Application of Remote Sensing Techniques to Detect Methane Emissions from the Oil and Gas Sector to Assist Operators with Sustainability Efforts
Bibliographic record
Abstract
Abstract The general concept of sustainability lies in the vision that incorporates the economic, social, and environmental dimensions. The energy sector has been addressed as one of the main contributors to emissions of anthropogenic greenhouse gases. Therefore, sustainability in the oil and gas (O&G) industry is mainly associated with the advancement in environmental and social performance across the industry. Individual firms, particularly those belonging to the O&G sector, are now assessed for their environmental, social, and governance (ESG) performance and their impact on climate change. To meet the different key performance indicators (KPIs) for corporate social responsibility (CSR) and ESG, the planning, development, and operation of O&G infrastructure must be conducted in an environmentally responsible. This paper discusses how methane detection of O&G infrastructure using remote sensing technologies enables operators to detect, quantify, and minimize the emissions while gaining insights and understanding of their operations via data analytics products. The remote sensing platforms accounted are satellite and aerial operating in tandem with data analytics to support sustainability initiatives and ESG metrics. This paper presents examples of measurements at O&G sites taken with GHGSat's satellites and aircraft platforms, showing evidence of methane emissions. A discussion of each platform and how they work together is presented. In addition, this paper discusses how these data can be used to achieve sustainability goals and tools for ESG initiatives through analytical models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".