A Meta-Data Framework for Transparency in Rate of Penetration Calculations
Bibliographic record
Abstract
Abstract Data exchanges between different electronic data recorder (EDR) systems and personnel occur on a regular basis in a well drilling operation. A significant portion of this data is derived; i.e., calculated or manipulated after sensor measurements. Currently, derived data calculations are poorly documented; therefore, the usefulness of this data diminishes through data transfer. The objective of this work is to define a meta-data framework for derived data. In this paper, we focus our efforts on one derived data channel, the rate of penetration (ROP) and identify the meta-data required to fully understand the values transferred to the end user. We start by identifying the different types of ROP and document the calculation procedure for each type. Part of the meta-data that needs to be captured involves data transformations that occur when this data stream is moved from one EDR to the next. We interviewed various EDR providers in an attempt to understand their current process. The different types of ROP calculations and their use in different types of drilling performance analysis are described in this paper. The calculation procedures were implemented and tested on an operator's data aggregation system. This effort also documents different EDR systems and how they handle sensed data required for ROP calculations. A meta-data framework is able to capture not just the calculation used, but also data transformations that occur as data hops from one EDR system to the next. Different data transfer protocols such as WITS0, WITSML, and OPC/UA necessitates a broad meta-data framework. While much of the meta-data can be embedded in the data transfer channel itself, a document describing all relevant meta-data is equally effective in communicating the information. Lastly, the meta-data framework developed here can also be applied to other forms of derived data (such as Hole depth, Bit Depth, WOB, etc.). This meta-data framework improves the transparency by providing guidelines to data aggregation providers on the type of information that should be supplied to end users. It also provides insights into how data gets transformed from its point of origin (sensor) to its point of consumption. Finally, it also documents the various type of ROPs and their appropriateness for the analysis that is performed using them.
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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 | 0.000 |
| 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".