Application of direct-fitting, mass-integral, and multi-rate methods to analysis of flowing \nfluid electric conductivity logs from Horonobe, Japan
Bibliographic record
Abstract
The flowing fluid electric conductivity (FFEC) logging method is an efficient way to provide information on the depths, salinities, and transmissivities of individual conductive features intercepted by a borehole, without the use of specialized probes. Using it in a multiple-flow-rate mode allows, in addition, an estimate of the inherent "far-field" pressure heads in each of the conductive features. The multi-rate method was successfully applied to a 500-m borehole in a granitic formation and reported recently. The present paper presents the application of the method to two zones within a 1000-m borehole in sedimentary rock, which produced, for each zone, three sets of logs at different pumping rates, each set measured over a period of about one day. The data sets involve a number of complications, such as variable well diameter, free water table decline in the well, and effects of drilling mud. To analyze data from this borehole, we apply various techniques that have been developed for analyzing FFEC logs: direct-fitting, mass-integral, and the multi-rate method mentioned above. In spite of complications associated with the tests, analysis of the data is able to identify 44 hydraulically conducting fractures distributed over the depth interval 150-775 meters below ground surface. The salinities (in FEC), and transmissivities and pressure heads (in dimensionless form) of these 44 features are obtained and found to vary significantly among one another. These results are compared with data from eight packer tests with packer intervals of 10-80 m, which were conducted in this borehole over the same depth interval. They are found to be consistent with these independent packer-test data, thus demonstrating the robustness of the FFEC logging method under non-ideal conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".