The pyxis project: A geospatial data system for emission estimation monitoring in the oil and gas industry

Abstract

Consistent estimation and monitoring of greenhouse gas (GHG) emissions in the Oil and Gas (O&G) industry is challenging due to inaccessible, fragmented, and unstandardized datasets. Earlier efforts in estimating such emissions required extensive manual analysis to harmonize diverse data sources on O&G operations. Also, these analyses depend on flaring and methane leakage datasets, which should ideally be updated in near real-time, challenging to integrate effectively to process models. To tackle these challenges, this study proposes a Geographic Information System (GIS)-based data platform called Pyxis for integrating and managing data input associated with GHG emissions estimates in the O&G sector. The Pyxis architecture includes a scalable geodatabase for source management and an automated data pipeline for data management using spatial indexing. This greatly reduces the manual labor traditionally needed for data matching and merging. In addition, top-down remote sensing data can be seamlessly associated with bottom-up field operations data through Pyxis, which improves data recency and spatiotemporal coverage. Here, we apply Pyxis to the O&G fields of Brazil as a case study to show how it can help generating accurate estimates of Carbon Intensity (CI) with data management among disparate and inconsistent data sources. This work highlights the potential of scaling up Pyxis globally via integrating artificial intelligence models for data extraction and ultimately becoming a valuable tool for GHG emissions monitoring and policymaking in the O&G industry.