Utilizing Remote Sensing and Data Analytics Techniques to Detect Methane Emissions from the Oil and Gas Industry and Assist with Sustainability Metrics

Abstract

Summary The purpose of this paper is to provide additional information and insights gained on manuscript SPE-209980-MS, accepted for presentation at the 2022 Society of Petroleum Engineers Annual Technical Conference and Exhibition (Esparza et al. 2022). The energy sector has been identified as one of the main contributors to emissions of anthropogenic greenhouse gases. Therefore, sustainability in the sector is mainly associated with the advancement in environmental and social performance across multiple industries. Individual firms, particularly those belonging to the oil and gas (O&G) industry, are now assessed for their environmental, social, and governance (ESG) performance and their impact on climate change. To meet the different key performance indicators (KPIs) for corporate social responsibility (CSR) and ESG, the planning, development, and operation of O&G infrastructure must be conducted in an environmentally responsible way. Today, operators calculate their own emissions, which are typically self-reported annually, usually relying on emission factors to complement the lack of emission measurement data. This paper discusses how methane detection of O&G infrastructure using remote sensing technologies enables operators to detect, quantify, and minimize methane emissions while gaining insights and understanding of their operations via data analytics products. The remote sensing technologies accounted for in this paper are satellite and aerial platforms operating in tandem with data analytics, providing a scheme to support sustainability initiatives through the quantification of some ESG metrics associated with methane emissions. This paper presents examples of measurements at O&G sites taken with satellites and aircraft platforms, providing evidence of methane emissions at the facility level. A discussion of each platform and how they work together is also presented. Additionally, this paper discusses how these data insights can be used to achieve sustainability goals, functioning as a tool for ESG initiatives through the incorporation of analytical models.