Shifting the Perspective: Modernizing Well Density Metrics for Multi-Layer Horizontal Well Development
Bibliographic record
Abstract
Abstract Unconventional reservoirs with low permeability shales and siltstones are currently being developed using horizontal wells in multiple layers. As this development technique has become more common, accurately understanding well-to-well communication is increasingly critical. Well positioning, reservoir thickness and well interference effects are important factors in the success of multi-layer development. Traditional well density metrics such as wells per section and lateral well spacing do not account for the multi-layer nature of these plays. This paper introduces readily derived metrics that enable a three-dimensional (3D) quantification of multi-layer well density. Unlike traditional analysis which considers pad development from a bird’s eye view, this paper considers the vertical cross-section of a pad which enables the 3D drainage to be quantified. The metrics Cross-Sectional Drainage Area (XDA) and Three-Dimensional Proppant Intensity (3DPI) are defined. XDA quantifies the well density relative to the thickness of the reservoir. 3DPI represents completion intensity and reservoir stimulation relative to the cubic volume of gross rock attributed to the multi-layer development. Once introduced, these two metrics are correlated to well and pad level performance. Examples from the Montney Formation in Western Canada and the Bakken Formation in North Dakota, USA are studied in detail. Ultimate hydrocarbon recovery factors, early time well performance and production profiles are analyzed and compared to the XDA and 3DPI metrics using visual analytics and multivariate machine learning models. In both the Montney and Bakken examples, XDA correlates with well performance and 3DPI correlates with pad hydrocarbon recovery factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".