Alternative Real-Time and Historical Visualization of Cementing Operations Data
Bibliographic record
Abstract
Abstract Many services on drilling rigs collect data electronically.The data is often either stranded on location or requires a substantial expense to send to head offices with proprietary equipment. Existing infrastructure already being paid for by the operator can be used to eliminate redundancy and efficiently send data from most services to head offices. While not a new concept, the process is being used by surprisingly few services. Cementing is the newest service to utilize this integration and is used to illustrate the concept in this paper. Introduction With the rising costs of products, services, and transportation, team leaders are mandated to increase efficiency and optimize operations. The best tool to show increased efficiency is data. Monitoring data will show performance history, current state, and how future improvements can be made.Drilling data is recorded and transmitted through the rig's satellite system. However, other services such as cementing typically record the data and transmit through their own proprietary real-time equipment or deliver the data to the operator at a later date. The challenge is to record all of the data in one place so that the operator does not have to sort through a stack of documents.Electronic drilling recorders (EDR) are installed on the vast majority of rigs in North America. The EDR records parameters from drilling operations, presenting and disseminating the information via a LAN on location. The parameters are sent off location via satellite and typically stored on a secure internet site. The internet is used to provide access (with appropriate security) to information about wells currently being drilled aswell as historical well information. The internet provides a convenient and inexpensive mechanism to store data and documents in a true electronic well file, to be disseminated and retrieved by the operator, partners, and service companies. In December 2004, the operator inquired as to whether it was ossible to record and transmit cementing data through the EDR. The ability to do so has existed for some time, but to the authors' knowledge, has never been conducted in Canada until this time. Communication between the cementing data and the EDR was conducted in the laboratory without issue. Field trialsfollowed shortly thereafter and proved to be just as successful. This interface connecting the cementing data to the operatoris a valuable asset. Real-time data is now transmitted to thisoperator's office on every cementing job conducted. Operator Challenge The operator's requirement to view cementing data in real time at minimal cost was achieved by requesting that the cementing companies add their specific data parameters to the general set collected by the EDR. This was done using wellsite information transfer standard (WITS), a common interface protocol. This new use of existing technology openscommunication and provides the team with the ability to make real-time decisions during the cementing job.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".