Application of Rate Transient Analysis Workflow in Unconventional Reservoirs: Horn River Shale Gas Case Study
Bibliographic record
Abstract
Abstract The Horn River Basin is a shale gas play located along the northern border between British Columbia and the North West Territories, in the Western Canadian Sedimentary Basin. This unconventional reservoir utilizes horizontal wells with multistage fracture completions in order to produce fluids from the very low permeability shale. As with many other emerging shale plays, the Horn River play presents significant challenges to operators who are tasked with understanding, developing and producing the play as optimally as possible. Rate Transient Analysis (RTA) utilizes continuous production and flowing pressure data to characterize the reservoir and completion, for the purposes of reserves assessments, supporting field development and completion strategy and for supporting decisions around capital allocation. It has proved to be a very helpful tool for accelerating the "learning curve" of well performance in new plays, for which well - established best practices do not exist. The purpose of this work is to illustrate how RTA can be applied in the Horn River, using a reliable, repeatable and technically sound workflow. To accomplish this, daily production data from eight multi-stage horizontal wells in the Horn River was analyzed using standard RTA techniques, including type curves, flowing material balance, specialized plots and analytical models. In addition to the standard approach, a probabilistic approach to RTA using Monte Carlo simulation is also included in this work, to address the significant non-uniqueness that exists in modeling unconventional reservoirs. The findings of this paper will include long term production forecasts, as well as our best estimation of reservoir and completion characteristics.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".