New Type Curves for Analyzing Horizontal Well With Multiple Fractures in Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract To produce economically from most shale gas plays, horizontal wells (cased or open hole) with multiple fractures are the most popular choice for completing the wells. A number of well-performance behaviors can be seen for these wells depending on reservoir behavior and induced hydraulic-fracture geometry. In this paper, conceptual models for well/reservoir/hydraulic fracture combinations are first presented. Next, we discuss the impact of various reservoir types/induced hydraulic-fracture geometries upon the sequence of flow-regimes that could be encountered for shale gas reservoirs. In addition, we develop and present new sets of dimensionless type-curves for one of the conceptual models. The newly developed type curves in this study yield more unique results than those presented previously. With these dimensionless type curves, the early linear flow (early-time half slope) and boundary-dominated flow (late-time unit slope) fall on top of each other and the transition between these two regimes depends on the geometry of the reservoir and completion. Using the type curves as a guide, we then present the flow regimes that are expected for different values of horizontal well length, number of fractures, length of the fractures and spacing between horizontal wells. We also present a new method for evaluating the contribution from the outer reservoir (beyond the fracture length). Finally, using the characteristics of new dimensionless parameters and the new type-curve set, we present a simple and practical procedure for long-term forecasting in multi-fractured horizontal wells.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.002 | 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".