Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution
Abstract. Reducing oil and gas methane emissions is crucially important for limiting the rate of human-induced climate warming. As the capacity of multi-scale measurements of global oil and gas methane emissions have advanced in recent years, including the emerging ecosystem of satellite and airborne remote sensing platforms, a clear need for an openly accessible and regularly updated global inventory of oil and gas infrastructure has emerged as an important tool for characterizing and tracking methane emission sources. In this study, we develop a spatially explicit database of global oil and gas infrastructure, focusing on the acquisition, curation, and integration of public-domain geospatial datasets reported by official government sources, industry, academic, and other non-government entities. We focus on the major oil and gas facility types that are key sources of measured methane emissions, including production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. The first version of this global geospatial database (Oil and Gas Infrastructure Mapping database, OGIM_v1) contains a total of six million features, including 2.6 million point locations of major oil and gas facility types and over 2.6 million kilometers of pipelines globally. For each facility record, we include key attributes—such as facility type, operational status, oil and gas production and capacity information, operator names, and installation dates—which enable detailed methane source assessment and attribution analytics. Using the OGIM database, we demonstrate facility-level source attribution for multiple airborne remote sensing detected methane point sources from the Permian Basin, which is the largest oil producing basin in the U.S. In addition to source attribution, we present other major applications of this oil and gas infrastructure database in relation to methane emission assessment, including the development of an improved bottom-up methane emission inventory at high resolution (1 km x 1 km). We also discuss the tracking of changes in basin-level oil and gas activity, and the development of policy-relevant analytics and insights for targeted methane mitigation. This work and the OGIM database, which we anticipate updating on a regular cadence, helps fill a crucial oil and gas geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution. OGIM_v1 is publicly available at https://doi.org/10.5281/zenodo.7466758 (Omara et al. 2022).
Mark Omara et al.
Status: final response (author comments only)
- RC1: 'Comment on essd-2022-452', Anonymous Referee #1, 09 Mar 2023
- RC2: 'Comment on essd-2022-452', Anonymous Referee #2, 20 Mar 2023
Mark Omara et al.
Oil and Gas Infrastructure Mapping (OGIM) database https://doi.org/10.5281/zenodo.7466758
High-resolution Permian bottom-up oil and gas methane emission inventory https://doi.org/10.5281/zenodo.7466607
Mark Omara et al.
Viewed (geographical distribution)
This is a great and valuable dataset, and I appreciate the authors hard work in aggregating and making this data publicly available. I downloaded the dataset and it was easy to navigate, visualize, and extract layers. The methods and uncertainties are well described in the paper. I have a few comments listed below:
-The gap assessment and spatial assessment in this study are both very useful for error quantification. However, I think there is an additional error term or test that could be helpful especially in context of your MethaneAir analysis - the gaps that exist in a "dense" country. Or another way, "if MethaneAir observes plume in the Permian, how close is the nearest piece of GIS infrastructure, and is it right to attribute it to that plume?" You show proof of concept with MethaneAir, but EDF has gathered extensive and independent facility-scale observations with attribution through PermianMap where you could do this test rigorously.
-Related to previous comment: in addition to randomly sampling GIS infrastructure and comparing to visible imagery, why not assemble an independent list of GIS infrastructure solely through visible inspection (say 100 of so in various basins/countries) and then see if your GIS database has elements near your visual list? This would be a more blinded test of gaps in the inventory and spatial accuracy.
-The analysis of Permian emissions based on the inventory raises confusion. After application of literature-based emission factors, you get an emission budget/basin loss rate on par with previous atmospheric inversion studies (3.1 Tg). However, the conclusion that this budget is dominated by upstream low-emitting sources is a very different conclusion than what's been found in previous studies, some of which have been published by EDF. For example, from the abstract of Lyon et al. (2021) [https://doi.org/10.5194/acp-21-6605-2021]: "the Permian Basin is in a state of overcapacity in which rapidly growing associated gas production exceeds midstream capacity and leads to high methane emissions." Also, in your description of activity/emission factors, you use EPA GHGI emission factors for gathering pipelines - however from Yu et al. (2021) [https://doi.org/10.1021/acs.estlett.2c00380]: " In this study, we use methane emission measurements collected from four recent aerial campaigns in the Permian Basin, the most prolific O&G basin in the United States, to estimate a methane emission factor for gathering lines. From each campaign, we calculate an emission factor between 2.7 (+1.9/–1.8, 95% confidence interval) and 10.0 (+6.4/–6.2) Mg of CH4 year–1 km–1, 14–52 times higher than the U.S. Environmental Protection Agency’s national estimate for gathering lines and 4–13 times higher than the highest estimate derived from a published ground-based survey of gathering lines." Application these alternative emission factors which have been observed in the Permian would certainly change your conclusion about upstream/midstream. Without this context explicitly stated in your Permian analysis, this section reads as an attempt to arrive at a certain prescribed conclusion, which I don't believe is your intent. The preferred approach would be to make an ensemble of estimates based on the many emission factor distributions that have been recently observed. Or at minimum, the manuscript should state that upstream/diffuse result from your inventory is not totally consistent with other studies from the Permian. However, this seems outside the scope of a data description paper and may be worth total removal/saving for another analysis.
-On the Zenodo DOI webpage it states that datasets for Russian compressors and VIIRS are not included in the dataset due to permissions. I did not see that description also written in the manuscript, where it should also be.