Development of Automated Real-Time Drilling Hazards Analysis Platform to Help Manage Drilling Parameters for Safe and Efficient Drilling
Bibliographic record
Abstract
Abstract The objective of this paper is to find optimum ways to simply display entire drilling information and details of a particular well in real-time from well-sites to offices and/or headquarters of any oil and gas company that is operating the drilling equipment or has taken the drilling equipment on rental basis to carry out drilling activities. During drilling activities, it is often the case that one is faced with several problems. These problems are both of mechanical and chemical nature. Some of the main parameters such as ECD, T & D, and borehole cleaning have to be monitored constantly so as to provide better insight as to when a particular issue may occur in the wellbore. Poor ECD management may result in several problems such as kicks and blowouts. Torque and drag occurs usually in low-angle wells where the downward friction and sliding force affects the drill-string. Borehole cleaning refers to carrying out the drilled cuttings back to the surface. If this is not taken care of, it could block the borehole which would subsequently affect the drill-string up-to the bit. An algorithm is defined by inputting various parameters which affect the aforementioned drilling processes. These are then fed into MATLAB. The appropriate flowchart that the algorithm will follow is defined through the "State-flow Chart" option in MATLAB. The same work is then carried out through Excel where conditional formatting is used to establish limits and format the data accordingly. This is subsequently effectively carried out through Pivot Tables and Slicers which aid in making Dashboards in Excel. The warning signals are input in the dashboard through Visual Basic for Applications (VBA) code and assigning the code to buttons. After this, the same work is shifted to Python through a Jupyter Notebook. Here, our Excel database is connected to the notebook through SQL Server Management Studio. After the alerting stage, training of ECD data through machine learning models is carried out along with necessary charts and graphs related to ECD for dashboard construction. The purpose of this paper is to accurately predict primary and secondary drilling parameters as well as to undertake real-time intervention in order to save costs, resources and man-power.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".