Bibliographic record
Abstract
Abstract Lease fuel (also called fuel gas, fuel consumed in operations or lease-used gas) is defined by the SPE/WPC/AAPG/SEG/SPEE-Petroleum Resource Management System (SPE-PRMS) as "that portion of produced natural gas, crude oil, or condensate consumed as fuel in production and lease plant operations." Reporting this "fuel" as reserves, usually with a clear reference to the reserves attributed to these volumes, has been a practice commonly used by many companies, but not by all. Differences exist within a country and between countries regarding the application and interpretation of the existing standards and regulations concerning the reporting of lease fuel as reserves, resulting in a lack of consistency and comparability between oil and gas companies in the reserves externally disclosed. Annual reports, 10-K and 20-F forms of oil and gas companies indicate that, during the last few years, significant volumes of additional reserves are still being added as a result of including, for the first time, the fuel Consumed in Operations (CiO) in the disclosed reserves. These volumes can be material. Based on the companies reviewed, these volumes can represent up to 12% of the company's total proved reserves, depending on the amount of produced hydrocarbon products consumed as fuel, the type of processes used (e.g., normal production of oil and gas, LNG processing, electricity generation, compression needs, etc.) and the location of the terminal point (SEC terminology) or reference point (SPE-PRMS terminology). A review of definitions, apparent misaligned standards (e.g., SPE-PRMS, Canadian Oil and Gas Evaluation Handbook (COGEH)) and requirements from regulatory bodies (Canadian Securities Administrators (CSA), US Securities and Exchange Commission (SEC), UK London Stock Exchange (LSE-AIM), European Securities and Market Authority (ESMA), Australian (ASX), Hong Kong (HKEx) and Singapore (SGX) Stock Exchanges) are discussed in this paper. Examples are also presented with the rationale of why these "lease fuel" volumes should be part of the reported reserves and should include proper narrative on these volumes. Updates of the lease fuel definition and references to lease fuel in the SPE-PRMS are also suggested in this paper, in order to reflect that, during the last few years, many countries and/or regulators have adopted the SPE-PRMS as the estimation and classification standard for reserves and resources, as well as the increasing amount of large integrated energy intensive projects (some of them presently in the undeveloped reserves category), and the increased relevance of unconventional resources and their role in lease fuel. Inconsistencies in the approach to report (or not) the "lease fuel" as reserves are addressed, highlighting the need for clarity in the standards and disclosures being applied to ensure proper transparency, consistent valuation and reporting within the industry in the area of lease fuel, allowing proper comparability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".