What is best practice greenhouse and energy reporting in the oil and gas industry?*
Bibliographic record
Abstract
The upstream oil and gas industry is diverse and many of the assets are geographically dispersed in offshore and onshore locations. The first year of National Greenhouse and Energy Reporting (NGER) in the 2009 financial year (FY09) challenged the industry to come to terms with complex issues such as the reporting structure, defining facilities, determining appropriate reporting methodologies, determining incidental emissions, obtaining contractor emissions and considering uncertainty estimates. This paper will explore the range of industry responses during FY09 and will be accompanied by a case study from Santos Limited to illustrate the journey. In responding to NGER requirements in FY09, the oil and gas industry was required to absorb many new legislative compliance obligations. At a company level, difficult decisions had to be made about the allocation of resourcing for NGER preparation and response. Companies were also faced with financial implications of the reported data, because that data would underpin permit liability under the proposed Carbon Pollution Reduction Scheme (CPRS). Going forward, a key optimisation challenge for FY10 and beyond is the management and use of the NGER data. This paper will cover the processes and systems used to collect and report data and how the use of that data for organisational decision making will all be an important optimisation consideration in a CPRS environment. The paper will also explore other NGER reporting issues for the oil and gas industry, such as: arrangements with stakeholders, such as joint venture parties, partners and contractors;selection of measurement methods, including complexities with venting, flaring and other fugitive emissions;availability of appropriate measurement equipment;issues with reporting of own-use emissions and intermediate energy use and production; and,measurement of exploration activities. These issues are likely to present an optimisation challenge to many in the industry during FY10 and beyond. The paper will then conclude with a case study by Santos Limited.
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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 | 0.233 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".