Quantifying Methane Emissions Through Process Simulations Model and Beyond

N. A. Abdul Talip, S. A. Abidin, M. H. Pikri, L. A. Karim, A. W. Zakaria, N. A. Abu Bakar
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Abstract

Methane concentration in the atmosphere is increasing steadily and this increment is driving climate change and continue to rise. Although the estimates of methane emissions are subject to a high degree of uncertainty, the energy sector is still one of the major sources of anthropogenic methane emissions. Focusing on the oil and gas industry, methane is emitted during normal operation, routine maintenance and system disruptions. However, globally more energy will be required in the future. Transitioning to a low carbon future requires an energy player in O&G to start managing methane emissions in the natural gas / liquefied natural gas value chain effectively. Many global methane management coalitions were established with common goals i.e. to reduce global methane emissions and to advance the abatement, recovery and use of methane as a valuable clean energy. One of it is Methane Guiding Principles (MGP) which focuses on priority areas for action across the natural gas supply chain, from production to the final consumer. Signatory members of MGP is to fulfill the expectations of the 5 principles in MGP that includes pursuing an accurate methane emissions quantification across its gas value chain. A baseline study was initiated to measure methane emissions for LNG plant, gas processing and gas transmission facilities, covering both intended and unintended releases. Methane emissions were quantified using a process simulation software that was developed by PETRONAS Group Technical Solutions, called iCON Emission, where the calculations applied in the software are aligned with API compendium, US EPA and IPCC. Methane emissions from unintended releases i.e. LOPC and fugitive leaks were quantified using the actual inputs from LDAR data (%LEL or concentration), stream compo, stream phase, device type and component correction factor to calculate methane emission rate. Meanwhile methane emissions from intended releases e.g. flaring, compressor seals, pneumatic devices, etc, were quantified using metered amount or designed leakage/vent rate. Further works on Fugitive emissions are currently developed by PETRONAS technologist using Inferential Modeling via machine learning approach. This approach is combining First Principle and Data Analytics to make Fugitive Emission as online information and accurate reporting. To provide further assurance to the results, PETRONAS had engaged a 3rd party to validate the results where it was concluded that methane emissions quantification using iCON tool is almost the same level of accuracy with Level 3 of OGMP 2.0 standard. This level of accuracy is at par with the practice of the other O&G peers. Based on the baseline identification & quantification of methane emissions, PETRONAS is able to take necessary mitigating action, operating its asset in a safe and sustainable manner protecting the environment while monetizing the methane emissions from LNG and gas processing facilities with approximate cost saving of RM 15 mil/year.
通过过程模拟模型及其他方法量化甲烷排放
大气中的甲烷浓度正在稳步增加,这种增加正在推动气候变化,并将继续上升。虽然甲烷排放量的估计有很大的不确定性,但能源部门仍然是人为甲烷排放的主要来源之一。油气行业在正常作业、日常维护和系统中断时都会排放甲烷。然而,未来全球将需要更多的能源。向低碳未来转型需要油气行业的能源参与者开始有效地管理天然气/液化天然气价值链中的甲烷排放。建立了许多全球甲烷管理联盟,其共同目标是减少全球甲烷排放,并促进甲烷作为一种宝贵的清洁能源的减排、回收和利用。其中之一是甲烷指导原则(MGP),该原则侧重于从生产到最终消费者的整个天然气供应链的优先行动领域。MGP的签约成员将履行MGP的5项原则,包括在整个天然气价值链中追求准确的甲烷排放量化。一项基线研究开始测量液化天然气工厂、天然气加工和天然气输送设施的甲烷排放,包括预期和意外排放。使用PETRONAS Group技术解决方案开发的过程模拟软件(称为iCON Emission)对甲烷排放进行了量化,该软件中应用的计算与API纲要、美国环保署和IPCC保持一致。利用LDAR数据的实际输入(%LEL或浓度)、流组分、流相、设备类型和组分校正因子来计算甲烷排放率,对LOPC和逸散性泄漏等意外释放的甲烷排放量进行了量化。同时,通过计量量或设计的泄漏/排气率,对燃烧、压缩机密封、气动装置等预期释放的甲烷排放量进行了量化。目前,马来西亚国家石油公司的技术人员正在利用机器学习方法进行推理建模,进一步研究逸散性排放。这种方法将第一原理和数据分析相结合,使逸散性排放成为在线信息和准确报告。为了进一步保证结果,马来西亚国家石油公司聘请了第三方来验证结果,结论是使用iCON工具进行甲烷排放量化几乎与OGMP 2.0标准的3级精度相同。这种精度水平与其他油气同行的做法相当。基于对甲烷排放的基线识别和量化,马来西亚国家石油公司能够采取必要的缓解措施,以安全和可持续的方式运营其资产,保护环境,同时将液化天然气和天然气处理设施的甲烷排放货币化,每年节省约1500万令吉的成本。
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