Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are sent to the base station. Using deep learning approaches such as holistically-nested edge detection (HED) and extreme inception (Xception) networks, images are analyzed at the base station to identify contours using dense extreme inception networks for edge detection (DexiNed). This algorithm is capable of finding many contours in images. Moreover, the CIELAB color space (LAB) is employed to locate black-colored contours, which may indicate oil spills. The suggested method involves eliminating smaller contours to calculate the area of larger contours. If the contour's area exceeds a certain threshold, it is classified as a spill; otherwise, it is stored in a database for further review. In the experiments, spill sizes of 1m2, 2m2, and 3m2 were established at three separate test locations. The drone was operated at three different heights (5 m, 10 m, and 15 m) to capture the scenes. The results show that efficient detection can be achieved at a height of 10 meters using the DexiNed algorithm. Statistical comparison with other edge detection methods using basic metrics, such as perimage best threshold (OIS = 0.867), fixed contour threshold (ODS = 0.859), and average precision (AP = 0.905), validates the effectiveness of the DexiNed algorithm in generating thin edge maps and identifying oil slicks. © 2023 Lavoisier. All rights reserved.
Medulloblastomas and ependymomas are the most common malignant brain tumors in children. However genetic abnormalities associated with their development and prognosis remain unclear. Recently two gene fusions, KIAA1549–BRAF and SRGAP3–RAF1 have been detected in a number of brain tumours. We report here our development and validation of RT-RQPCR assays to detect various isoforms of these two fusion genes in formalin fixed paraffin embedded (FFPE) tissues of medulloblastoma and ependymoma. We examined these fusion genes in 44 paediatric brain tumours, 33 medulloblastomas and 11 ependymomas. We detected both fusion transcripts in 8/33, 5/33 SRGAP3 ex10/RAF1 ex10, and 3/33 KIAA1549 ex16/BRAF ex9, meduloblastomas but none in the 11 ep
... Show MoreBackground: Osteoporosis is denoted by low bone mass and microarchitectural breakdown of bone tissue, directing to increased fracture risk and bone fragility. Fractures may lead to a decreased quality of life and increased medical costs. Thus, osteoporosis is widely considered a significant health concern.
Objective. This study aimed to compare quantitative computed tomography (QCT) and dual-energy X-Ray absorptiometry (DXA) to detect osteoporosis in postmenopausal women.
Subjects and Methods. We measured spinal volumetric bone mineral density (BMD) with QCT and areal spinal and hip BMD with DXA in 164 postmenopausal women. We calculated the osteo
... Show MoreGas-lift technique plays an important role in sustaining oil production, especially from a mature field when the reservoirs’ natural energy becomes insufficient. However, optimally allocation of the gas injection rate in a large field through its gas-lift network system towards maximization of oil production rate is a challenging task. The conventional gas-lift optimization problems may become inefficient and incapable of modelling the gas-lift optimization in a large network system with problems associated with multi-objective, multi-constrained, and limited gas injection rate. The key objective of this study is to assess the feasibility of utilizing the Genetic Algorithm (GA) technique to optimize t
This effort is related to describe and assess the performance of the Iraqi cement sample planned for oil well-cementing jobs in Iraq. In this paper, major cementing properties which are thickening time, compressive strength, and free water in addition to the rheological properties and filtration of cement slurry underneath definite circumstances are experimentally tested. The consequences point to that the Iraqi cement after special additives encounter the requests of the API standards and can consequently is used in cementing jobs for oil wells. At this research, there is a comparative investigation established on experimental work on the effectiveness of some additives that considered as waste materials which are silica fume, baux
... Show MoreThis effort is related to describe and assess the performance of the Iraqi cement sample planned for oil well-cementing jobs in Iraq. In this paper, major cementing properties which are thickening time, compressive strength, and free water in addition to the rheological properties and filtration of cement slurry underneath definite circumstances are experimentally tested. The consequences point to that the Iraqi cement after special additives encounter the requests of the API standards and can consequently is used in cementing jobs for oil wells. At this research, there is a comparative investigation established on experimental work on the effectiveness of some additives that considered as waste materials which are silica fume, bauxite,
... Show MoreThe current study focuses on utilizing artificial intelligence (AI) techniques to identify the optimal locations of production wells and types for achieving the production company’s primary objective, which is to increase oil production from the Sa’di carbonate reservoir of the Halfaya oil field in southeast Iraq, with the determination of the optimal scenario of various designs for production wells, which include vertical, horizontal, multi-horizontal, and fishbone lateral wells, for all reservoir production layers. Artificial neural network tool was used to identify the optimal locations for obtaining the highest production from the reservoir layers and the optimal well type. Fo
The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage (5, 10 and 20 wt.% ) of (n-heptane, toluene, and a mixture of different ratio
... Show MoreLarge quantities of petroleum-contaminated soil are generated with increased global energy consumption and crude oil production. This theoretical study evaluates the treatment of 1 ton of petroleum-contaminated soil using seven methods: incineration, physical washing, chemical washing, thermal pyrolysis, Fenton-oxidation-pyrolysis, the biological treatment, and asphaltenes. Data were based on experimental results from the Nahran Bin Omar oil lake in Basra Governorate, Iraq, (2019–2021). The methods were compared by waste generation, treatment cost, and duration. Results indicate that using petroleum-contaminated soil as a raw material for asphalt manufacturing is most beneficial since it is sold as a raw material. Incineration is faster a
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