Drilling a Challenging HPHT Well Utilizing an Advanced ECD Management System with Decision Support and Real Time Simulations
Bibliographic record
Abstract
Abstract A very challenging HPHT well has been drilled utilizing an advanced ECD Management System including real time simulations, early diagnosis of upcoming problems, and real time simulations using state-of-the art models. The System uses all available real time drilling data (surface and downhole) in combination with real time modeling to monitor and optimize the drilling process. This information is used to visualize the wellbore in 3D in real time. It has been implemented in Total E&P Norge TASC (Total Activities Support and Collaboration) Center in Norway. The system is composed of the following elements, some of which are unique and ground-breaking: An advanced and fast Integrated Drilling Simulator which is capable of modeling the different drilling sub-processes dynamically, with interaction between these sub-processes in real time. Automatic quality check and corrections of drilling data; making it suitable for processing by computer models Real time supervision methodology for the drilling process using time based drilling data as well as drilling models / the integrated drilling simulator. Methodology for diagnosis of the drilling state and conditions. This is obtained from comparing model predictions with measured data. Advisory technology for more optimal drilling. A Virtual Wellbore, with advanced 2D and 3D visualization of the downhole process. Data flow and computer infrastructure. Among the challenges during planning and drilling of this well have been: Very small window between pore and fracture pressure. RT ECD simulations were performed with an advanced hydraulic and thermal wellbore model. Increased probability for instabilities in the tight window. Was mitigated by RT stability modeling during drilling. Pore pressure predictions which were updated during drilling. Experiences from the drilling as well as use of the ECD Management System will be summarized and presented. The usefulness of supervision and diagnosis functionalities is illustrated.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".