资产完整性管理-前端加载阶段的实施计划

G. Ferrario, Salvatore Grimaldi
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引用次数: 0

摘要

通过定义诊断数字工具的实施计划,以减少停机时间,并在运行期间引入预测性维护,从而在绿地项目开发的前端加载阶段吸取资产完整性管理的经验教训。Eni开发了一个数字应用平台,通过实施集成资产管理(IAM)系统来增强运营管理。高级分析工具是其中的一部分,旨在监测、预测和防止生产紊乱和异常;该工具是通过验证感兴趣的领域和关键,识别主要设备数据集,并通过实施和验证预测模型来建立的。从历史数据出发,在专家的支持下,数据科学家开发出能够找到一组输入变量和输出变量(待预测/监测的现象)之间相互依赖关系的算法,从而检测异常和关键。设想的主要受益领域是生产连续性,能够预测静态和旋转设备的问题,并提供对初期问题影响最大的变量的信息。该工具将支持技术人员,帮助他们防止可能导致生产损失或资产完整性问题的故障和超出规格的事件,激活预测性维护,旨在努力持续监测和改进工厂的运行性能。建立能源效率预测模型,通过对固定燃烧二氧化碳(CO2)排放指数(t CO2/kbbl)的预测,预测资产未来的能源性能,并提供主要影响设备和变量的列表。该工具的实施计划从开发的早期阶段开始,通过简化过渡,避免后续的改造工程和额外的成本,帮助组织优先考虑数字化工具的实施,将其作为交付给运营人员的资产的执行和实现的一部分。自前端加载阶段以来,高级分析工具的实施已嵌入到开发项目的新绿地计划中,从而促进了数字化实施,并通过将其作为提交给合资伙伴和主管部门的投资提案的一部分,最大限度地降低了部署成本。
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Asset Integrity Management - Implementation Plan During Front End Loading Phases
Capitalization of lessons learned on Asset Integrity Management during Front End Loading phases of a green field Project Development, by defining plan for implementation of a diagnostic digital tool for reducing downtime and introduce predictive maintenance during Operation. Eni developed a platform of Digital applications for enhanced Operations management by implementing an Integrated Asset Management (IAM) system. Advanced Analytics tool is part of it and is designed for monitoring, foreseeing and preventing production upsets and anomalies; the tool is set up by verification of areas of interest and criticalities, with identification of main equipment data sets and by the implementation and validation of predictive models. Starting from historical data, data scientists supported by experts develop algorithms capable of finding interdependencies between a set of input variables and an output variable (phenomenon to be predicted/monitored), thus detecting anomalies and criticalities. Main areas of benefit are envisaged on Production continuity, capable of predicting problems on static and rotating equipment and giving information on the most impacting variables on the incipient problems. The tool will support technicians to help them preventing failures and out-of-specs events which may cause loss of production or asset integrity issues, with the activation of predictive maintenance and the aim to strive a continuous monitoring and improvement of plant operational performances. An Energy Efficiency predictive model will also be set up, capable of forecasting the future energy performances of the asset through the prediction of the Stationary Combustion of Carbon Dioxide (CO2) emission index (t CO2/kbbl) and providing the list of the main influencing equipment and variables. The plan for implementation of the tool from the Early phases of development help the organization on prioritizing the implementation of Digital tools as part of the execution and realization of the Asset to be delivered to the Operational personnel, by easing the transition and avoiding subsequent retrofitting carrying brownfield works and additional costs. The implementation of Advanced Analytics tool has been embedded in a new green field initiative of a Development Project since Front End Loading phases, thus fostering digital implementation and minimizing deployment costs by including those as part of the Investment Proposal presented to Joint Venture Partners and Authorities.
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