Maximising the Value of Unconventional Reservoirs by Choosing the Optimal Appraisal Strategy
Bibliographic record
Abstract
Abstract The choice of appraisal strategy for the decision whether to develop a reservoir largely determines the amount of uncertainty that is carried forward to the development and execution phase of the project. Hence, the selection of an appraisal strategy can indirectly influence later go/no-go decisions. Reservoirs are appraised by drilling and producing wells. In unconventional reservoirs, these wells represent a small subset of possible wells that could be monetised if the decision were made to develop the reservoir, i.e. the appraisal wells sample an underlying population of possible wells. This study explores how an optimal appraisal strategy can be designed in terms of the number of appraisal phases, the number of wells to be drilled in each appraisal stage, and how long to produce the appraisal wells before deciding whether to abandon the project or to proceed to the next stage. It is demonstrated how the view on the average expected ultimate recovery of a well in an unconventional reservoir can be continually revised as new information surfaces. Production data from a large well set from the Montney Formation, which straddles British Columbia and Alberta, Canada, is used to assess how accurately initial production predicts the expected ultimate recovery of a single well.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".