Adopting Physical Models in Real-Time Drilling Application: Wellbore Hydraulics
Bibliographic record
Abstract
Abstract The first step towards drilling optimization and automation is a reliable data acquisition and handling system. This includes receiving and processing different frequency data across multiple platforms and ensuring proper data quality. Once we implement such a platform, we can build different advisory solutions to improve drilling efficiency and move towards drilling automation. The developed Drilling Intelligence Guide (DIG) already enabled us to achieve the aforementioned goal and access different data (from contextual to high frequency) in real-time. In the next phase, different models (physical or data analytics) can be built in to optimize various stages of drilling operations. The drilling industry has made significant progress on different physical models that can be run offline (either for pre-job design or post-job analysis) in the previous decades. Given the developed data platform, we can now adopt and run these models in real-time for optimization and automation purposes. As an example, a real-time, computationally efficient hydraulic model that can account for drill string rotation and eccentricity would enable us to monitor ECD (to be used for wellbore stability, kick and lost circulation mitigation) and calculate pump pressure corresponding to different operational conditions (to be used for drill string washout/pump failure prediction and sensor calibration). The aim of this paper is to show the application of the hydraulic model for real-time monitoring and optimization purposes. An analytical hydraulic model including drill pipe rotation and eccentricity effects is used and compared against transient numerical simulations. In the next step, Pressure While Drilling (PWD) data are used to verify the accuracy of the model for real-time applications. After the verification steps, we explain the process to couple real-time data (e.g., flow rate, RPM), contextual data (e.g., mud properties, BHA and wellbore geometry) and the physical model using field examples. In addition to ECD monitoring at the bit and casing shoe, the real-time hydraulic model can also be used to monitor washout and pump failure events. The application of the model is shown using field examples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".