Development Best Practices in the Duvernay Liquid Rich Shale - Early Stage Sensitivity Analyses and Comparative Economics for Decision Making
Bibliographic record
Abstract
Abstract To maximize the development value of the emerging Canadian Duvernay liquid rich shale (LRS) play quality risk-based decisions need to be made. These decisions must consider a number of complex variables that can all have a high impact on both short and long term economic viability. Credible ranges for variables such as well construction costs, production decline rates, infrastructure configurations, and commodity prices (e.g. oil price volatility witnessed in 2014) must be built. Quality decisions can then be made by leveraging a detailed understanding of the variables’ interdependencies and quantified economic impacts. This paper describes the construction and analysis of a suite of Duvernay scenarios developed by Shell Canada using sophisticated hydrocarbon planning software. They were built using a strategy table approach and focused on options for gas processing facilities. The impacts of drilling ramp-up timing, shallow cut versus deep cut configurations, and operator versus midstream build-own-and-operate (BO&O) were examined. For example, several midstreamers exist in the Duvernay play area with significant, non-optimal (sour and shallow cut) processing capacity. These facilities can be used today to capture initial value but require expansion in the future; therefore, an evaluation of all credible new build options is required to understand how best to develop the play. A suite of key comparative economic metrics, including net present value (NPV), profit to investment ratio (PIR), and payout period (POP), was generated. Expected monetary values (EMV) for each scenario were calculated and used to identify the optimal, risk-based approach. Additional insights were derived by comparing the scenarios, these insights included: the value of information associated with a longer appraisal period, the value of infrastructure flexibility, and the NGL recovery and product price thresholds required to justify deep cut construction.
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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 | 0.000 |
| 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) | 0.000 | 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".