Long Term Conductivity of Narrow Fractures Filled with a Proppant Monolayer in Shale Gas Reservoirs
Bibliographic record
Abstract
The primary goal of hydraulic fracturing is to create a high conductive pathway. Gas shale is mainly fractured by slick-water. A complex network of narrow secondary fractures is created in slick water fracturing. These narrow fractures without proppants maintain low conductivity. A partial monolayer of proppant can be used to enhance the conductivity of these fractures and then improve the production. Due to the interaction between proppants and fracture surface under confining stress, the proppants will embed into the formations, which results in a decrease in fracture width and conductivity. Researches available in literature have addressed the problem. However, the shale reveals varying amounts of creep deformation in response to applied stress, which will continuously enhance the proppant embedment and reduce fracture width. The influence of this time dependent effect on the long-term conductivity of partial monolayer proppant is not well understood. The study of the characteristics and controlling factors of the long-term change in conductivity can benefit to the production analysis and hydraulic fracturing optimization. Therefore, models combining numerical and analytical methods are developed in this paper. A finite element model is developed to simulate the long-term change in fracture width. Then a simplified model based on Carman-Kozeny equation is used to calculate the long-term conductivity. Simulation results show that after considering long term creep effects, there is still an optimal proppant concentration, which remains the maximum residual conductivity after proppant embedment. The simulation results also indicate that the optimal concentration depends on stress, rock mechanical properties, proppant mechanical properties and time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".