Maximizing Drilling Performance With Real-Time Surveillance System Based on Parameters Optimization Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With deeper exploration and development of hydrocarbon reservoirs, a novel drilling parameters optimization algorithm, named as Navigation Optimization (NAVO) based on mechanical specific energy (MSE) theory, was investigated to continually improve the rate of penetration (ROP) and drilling performance. From the perspectives of rock mechanics and conservation of energy, the relationship among drilling parameters, ROP and MSE has been derived from comprehensive analysis of optimized drilling mechanism. Based on the R.Teale MSE model, the specific energy concept, considering the effect of hydraulics energy on rock breaking efficiency, is further extended based on the hydro-mechanical specific energy (HMSE). With the principle of maximum ROP and minimum HMSE, drilling parameter recommendation model was established, and a real-time drilling optimization system was developed and named as DrillNAV. The DrillNAV system could monitor all dynamic drilling parameters during drilling operations and feed back the advisory for drillers in real time. A pilot test showed the use of DrillNAV provided about 35% higher ROP with identification of downhole vibrations. It showed that NAVO algorithm can optimize drilling parameters in real time, which can be used to drilling performance evaluation and rock breaking analysis so as to raise the ROP and reduce drilling cost. Key Words : Navigation optimization; Hydro-mechanical Specific energy; Drilling parameter recommendation; DrillNAV system
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it