An agent-based model for estimating emissions reduction equivalence among leak detection and repair programs
Bibliographic record
Abstract
Alternative leak detection and repair (alt-LDAR) programs are being introduced by regulators in North America to provide flexibility in how oil and gas producers manage their fugitive methane emissions. However, emissions reduction equivalence must be established between a proposed program and a regulatory standard. We present LDAR-Sim, an open-source, agent-based numerical model for estimating equivalence among LDAR programs and exploring specific LDAR scenarios. Novel advancements include the ability to: (1) set facility-specific LDAR requirements and deployment constraints, (2) simultaneously deploy multiple technologies, each with multiple intelligent agents, (3) integrate screening and close-range methods in a collaborative work practice, (4) consider unique environmental limitations of different technologies, (5) evaluate the impact of limited equipment and labour, and (6) explore the impact of legally vented emissions on screening technologies. We examine several alt-LDAR scenarios using real assets and discuss model confidence and sensitivity to inputs. We show that equivalence determinations depend on explicit definition of reference standards, including weather and labour availability. Screening method performance is vulnerable to the confounding presence of vented emissions and to criteria that trigger follow-up surveys. Relative mitigation of programs is highly sensitive to leak production and null repair rates, two elusive parameters used in previous studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".