Toward developing a streamlined workflow for methane emission monitoring, reporting, and verification in the oil and gas industry
Bibliographic record
Abstract
Establishing a streamlined workflow within the Monitoring, Measuring, Reporting, and Verifying (MMRV) framework is crucial for effective methane emission management and accurate methane emission reconciliation in the oil and gas (O&G) industry. Despite existing MMRV standards such as the Oil & Gas Methane Partnership 2.0 (OGMP 2.0), Veritas 2.0, and MIQ providing valuable guidance, the O&G industry still faces obstacles in compliance with these standards. These obstacles include (1) The Bottom-Up (BU) inventory, constructed with generic activity and emission factors, underestimates emissions and poses gaps in closing uncertainties during the reconciliation process, (2) The decision to deploy one or multiple methane sensing technologies, relying on emission profiles derived from limited sample measurements, can not accurately represent all emissions due to their inherent limitations and the stochasticity and intermittency of emissions, (3) No standard has been employed to assimilate observations from sensing technologies with varying measurement scales and data from operational events, and (4) Addressing various uncertainties, including those arising from direct measurements, atmospheric inversion modeling, and population inference from sample emission events, proves challenging in the final stages of the reconciliation process.In this study, we present a streamlined MMRV-focused workflow integrating established and novel methodologies for reconciling emissions. The workflow consists of five key steps: Firstly, using the Oil and Gas Production Greenhouse Gas Emissions Estimator (OPGEE) to construct more accurate BU inventories and emission profiles for each type of equipment; secondly, determining the technology deployment plan and work practice based on constructed emission profiles using the Leak Detection and Repair Simulator (LDAR-Sim); thirdly, assimilating real measurements from deployed technologies through an ISO/OGC standard-based integrated sensor web architecture; fourthly, leveraging assimilated measurements and operational data to resolve emission events and reconcile the emissions; and finally estimating uncertainties from emission quantifications, inaccuracies in establishing emission event duration, and missed emission events. We demonstrate this workflow using data from the upstream O&G sites provided by an anonymous company. At the end of the demonstration we reconcile and report emissions by following the OGMP 2.0 guidelines.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".