Improving the Annual Reserves/Audit Process Through the Use of Citrix and Web-Based Applications
Bibliographic record
Abstract
Abstract As companies are increasingly challenged by oil and gas reserve disclosure and corporate governance requirements, it takes more internal and external resources to be compliant. This paper outlines a secure Citrix and web based process which has been used for over 5 years that significantly improves access to data, communication, work product quality, transparency, efficiency, and thus, the cost of an annual reserves or audit requirement. The most significant process change is that the reserve database is hosted by the E&P company with the independent consultant acting as the administrator through secure Citrix access. The process utilizes industry available software processes that allow the independent consultant and E&P company staff, in different parts of the world, to see the work product in 24/7, resulting in workflow and cost efficiencies related to data and/or interpretation issues. In addition to the final report, the reserve database and technical/financial models are also available to the E&P Company for internal business decisions and updating during the year which improves the external process for the following year-end. There are a number of opportunities to improve the Citrix and web based process which will also be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".