Real-Time Borehole Condition Monitoring using Novel 3D Cuttings Sensing Technology
Bibliographic record
Abstract
Abstract Wellbore instability and stuck pipe incidents are large contributors to drilling-related non-productive time (NPT). Drilling cuttings/cavings monitoring is crucial for early detection and mitigation of such events. Currently, monitoring is done manually and lacks a streamlined approach. Automating this process would be very beneficial, and is possible due to recent advances in sensing technology. Real-time cuttings/cavings monitoring can be used to quantify cuttings volume, measure size distribution, and analyze shape. By correlating these measurements with ongoing drilling operations, the hole condition (in particular hole cleaning/cuttings transport efficiency, wellbore stability situation, etc.) can be automatically assessed in real-time. This makes pro-active prevention and mitigation of NPT related to hole cleaning and wellbore instability possible. In this paper, we detail a system designed and prototyped to allow us to measure cuttings/cavings in real-time. A highly portable device employs a 2D high-resolution camera and a 3D laser sensor to determine the physical properties of cuttings. The 3D point cloud/depth data obtained by this device provides cuttings size distribution, volume and shape characteristics. Comparisons and discrepancies between expected and sensed quantities can then be used for alarming purposes and taking appropriate corrective action. A prototype experimental setup was constructed to evaluate the ability to quantify relevant cuttings properties and profiles in the presence of drilling fluids. In a controlled environment, the cuttings slide down a shaker table's clearing chute while simulating various realistic external variable scenarios. The environmental impact on the accuracy, repeatability and robustness of the various sensors under investigation was determined to identify the sensors best suited for the task at hand. The optimum device configuration was then implemented and evaluated to verify that the system is viable for use in the field. The automated cuttings monitoring system can warn drillers to potential hazards associated with poor hole cleaning conditions, ongoing wellbore breakout, and the likelihood of stuck pipe events.
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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".