A Step by Step Approach to Improving Data Quality in Drilling Operations: Field Trials in North America
Bibliographic record
Abstract
Abstract Multiple literature studies have indicated that a significant amount of data collected during drilling operations is unreliable. To move towards better data quality, two critical hurdles need to be overcome. First, the case for the value of good data needs to be made, so that resources can be allocated towards improving data quality. Second, a process needs to be established within the operator company to measure and improve the quality of data. This paper is a case study in addressing these challenges. In this work, we focus on eight core surface sensor measurements essential to drilling operations (block position, hook load, rotary speed, rotary torque, pump strokes per minute, flow rate out, standpipe pressure and pit volume) and attempt to assess/improve their quality. The first step involves identifying how much of each measured data deviates from their accepted values. This is most economically accomplished using automated data validation software. Once the root cause is identified, steps can be taken to rectify the problem. Four rigs in North America were identified for this trial conducted over a six-month period. The goal is to establish a data quality improvement loop that continually accesses data, identifies issues, and implements corrective actions. This paper explains this process and how it has been applied to improve the quality of drilling data.
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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 | 0.049 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".