Guidance for Calibration of a Directional Drilling Wellbore Propagation Model Using Field Data
Bibliographic record
Abstract
Abstract Accurately estimating the directional tendency of a drill string is important to ensure tracking of the planned trajectory and to prevent over/undershooting targets. Predicting this tendency is often achieved through calibration of a physical model of wellbore propagation using previous directional drilling data. However, the amount of relevant prior information to use and the uncertainty in future predictions is not always clear. The goal of the work reported here was to determine the amount of prior directional drilling data to use for calibration, and the frequency at which recalibration should occur to ensure accurate predictions of wellbore propagation. A procedure was designed to provide this information for a given wellbore propagation model in a given field. The particular model used in this study is a quaternion-based wellbore propagation model that describes the motion of the bit as a rigid body in space which moves based on fixed angular velocities and drilling inputs given in the form of a traditional slide sheet. However, the procedure can be used for any wellbore propagation model that requires calibration on prior directional drilling data. The procedure consists of two tests. The first test (hold-out test) shows how the prediction error varies over an increasing amount of calibration data while holding the testing-set size constant. The second test (prediction test) shows how the prediction error varies over an increasing testing-set size while holding the amount of calibration data constant. These variations in prediction error can then be examined by looking at the spread of data over these tests. The test methodology was applied on field data obtained from North American wells where bent sub motors were used for deviation control. The first test showed diminishing returns when incorporating more actions into the model calibration. In cases where bottom-hole assembly (BHA) changes were observed, calibrating on a larger number of prior actions slightly increased the test error. The second test allowed us to approximate the error propagation rate of our model predictions in the current field. The procedure described in this paper allows for such information to be quantified and visualized easily to improve the directional driller's understanding of the needs and reliability of the wellbore propagation model being used.
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.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".