Best Practices for Verifiable Bottom-Up Baseline Emissions Quantification of Producing Oil and Gas Fields
Bibliographic record
Abstract
Abstract This paper studies the gaps that oil and gas operators faced when undertaking a bottom-up facility inventory and baseline quantification of emissions to support corporate priorities for project evaluation and enable participation in government subsidies related to emission reduction programs provided by the Government of Alberta, Canada. The paper aggregates the challenges observed during a survey of over 14,000 facilities in Alberta, Canada from 2020-2022. The paper presents best practices to ensure business readiness for baseline emissions quantification. Globally, countries are making international commitments in Nationally Determined Contributions (NDCs) relating to GHG emission reductions. In the case of Alberta, governments, regulators and independent bodies have repeatedly expressed concern that the estimated and reported emissions represent only a fraction of the actual methane emissions from oil and gas production in Alberta, resulting in an inaccurate baseline for decision making. In 2020, the Government of Alberta announced an incentive program to support and encourage producers to create full-field emitter inventories, quantify baseline emissions and identify emission reduction opportunities. Actioning the program required the creation of standardized emissions data collection criteria for all equipment within the production system as well as production facilities. A standardized emissions data collection criteria was developed through stakeholder engagement with the Alberta regulator, government, operators, carbon credit experts, data capture service providers and 3rd party verifiers. The uptake of the program has been strong, with operators benefiting from baseline emission quantification and emitter inventories to prioritize imvestment decisions. Oil and gas producers are now making investment decisions to meet Canada's commitment to the Paris Agreement, with accurate baseline emission quantification and technology implementation plans. These producers are targeting investment to "big hit" emissions reduction investments with the clarity of an accurate bottom-up baseline emission quantification. While the incentive program successfully incentivized and financially supported producers in this endeavor, the participating companies faced several technical roadblocks, including incomplete emitter inventories, emissions data management, change management and repeatability. These challenges need to be addressed to limit stakeholders setting emission reduction targets prior to field level emissions quantification and prioritization of "big hit" emissions reduction opportunities. Through the program the authors observed and developed a business readiness methodology and best practices to address these challenges. This paper presents the business readiness methodology developed and discusses a set of best practices for undertaking site inventories and baseline emissions quantification that can be confidently actioned in any jurisdiction to create impactful methane emission reduction pathways.
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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 | 0.135 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".