Harmonic Pulse Testing for Well Monitoring: Application to a Fractured Geothermal Reservoir
Bibliographic record
Abstract
Abstract Harmonic Pulse Testing (HPT) has been developed as a type of well testing applicable during ongoing field operations because a pulsed signal is superimposed on background pressure trend. Its purpose is to determine well and formation parameters such as wellbore storage, skin, permeability, and boundaries within the investigated volume. Compared to conventional well testing, HPT requires more time to investigate the same reservoir volume. The advantage is that it does not require the interruption of well and reservoir injection/production before and/or during the test because it allows the extraction of an interpretable periodic signal from measured pressure potentially affected by interference. This makes it an ideal monitoring tool. Interpretation is streamlined through diagnostic plots mimicking conventional well test interpretation methods. To this end, analytical solutions in the frequency domain are available. The methodology was applied to monitor stimulation operations performed at an Enhanced Geothermal System site in Pohang, Korea. The activities were divided into two steps: first, a preliminary sequence of tests, injection/fall‐off, and two HPTs, characterized by low injection rates and dedicated to estimate permeability prior to stimulation operations, and then stimulation sequence characterized by a higher injection rate. During the stimulation operations other HPT were performed to monitor formation properties behavior. The interpretation of HPT data through the derivative approach implemented in the frequency domain provided reliable results in agreement with the injection test. Moreover, it provided an estimation of hydraulic properties without cessation of stimulation operations, thus confirming the effectiveness of HPT application for monitoring purposes.
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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 | 0.000 | 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 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".