A Grand Challenge: Net-Zero Operations—Changing the Definition of “Business as Usual”
Bibliographic record
Abstract
_ This is the second of a series of six articles on SPE’s Grand Challenges in Energy, formulated as the output of a 2023 workshop held by the SPE Research and Development Technical Section in Austin, Texas. Described in a JPT article last year, each of the challenges will be discussed separately in this series: geothermal energy; improving recovery from tight/shale resources; net-zero operations; carbon capture, storage, and utilization; digital transformation; and education and advocacy. The first article, “An SPE Grand Challenge Update on Geothermal Energy,” was published in April 2024. _ At COP28, more than 50 oil and gas companies, many of them national companies, took a historic step toward decarbonization by launching the Oil & Gas Decarbonization Charter. The charter represents significant short- and long-term ambitions to reduce emissions, including net-zero operations by 2050 at the latest. This article explores the importance of this effort, the opportunities available to the industry to reduce its Scope 1 and 2 emissions, and the key technologies needed to achieve the net-zero goal. The Size of the Prize According to the latest data from the International Energy Agency (IEA), the production, transport, and processing of oil and gas resulted in 5.1 GtCO2e in 2022. These emissions stem from the production and delivery of oil and gas and the combustion of fossil fuel necessary for operations on-site (Scope 1), and the import of electricity from external sources consumed in oil and gas facilities (Scope 2). These Scope 1 and 2 emissions represent just under 15% of global energy-related greenhouse gas (GHG) emissions. This is a sizable contribution, nearly equivalent to all energy-related GHG emissions from road transport, highlighting the relative scale of the opportunities associated with implementing operational changes in the industry. The faster and deeper these emissions reductions are, the more significant the impact will be. Sources of Emissions To understand the various paths to net zero an oil and gas company can follow, it is critical to map the sector’s sources of emissions throughout the value chain. It is generally recognized that around 60% of an integrated company’s emissions emanate from upstream operations, predominantly from onshore assets. Refining and distribution are the second main contributors, but of a smaller order of magnitude, notably because of significant efforts already made by companies to reduce emissions from their refining assets and levers of decarbonization potentially easier to implement. However, emissions volumes and intensities differ significantly across regions, countries, and asset types. An asset operating in the Canadian oil sands region will have a very different emissions profile compared to an offshore platform in Western African countries. These differing profiles also call for tailored emission-reduction approaches. This means decarbonization studies must be carried out at the asset level, allowing operators to identify the most cost-effective means of tackling emissions reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".