Accounting for Hydraulics and Vibration in MSE Calculations to Estimate Formation Properties
Bibliographic record
Abstract
Abstract Mechanical Specific Energy (MSE), the amount of work done to drill a unit volume of rock, has been evolving as an important parameter to indicate drill-bit efficiency by quantifying bit-rock interactions. Until now, the industry application has been on drilling efficiency, however, the study extends this application by utilizing MSE for estimating subsurface rock properties such as rock strength. Once known, rock strength may be used for improved geosteering – since measurement of MSE is at the bit – or for completion designs. In this study, high resolution (10-second) drilling data is used for the analysis. An updated MSE calculation is proposed which incorporates mud motor dynamics, frictional losses along the drill-string, and drillstring dysfunctions (i.e. vibration). Two new parameters in the form of Hydraulic Specific Energy (HSE), that estimates role of hydraulic impact force at the formation, rather than bit bottom and Vibration Specific Energy (VSE), that estimates the energy associated with vibrations and how it impacts the translational (weight on bit) and rotational energy (Torque) supplied from the top-drive, have been introduced. Upon correcting MSE values for drill-string vibrations (VSE), mud-motor effects and frictional losses along the drill-string, as well as incorporating hydraulic term (HSE), the comparison between MSE and Rock CCS along the well show a high degree of correlation giving an indication of using MSE to estimate rock strength. These corrected MSE values are the actual energy requirements (MSEactual) contributing towards rock-cutting action downhole. Finally, rock CCS values are estimated using the correlations developed between corrected MSEactual and corresponding drilling efficiency calculated as a function of RMS amplitudes of drillstring vibrations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".