Integration of Montney Microseismic Information Into a Reservoir Simulator to Analyze a Horizontal Wellbore With Multiple Fracture Stages
Bibliographic record
Abstract
Abstract In order to get a clear picture of the effectiveness of a multi-stage stimulation treatment pumped into the horizontal wellbore of a tight gas reservoir, one must integrate data from a number of different sources. This will provide a more complete forecast of the reservoir's development. The Montney formation straddles the British Columbia / Alberta border in the Western Canadian Sedimentary Basin. There is significant variability in the formation's properties across its area, but even so, we have seen a multitude of horizontal wellbores lined up within the formation in recent years. Many involved have questions about fracture spacing along a horizontal wellbore, and ultimately, the spacing of the horizontal wells in a field. The answers to these questions can lead to improved recovery factors and better economics for the resource play. In this case study, seven stages of Basal Doig / Upper Montney microseismic data are integrated with fracture pumping information, and finally incorporated into a reservoir simulator. Two years of production history from the Montney horizontal is matched to "calibrate" the four layer reservoir model and make it a predictive tool. This provides the basis for understanding of drainage radiuses around the fractures, and for recommendations on fracture spacing in order to optimize completions in subsequent wells. The calibrated reservoir model is then used as a predictive tool to understand drainage radius and productivity differences when the number of fracture stages in the wellbore is increased to reduce fracture spacing. Predictably, the cumulative production from the tight gas well with more stages is greater than the same well with fewer stages. Ultimately, there is an economic trade-off between completing the well with more stages and increased well productivity, and an optimal combination that differs from one region to the next.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".