Proposed Methodology and Application of Statistical Analysis Methods on MPD Surface Back Pressure Control System's Performance Evaluation in Extended Reach Drilling (ERD) Wells
Bibliographic record
Abstract
Abstract Over the years, the performance of MPD systems is often called into question when it comes to Surface Back Pressure (SBP) applications to maintain the desired downhole pressure set point, thus eroding the confidence level in applying constant bottomhole pressures at target depths – whether at bit or a fixed depth. In this paper, the performance of various MPD systems is evaluated utilizing statistical analysis, including offline Change Point (CP) inference and Regression analysis on the downhole Pressure While Drilling (PWD) data as a result of the SBP applied during drilling operations. The paper also proposes an objective methodology in the evaluation process, minimizing the influence of various MPD systems’ operating philosophies. The main criteria of the evaluation process are to determine the variances between the modelled and actual MPD setpoint downhole pressure against PWD data at target depth, depending on various operation types. In order to reduce the influence of varying operating philosophies across different MPD systems, the methodology proposed focuses on drilling operations and common data across wells. Analytical methods, including offline Change Point Analysis, are used to identify the sequence of events, operation types and pumps-off events. The trending of ESD and ECD are then determined based on operation categories. Finally, the variances and standard deviation between PWD data and setpoint EMWs are determined using regression analysis as well as normal median and mean values analysis to evaluate the performance of the MPD system and also to determine factors that could affect the performance of the system.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".