Process Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis, LOPA, etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis.ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O'Connor Process Safety Center -Qatar (MKOPSC-Q) to develop a methodology that establishes a framework for a tool that monitors in real time the potential increases in risk levels as a result of pre-identified risk factors that would include the use of KPIs (leading or lagging) as observations or evidence using Bayesian Belief Networks (BN).In this context, the paper presents a case study of quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BBN, modification of the skeletal BN to incorporate KPIs (loss of primary containment (LOPC), equipment, management and human related), and testing of the BBN with forward and backward inferences. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made.

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The U.S. Environmental Protection Agency (EPA) has promulgated regulations governing the detection and repair of equipment leaks that cause fugitive emissions of volatile organic compounds (VOC). These regulations are embedded in various emission standards and are generally referred to as Leak Detection and Repair (LDAR) programs. The primary method used currently in the U.S. to detect leaks is EPA Method 21. 1 Method 21 requires operators to use portable instruments, typically a Flame Ionization Detector (FID) or a Photon Ionization Detector (PID), to ЉsniffЉ around the circumference of individual equipment components (e.g., valves, flanges, pump seals, etc.) 2 . If the detector reading (parts per million or PPM) is higher than target thresholds, the component is deemed to be leaking and it must be repaired within a certain time. Fugitive VOC emissions from a facility are calculated based upon the PPM readings (referred to as screening values or SVs) and empirical correlations between SVs and mass emission rates. 2 Because the leak check is performed on each individual component basis, the implementation of a Method 21 based LDAR program is tedious, labor intensive, and prone to errors.Optical gas imaging (OGI) technology has been developed and can be used to detect VOC leaks from process equipment. The OGI technology allows operators to use a specially designed Infrared (IR) video camera to see VOC plumes leaking from components that are not visible to the naked eye. Detecting VOC leaks using OGI is more efficient than Method 21 because leak checking using OGI is visual, making detection faster, and can be performed over an area instead of component-bycomponent. The OGI method allows operators to detect larger leaks easily and more frequently, achieving the same environmental benefit with a lower cost. For this reason, the OGI method is also referred to as ЉSmart LDARЉ. In December 2008, U.S. EPA promulgated the ЉAlternative Work PracticeЉ (AWP) rule allowing operators to use OGI for LDAR compliance. 3 However, the AWP rule requires operators to continue to perform leak checks using Method 21 at least once a year.Although OGI can be very effective in detecting leaks, it does not provide a quantitative measure of leak rate. This has been one of the shortcomings of OGI from a regulatory perspective, thereby hindering its adoption as a true alternative to Method 21. This paper describes development of quantitative OGI (QOGI) technology. Existing OGI camera technology is the basis the new GOCI technology. If an OGI camera detects a leak, then, the operator can apply the new QOGI technology quantify the mass leak rate from the captured video images.

ExxonMobil Research Qatar and Providence Photonics, LLC, a U.S. based firm, are undertaking research to develop a Remote Gas Detection (RGD) system that integrates computer vision algorithms and infrared (IR) optical gas imaging technology to achieve autonomous remote detection of hydrocarbon plumes. The RGD system is designed to provide continuous surveillance and early warning to operations personnel in case of gas releases and to detect fugitive gas emissions. The RGD system utilizes a custom built IR imager and integrated cooler assembly, and a computer vision algorithm that analyzes the video output from the IR imager to determine the presence of hydrocarbon plumes. Most hydrocarbons have strong absorption peaks in a narrow mid-wave IR (MWIR) region. The algorithm takes advantage of the difference in contrast between a hydrocarbon plume and the background in an IR image and the temporal changes due to plume behavior for the analysis. The algorithm compares sequentially collected IR images and uses a multi-stage confirmation process to confirm the detection. It has multiple filters that mitigate interferences like steam and other movement of objects in the scene such as humans, vehicles, and trees. Early field tests indicate that a 4 lb/hr propane leak could be autonomously detected from a distance of up to 800 feet. The RGD camera assembly enclosure is designed to obtained explosion proof certification using the ATEX standard for deployment at classified/hazardous areas in oil and gas processing facilities. Instrument air provides cooling and is used to purge the system. Multiple deployment opportunities at process facilities are currently underway. Results from field testing at these process facilities will help researchers investigate the effect of temperate and harsh weather conditions, the effect of varying temperatures and gain a better understanding of equipment wear and tear, maintenance requirements and life expectancies. These data sets will produce an accurate assessment of the performance of the RGD system under actual working conditions and will be used to qualify the technology for widespread adoption within the industry. Work has also been undertaken to compare the performance of the RGD system versus existing detection technologies. The most common leak detection technology is point sensors and path infrared sensors. This technology requires dispersed gas to physically contact the point sensors or move between two path detectors. Field tests are used to compare the performance of these mature technologies to the capabilities of RGD.