Simulator and the First Field Test Results of an Automated Early Kick Detection System That Uses Standpipe Pressure and Annular Discharge Pressure
Bibliographic record
Abstract
Abstract A new automated method of detecting an influx or losses using standpipe pressure (SPP) and annular discharge pressure (ADP) has been tested while drilling a tight gas well in Canada with approximately 200 hours of operational time being logged. While tight gas reservoirs may seemingly pose a low well control risk, they are typically drilled underbalanced to increase the rate of penetration and to eliminate a casing string which is required when using a higher mud density in order to protect upper formations due to low kick margin tolerance. The operator’s experience was that previous kicks went undetected until a significant volume had been produced due to the closed loop system having fluid retention without accurate flow measurement, resulting in slower pit volume measurement, extended well control time and requiring the casing to be set deep enough to protect the weaker formations from high shut-in pressures. Delta flow using flow meters has been in use for over 15 years for early kick detection (Haeusler et al 1995), however for tight gas drilling this method has not proven suitable due to cost, complexity and measurement disruption due to high gas fractions in the drilling fluid. A comparison of using a high resolution flow meter and pressure sensors prior to the field test will be discussed in addition to the field trial results. Benefits of using this new system, what was learned during the trial and improvements to the system will also be discussed along with how the system can be applied to Managed Pressure Drilling applications where the system is augmented using choke position and with conventional drilling applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".