Parametrized inversion framework for proppant volume in a hydraulically fractured reservoir
Bibliographic record
Abstract
Summary Hydraulic fracturing is an important technique to allow mobilization of hydrocarbons in tight reservoirs. Sand or ceramic proppant is pumped into the fractured reservoir to ensure fractures remain open and permeable after the hydraulic treatment. As such, the distribution of proppant is a controlling factor on where the reservoir is permeable and can be effectively drained. Methods to monitor the fracturing process, such as tiltmeters or microseismic, are not sensitive to proppant distributions in the subsurface after the fracturing treatment is complete (Cipolla and Wright, 2000). An electrically conductive proppant could create a significant physical property contrast between the propped region of the reservoir and the host rock. Electromagnetic geophysical methods can be used to image this property (Heagy and Oldenburg, 2013). However, traditional geophysical inversions are poorly constrained, requiring a-priori information to be incorporated through known electrical properties. We examine a strategy to invert directly for the proppant volume using a parametrization of electrical conductivity in terms of proppant distribution within the reservoir.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".