... on background-foreground segmentation, objects detection and classification, tracking, and behavioral analysis have covered a large portion of these investigations. Other than visible cameras, many sensor modalities have been investigated for surveillance systems, including thermal and infrared cameras, radar...

... main categories of ESD valves considered; i. Emergency Service (ESDV & EBDV), ii. Inventory Isolation, iii. Pipeline Sectioning/Isolation and iv. Process Service (Routing, Sequencing, etc.) air emission gas processing valve chemical spill midstream oil & gas spam filtering detection...

... when they tend to compromise on safety, is of huge value. While many attempts have been made in the literature in terms of Personnel Protective Equipment (PPE), there is no available solution that uses modern deep-learning based video analytics to comprehensively detect multiple type of HSE use-cases...

... inspection machine vision pipeline detection upstream oil & gas shell autonomous integrity recognition application workflow shell autonomous integrity recognition corrosion cloud insulation machine vision application Introduction Shell assets across the lines of business like refineries...

... data obtained from the autonomous 3D maneuvering around the flare stack is analyzed using a deep learning detection network for flame and smoke detection for performance monitoring. The results of the analysis provide conclusions about the overall system operation and structure condition. Related...

... One aspect is related to the human factor. With the increasing number of data generated by sensors and other activities, the responsible person gets easily overwhelmed by the well monitoring task. It is also challenging for the human eye to detect a slow pressure decrease or increase...

... reconciliation algorithm coupled with a stead state detection algorithm creates first the exact replica or digital twin of the process plant. An optimization algorithm then identifies the true gap to potential and provides the advisory targets. The application solution is scheduled to run every hour mapping...

... Abstract In this work, we present a novel Artificial Intelligence (AI) powered non-destructive testing (NDT) system for the detection of potential corrosion under insulation (CUI) inspections, code named DPCUI, developed by the Research and Development Center (R&DC) at Saudi Aramco...

... learning algorithm to detect anomalous sound amidst industrial noise, which in turn helps in the identification of the malfunctioning equipment on an offshore rig. The recent success of contrastive learning algorithms to detect novelty in visual representation has inspired our work. We hypothesise...

... and limits defined in the system. Process simulation model predicts other operating parameters not covered by the existing instrumentations, serving as virtual analysers, providing near real-time equipment and unit performance. Data driven model can be used for prediction, optimization and anomaly detection...

... Abstract The paper addresses the integrity monitoring of critical infrastructures as fluid filled transportation pipelines by developing a pilot full scale application of Advanced Leak Detection (ALD) monitoring system. The objective of the ALD system is to improve performances with respect...

... to interpreting logs or using core data. The purpose of this paper is to discuss machine-learning (ML) applications grouped in the solution to automatically QC and process core and log data for hundreds of wells. The goal was to detect poor data and outliers to apply corrections that mimic human-led...

... is to discuss the detailed implementation of machine learning to automate the detection and computation of the KPI's in real-time. The existing method for drilling performance monitoring requires extensive human data interpretation to calibrate the parameters required in this process. To overcome...

... the model training process by utilizing synthetic images. This will present cost and time reduction as well as the ability to train models for features with sparse real data. dataset machine learning flange deep learning accuracy detection upstream oil & gas artificial intelligence...

... and data science work not only led to an increase in the mean intersection-of-union metric but also to the assurance that fundamental geological principles were honored. In practice, the algorithm ensured that petrographic object detection was constrained by biostatistical population criteria as well...

.... Extraction of hydrocarbon compounds in samples Soxhlet extraction Abstract The convenient, quick and accurate detection of hydrocarbon compounds in rocks and the establishment of the detection method of hydrocarbon compounds in rocks are of great significance for the study of hydrocarbon compounds...

... failure due to third party and geohazards. Data automation has significanly reduced data processing time and human error for anomalies detection and verification, this subsequently improve workforce productivity while reducing safety risks, operations and maintenance costs. neural network machine...

... are tough to find in battery-powered IoT due to their battery lifetime limitations. upstream oil & gas battery internet of things artificial intelligence sustainability sustainable development vibration sensor climate change frequency leak detection operation steam leak monitoring...

... Abstract This work aims to present a methodology based on a proactive approach for the early detection of possible liquids carryover towards compressors installed in upstream process plants where the presence of heavy hydrocarbon inside gas stream is a recursive problem. Analyzing trough remote...

... Abstract One of prerequisite of any detection system is to get the requirement the risk analysis that estimates mainly the safety and environmental impacts of a loss of containment. From this prerequisite it is possible to consider a strategy for an early detection of a loss of containment...