Continuous Wellbore Surveying While Drilling Utilizing MEMS Gyroscopes Based on Kalman Filtering
Bibliographic record
Abstract
Abstract The current method to compute the wellbore while drilling is based on stationary surveys at the desired station. This is done by measuring the inclination and the azimuth of the borehole between the current and the previously surveyed stations. Using a mathematical model based on assumptions of the shape of the drilled section, the coordinates of the borehole can be derived. This current method neglects the actual trajectory between the two surveying stations. Exploration and production companies demand cost effective drilling operations. Thus, demand has been rising for a continuous survey that captures the actual trajectory between the stationary surveying stations. This provides an actual estimate of the curvature "dogleg" along the well trajectory. In addition, this allows a better estimation of the casing and cementing of the borehole. Therefore, in this development the wellbore trajectory between the two surveying stations is continuously surveyed using three accelerometers and three MEMS gyroscopes. The computation algorithm is based on strap down Inertial Navigation System mechanization and Kalman filtering. The inputs to the continuous drilling survey system are the accelerometers and gyroscopes measurements, while the outputs are position, tool face, inclination and azimuth of the drill bit. This wellbore survey system will exhibit an unlimited growth of position, and azimuth errors if there are no external observations to update the surveying system. Two external update schemes can limit this error growth while drilling. The first is based on the continuous source of drilled pipe length measurements while the second is the zero velocity update. The Kalman filter continuous surveying system was successfully applied to drilling tests. External updates of the drill pipe length were utilized to reduce measurement error drift. When the drilling process was stopped to connect new drill pipe stands, zero velocity updates were employed by the Kalman filter.
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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) | 0.000 | 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) | 0.001 | 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".