Demonstration of Proof of Concept of Electromagnetic Geophysical Methods for High Resolution Illumination of Induced Fracture Networks
Bibliographic record
Abstract
Abstract In April 2017, the Advanced Energy Consortium (AEC) successfully completed data collection for a proof-of-concept demonstration of remote mapping of hydraulically fractured networks using electromagnetic (EM) proppant additives and a variety of EM tools and configurations. This field-pilot demonstration was conducted at the Devine Test Site, located approximately 50 miles southwest of San Antonio, Texas, and managed by the Bureau of Economic Geology (Bureau) at The University of Texas at Austin. The objective of the ongoing integrated research program is to develop a remote EM-imaging technique for hydraulically fractured networks in order to obtain a higher-resolution image of proppant distribution (lateral/vertical extent and azimuth), which current technologies, such as microseismic, do not allow. The current study is a more in-depth follow-up to a series of shallow field tests that the AEC conducted in 2015 near Clemson University in South Carolina. This paper details the special aspects of the Devine Test Site that make it a unique asset for benchmarking EM-based hydraulic-fracture mapping tools and models. Results from the Devine Test Site demonstrate that a measurable and noticeable EM anomaly was detectable with both time-domain and frequency-domain induced polarization methods. EM- inversion results were consistent with analysis of surface tiltmeter results but diverged significantly from passive seismic responses obtained during the hydraulic-fracturing process. The site will be cored at multiple locations over the next few months, after which accuracy of models and methods will be validated. Future opportunities for collaboration on this highly validated benchmarked site are discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".