Evaluation of Nonlinear Fracture Relative Permeabilities and Their Impact on Waterfrac Performance in Tight Gas Sands
Bibliographic record
Abstract
Abstract This paper presents the results of a simulation study to evaluate the effects of gas-water fracture relative permeabilities on post-fracture performance for a well stimulated with a water-frac. More specifically, we investigate the effects of the fracture relative permeability curve shape on both short-term and long-term post-fracture production performance. We utilize a reservoir flow simulator coupled with a geomechanical model which allows us to account for fracture growth during the stimulation treatment and to model realistic fluid distributions in both the reservoir and fracture. We also incorporate new fracture relative (gas-water) permeability data measured in the laboratory for a range of proppant types. Unlike many previous studies that simply assume a linear shape, the new measured fracture relative permeability data are quite non-linear. We compare short-term fracture cleanup and long-term water frac production performance using both the measured non-linear as well as hypothetical linear gas-water fracture relative permeability curves. Generally, the non-linear relative permeability data—combined with low initial absolute fracture conductivities—create very low effective fracture conductivities to gas and cause ineffective fracture cleanup. Although fractures with high absolute conductivities clean up more effectively, we still observe significant residual water saturations—even after several hundred days of production. We also observe differences in the longer-term production performance caused by residual fracture water saturations that are much higher than originally thought. Finally, we assess the effects of relative permeability curve shape on post-fracture diagnostics using pressure transient testing. Evaluation of simulated pressure buildup tests suggests the computed fracture half-lengths are essentially equal to the model inputs, but the computed effective fracture conductivities are much lower.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".