A New Approach for Field Surveillance: Enhanced Monitoring Associating Smart Alarms To Produce More Oil
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In recent years, Underground Gas Storage (UGS) has earned itself strategic importance as it guarantees energy sustainability in markets suffering from unpredictable supply. Storage management is a complex activity which faces the challenge of combining the variability of daily commercial client requests along with the capability of the reservoirs to deliver. Gas production and re-injection activity requires standard core competencies of the oil and gas industry, however the process is fast paced as compared to conventional hydrocarbon production activity as time scales from data analysis to decision making is reduced to hours. Stogit Spa, an Italian gas storage company managing 8 depleted gas fields in Italy has implemented an integrated system that dynamically links databases, visualization tools and reservoir/well simulators in order to assist the management process. This system called PERSEO (PErformance Reservoir StoragE Optimization) is aimed at enhancing efficiency by utilising a piece of intelligent fields application. The large amount of data from the 270+ wells of STOGIT equipped with SCADA systems monitoring real time gas rate and well head pressure, provides information for production/injection management. The challenge has been to extract the maximum information from the available database and computerize the process of updating well/reservoir performance automatically. This allows for faster monitoring, analysis and prediction of future well behaviour. A workflow was realised to transform available real time data into calibrated models in the following two steps:*Design an algorithm capable of filtering stabilised gas rates and wellhead pressures for every injection/production period to extract data representative of the well performance*Automatic updating of well performance (IPR/VLP) model and matching the algorithm filtered data with theoretical and practica 1 models (back pressure C, n equation). This paper describes the outline of the implemented PERSEO system, details of the computerized workflow along with sensitivities and the results obtained. Introduction The concept of "intelligent field" is a promising scenario for the future of oil and gas industry. What is more or less foreseen today (Gomersall 2007, Murray et al 2006, Unneland et al 2005) is the idea of creating a higher automation level in the management of a production asset. The increasing amount of available data, among which real-time subsurface sensors, and the trust in the potential of the "digital" revolution are probably the main drivers of this belief. However the kind of automation required is something really peculiar with the oil industry, where we should always admit that we don't really know what our main asset - the reservoir - looks like. The intrinsic impossibility to directly measure, to "see" the reservoir, brings the petroleum engineer/geologist in the continuous interpretation of indirect data in order to feed more or less complex models. Going from data to models and from those to decisions is the concretization of the value of the whole petroleum engineering activity. Decisions are fastened and enhanced by a value-chain able to define from data a limited number of clear scenarios.
In recent years, Underground Gas Storage (UGS) has earned itself strategic importance as it guarantees energy sustainability in markets suffering from unpredictable supply. Storage management is a complex activity which faces the challenge of combining the variability of daily commercial client requests along with the capability of the reservoirs to deliver. Gas production and re-injection activity requires standard core competencies of the oil and gas industry, however the process is fast paced as compared to conventional hydrocarbon production activity as time scales from data analysis to decision making is reduced to hours. Stogit Spa, an Italian gas storage company managing 8 depleted gas fields in Italy has implemented an integrated system that dynamically links databases, visualization tools and reservoir/well simulators in order to assist the management process. This system called PERSEO (PErformance Reservoir StoragE Optimization) is aimed at enhancing efficiency by utilising a piece of intelligent fields application. The large amount of data from the 270+ wells of STOGIT equipped with SCADA systems monitoring real time gas rate and well head pressure, provides information for production/injection management. The challenge has been to extract the maximum information from the available database and computerize the process of updating well/reservoir performance automatically. This allows for faster monitoring, analysis and prediction of future well behaviour. A workflow was realised to transform available real time data into calibrated models in the following two steps:*Design an algorithm capable of filtering stabilised gas rates and wellhead pressures for every injection/production period to extract data representative of the well performance*Automatic updating of well performance (IPR/VLP) model and matching the algorithm filtered data with theoretical and practica 1 models (back pressure C, n equation). This paper describes the outline of the implemented PERSEO system, details of the computerized workflow along with sensitivities and the results obtained. Introduction The concept of "intelligent field" is a promising scenario for the future of oil and gas industry. What is more or less foreseen today (Gomersall 2007, Murray et al 2006, Unneland et al 2005) is the idea of creating a higher automation level in the management of a production asset. The increasing amount of available data, among which real-time subsurface sensors, and the trust in the potential of the "digital" revolution are probably the main drivers of this belief. However the kind of automation required is something really peculiar with the oil industry, where we should always admit that we don't really know what our main asset - the reservoir - looks like. The intrinsic impossibility to directly measure, to "see" the reservoir, brings the petroleum engineer/geologist in the continuous interpretation of indirect data in order to feed more or less complex models. Going from data to models and from those to decisions is the concretization of the value of the whole petroleum engineering activity. Decisions are fastened and enhanced by a value-chain able to define from data a limited number of clear scenarios.
Integrated Cybersecurity for Modern Information Control Models in Oil and Gas Operations
Modern logical information and control models are the brains that run, monitor, maintain and secure operational facilities. The design objective of these logical systems is to optimize production and performance while minimizing supply chain problems. To achieve this critical objective, information flow, critical data, operational control points, as well as risk points are identified while fitting together the different compartments of these artificial models. This cyber and logical representation of the physical asset environments, such as drilling and workover rigs, is displacing the traditional physical operational models in several domains of the oil and gas industry, including upstream, midstream and downstream. With the extended, geographically dispersed infrastructures of the oil and gas industry, the real-time communication and remote control capabilities are providing privileges to make more robust decisions that optimize deliverables. Additionally, as the added technologies, such as surveillance, are replacing the human element in tough locations, safety records are being boosted by reducing exposure to combustible, harming chemicals and off-road traffic. Automation is often more efficient and safer than human intervention because it offers new operational capabilities, such as forward prediction, swift detection and reaction to events, and shuts down immediately if anomalous activities are indicated in data flow patterns or if signals are lost. For instance, onshore and offshore drilling operations in real-time monitoring and control centers that run land and subsea operations apart from the control room rely on analytics-driven strategies provided by the adopted intelligent systems to harness the full value of operational excellence. This paper explores the design and function of the logical cyber representation of the physical asset environments, whether for drilling wells, producing wells, pipelines, or treatment facilities, to list a few components of the oil and gas supply chain. Physical assets and their controls are different for each compartment, and so are the communication networks and accompanying proprietary software. There are distinctive characteristics for each logical information and control network deployment architecture, depending on the operational requirements and levels of tolerance. This paper also highlights examples where such models have promoted solutions to mitigate uncertainty. For example, forward pore pressure prediction was applied while drilling along the minimum in-situ horizontal stress plane to predict what is ahead of the bit, improve wellbore stability and lateral trajectories, validate data, and prevent human error. The analysis conducted showed that operational efficiency and cybersecurity compromise is essential for business success while constructing the information and control models. The paper discusses three useful tools that assist in promoting integrated cybersecurity for artificial models. The three tools are safety instrumented systems, decision tree, and information risk management.
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