Analysis of Multi-Well and Stage-by-Stage Flowback from Multi-Fractured Horizontal Wells
Bibliographic record
Abstract
Abstract As a result of low gas prices and recent reductions in natural gas liquids prices, unconventional light oil reservoirs remain a primary target for exploration and development in North America. Due to the low permeability of such reservoirs, well-life tends to be dominated by transient linear flow from the matrix to the fractures which may last several years. This flow period is affected by hydraulic fracture properties, therefore operators are looking for new methods to characterize hydraulic fractures, particularly early in the well life. In previous studies by the authors it has been shown that high-frequency flowback flow rates and flowing pressures can be modeled to obtain key hydraulic fracture parameters (i.e. half-length and conductivity). Although commingled data is commonly gathered, technology exists to collect individual stage data. In this work, we advance the analytical procedure presented by Clarkson and Williams-Kovacs (2013b) and Williams-Kovacs and Clarkson (2013c) for analyzing pre- and post-breakthrough of formation fluid during flowback of light tight oil wells by investigating multi-well and stage-by-stage flowback. Studies have shown that there may be significant communication both between stages and between wells on a given pad (or beyond) during the flowback period but not during long-term production. In order to accurately model the flowback period this communication must be accounted for in order to properly assess individual well hydraulic fracture properties. Building upon the analytical models developed previously for single well flowback from unconventional light tight oil wells, we employ the "communicating tanks" concept to account for stage (or inter-well) interaction. Proper allocation and transfer of fluids between stages, if they are communicating, is necessary to ensure that the fracture volume assigned to each stage/well is correct. Our work demonstrates that if individual stage/well flowback data is analyzed without accounting for communication, derived reservoir properties (i.e. fracture halflength) are in significant error. As a result, future well performance predictions, and attempts to optimize fracture stimulations, will also be in error. Our new methods are tested against both simulated and field examples. Stage-by-stage flowback is demonstrated using simulated data, while multi-well flowback will be demonstrated using field data from a multi-well pad.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".