A Simulation Model for Accurate Prediction of Uneven Proppant Distribution in the Marcellus Shale Coupled with Reservoir Geomechanics
Bibliographic record
Abstract
Abstract Proppant distribution directly affects the effectiveness of hydraulic fracturing treatment, and significantly impact well production. In most situations, proppants are not uniformly distributed in hydraulic fractures and most proppants cannot be effectively transported into the far ends of fractures from wellbore. As a result of uneven proppant distribution and the ductile and soft geomechanical properties of the Marcellus shale, the reduction in hydraulic fracture conductivity varies greatly from cluster to cluster in a shale reservoir during reservoir pressure depletion. However, the influences of accurate prediction of uneven proppant distribution in hydraulic fractures and geomechanics on well productivity have been ignored in previous simulation studies. In this study, a reservoir simulation model for accurate prediction of uneven proppant distribution is developed. This dual-permeability model is coupled with reservoir geomechanics to illustrate the interaction of stress changing and multiphase flow within hydraulic fractures. History matching from a production well in the Marcellus shale is performed to validate our model. In addition, simulations using different matrix permeability are performed to demonstrate the impact of matrix permeability, along with the effect of proppant distributions and geomechanical properties on well performance. The simulation results show that gas production varies significantly due to different proppant distribution and geomechanical properties. Coupled geomechanical simulation results clearly indicate that the proppant embedding effects are also noticeable due to soft rock mechanical properties and uneven proppant distribution. This paper provides operators with a clear insight of influences of uneven proppant distribution and geomechanics on shale gas performance, optimization of a well treatment design, and an extensive view about the long-term production behavior for the Marcellus shale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".