Numerical Simulation of Proppant Embedment Depth in Inhomogeneous Formation Based on Field Variables Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Hydraulic fracturing, a deep formation resource extraction method, is widely used to extract oil, gas, and geothermal resources, where fracturing fluids and proppants are usually injected to support the hydraulic fractures that transport the resources. After fracturing fluid loss, there will be the embedding process of proppant in inhomogeneous formations. To understand the mechanism of this phenomenon, the study comparatively investigated the embedding process of proppant in homogeneous formations, layered formations, and continuously varying inhomogeneous formations with finite element methods. Specifically, formation properties, in terms of the inhomogeneous formations, are defined as the nonlinear function of a constant position with the field‐variable (FV) method. The results show that equating nonhomogeneous formation to homogeneous formation underestimates the depth of proppant embedment in actual hydraulic fractures, which varies with proppant size. Next, the underestimation of proppant embedment in homogeneous formation, in turn, results in an overestimation of hydraulic fracture permeability. The FV method can more accurately characterize the proppant embedding process in inhomogeneous formations and reflect the fracture permeability after proppant embedding. Further, the advantages, limitations, and future research directions of this study are discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it