Analysis of Finding and Development Costs in Western Canada: Looking for Most Cost Efficient Unconventional Plays
Bibliographic record
Abstract
Abstract Petroleum producers are currently engaged in significant expenditure towards exploration and development in unconventional oil and gas plays in Western Canada. Finding & Development costs are different in various plays and strategies. But where the producer should focus their resources to achieve most cost efficient production? The paper describes methodology of analysis of Finding & Development cost. A probabilistic model was developed to quantitatively assess exploration and development expenditure, production, and reserves for various producers for different oil and gas plays. The model employs a number of key performance indicators (KPIs) such as Finding & Development costs with and without acquisitions, reserves life, reinvestment, and others. The methodology was applied to a comprehensive study of Finding & Development expenditure in Western Canada focused mostly on unconventional oil and gas. The study included more than 80 oil and gas companies. Each company may be involved in exploration and development of many plays. The expenditure, production, and reserves were analyzed for the last 12 years. The companies were subdivided into three groups based on their production. Each company’s Finding & Development costs and other KPIs were calculated for Western Canada as a whole and for a particular strategy or play where the company was operating, as well as for CBM, tight, and shale gas. The study found significant variance in finding and development cost in Western Canada. All companies and all strategies are ranked based on their Finding & Development costs and other KPIs. The results of the study can be applied to the comparative analysis of efficiency of the exploration and development expenditure, which in turn can help improve portfolio management and decision making processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".