Monitoring hydraulic fracture volume using borehole to surface electromagnetic and conductive proppant
Bibliographic record
Abstract
Abstract Numerical modeling of a North American hydraulic fracture experiment is done to demonstrate the accuracy with which the volume containing proppant could be estimated when electrically conductive proppant is used. An electromagnetic (EM) acquisition system with surface electric and magnetic field receivers and a grounded electric dipole source is simulated. The source has one electrode on the surface and one down a steel-cased lateral well that is adjacent to the lateral well that is being hydraulically fractured. The simulations are performed using measured EM noise at the site during hydraulic fracturing. A 3D OcTree finite-volume code is used that allows very fine meshing around the wells and fractures that expands rapidly toward the boundaries keeping memory requirements within available resources. The effect of the steel casings is modeled in the forward and inverse solutions. Possible scenarios for source-receiver configurations, proppant conductivity, number of perforations per frac stage, variations in the steel casing properties, as well as geometric errors in the locations of receivers and in the placement of lateral wells are considered. Hydraulic fracture stages are modeled as 3D geobodies with variability in the direction perpendicular to the well. Frac stages are embedded in a layered background model built from logged resistivities. The inversion of the EM data starts with the pre-frac data to recover the anisotropic layered background conductivity, steel casing conductivity, and susceptibility. Data differencing between the frac stage and the background or between successive frac stages is used for inversion of frac stage properties. A parametric box model is fit to each stage to estimate the volume, length, height, and mean stage conductivity. Hundreds of inversions starting from random parameter values are run to calculate parameter means and standard deviations. The mean values of the recovered volume, length, and height are all within 20% of the true values.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".