted to the water led to a limited volume of water remaining in the lake.

Spatial and temporal distribution of surface water contaminants in the Houston Ship Channel after the Intercontinental Terminal Company Fire

“…For example, in a stationary body of water (i.e., in a lake), the anaerobic biodegradation prevails over dissolution for toluene and o-xylene; however, for benzene, ethylbenzene, and m,p-xylene the dissolution is the primary path for reducing the concentrations over time 25 . Longitudinal studies of hydrocarbon substance-contaminated water in oil-producing regions are conducted using either remote sensing 26 , or physical sampling with extensive chemical analyses 27 . Albeit few previous studies conducted as granular and extensive chemical analysis as one that was performed after the ITC fires, the findings in our study of a rapid gradual decline in concentrations of spill-derived contaminants is commensurate with the observations in surface water samples from other large-scale oil spills 27 - 29 .…”

Assessing LAPAN-A3 Satellite with Line Imager Space Application (LISA) Sensor for Oil Spill Detection

“…The oil spill occurrence is studied by developing falsecolor composites (FCC) according to the OIF calculation result. The oil spill features can be revealed by displaying different color composites through the multispectral image, and it will optimize the differentiation of certain specific features of the observed object [38]. Four composite color combinations are developed in RGB format and visually interpreted.…”

“…The appearance of oil on LA3 is brownish and on Sentinel-2 is a dark pattern. The oil layer has a reflectance contrast at 400-700 nm [38]. Sensors with the visible to near-infrared (VNIR) spectral range can recognize the spectral change associated with oil-covered water [39].…”

“…The 660 -760 nm range is the best for detecting oil spills in coastal. Meanwhile, Abdunaser [38] explained that concentrated oil would appear brownish with a wavelength of 600-700 nm, and if it is close to 700 nm, it will be brownish-red. Pure oil can be detected in a narrow band of reflection at NIR-SWIR reflectance [43].…”

Pemanfaatan Sistem Informasi Geografis Berbasis Web Dalam Meningkatkan Akurasi Informasi Terkait Rekam Jejak Sumur Minyak Dan Gas Bumi Di Pulau Madura

“…Penggunaan webgis saat ini telah berkembang untuk berbagai sektor seperti pemanfaatannya dalam pembangunan wilayah seperti smart city (Hari Ginardi et al, 2017), pendidikan, integrasi basis data biota laut (Agurto et al, 2018), integrasi dengan sistem cloud computing (De Lázaro Torres et al, 2017;Fortunato et al, 2017;Helmi et al, 2018;Piragnolo et al, 2021;Sciacca et al, 2022;Xia et al, 2015), kesehatan (Barik et al, 2019;Mooney & Juhász, 2020), bibliografi dalam penelitian (Howell et al, 2019), manajemen mitigasi kebencanaan (Capolupo et al, 2021;Fauzia et al, 2021;Feng et al, 2012;Franceschini et al, 2022;Gang et al, 2016;Gebreegziabher et al, 2021;Xianfu et al, 2012), forecasting (Fortunato et al, 2017), pariwisata (Klug et al, 2012), minyak dan gas bumi (Abdunaser, 2020;Khan & Hasan, 2020;Li, 2020;Russell et al, 2015;Valensise et al, 2022), hingga dalam memberikan solusi kebijakan dalam penentuan lokasi pembangkit listrik power to gas (La Guardia et al, 2021).Penggunaan perangkat lunak dalam bidang geospasial sangat disarankan sesuai dengan amanat UU No 11 Tahun 2011 tentang Informasi Geospasial pasal 31 yang menyatakan bahwa pengolahan data geospasial dan informasi geospasial dilakukan dengan perangkat lunak yang berlisensi dan/atau bersifat bebas dan terbuka (Mary Fargher, 2018;Serrano-Estrada et al, 2021).…”