Net reserves evaluation and sensitivity analysis of shale gas project under royalty & tax system in British Columbia, Canada
Bibliographic record
Abstract
With the declination of production and increasing of fossil-fuel demand, petroleum companies strive to maximize production by conducting more advanced drilling operations, such as extended reach, horizontal and high-pressure/high-temperature (HP-HT) drilling and multi-stage hydraulic fracturing, which are expanding globally into unconventional resources drilling. Shale gas, which constitutes for a significant percentage of the natural gas resource base and offers tremendous potential for future reserve and production growth, was becoming the increasingly important asset for petroleum companies. This paper took a shale gas project in British Columbia (Canada) as an example, combining the characteristics of the shale gas reservoirs with royalty & tax policy of this region. The research on shale gas reserve evaluation and the principles of net reserve calculation under royalty & tax contract was carried out. The four main aspects such as technique, economy, commerce and engineering, were studied to analyze the influence on net reserves from the following factors: production, declining rate, development plan, oil price, Opex(operating costs), Capex(investment) and taxes, etc. Sensibility analysis was conducted by adopting the most weighted factors, such as oil prices, production, Opex and Capex. All the effort was to put forward the corresponding suggestions on optimizing development strategy, solve the current reserves evaluation problems of shale gas, and provide reference for the shale gas assets transaction, development and perfection of reserves value evaluation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".