Effect of Transient Temperature on MWD Resistivity Logs
Bibliographic record
Abstract
Measurement While Drilling (MWD) is a relatively new technique in formation evaluation. The measurements of the physical properties of the newly drilled formation are made simultaneously during the drilling operation. Heat conduction from the formation to the wellbore containing the circulating drilling mud, and convective heat flow due to infiltration of the liquids from the wellbore into the formation, result in a transient temperature profile in the formation. This transient phenomenon affects the interpretation of the measured resistivity logs. Therefore, it might be necessary to account for the transient temperature profile and apply local correction factors to the measuring devices. In this study, a mathematical model is developed to study the temperature profile around a wellbore. Special features of the model include variable infiltration rate as mud cake builds up on the wellbore wall and its effect on the temperature profile. Furthermore, the solution is obtained for a two-zone medium. The zone close to the wellbore, i.e. the invaded zone contains mud filtrate, oil and connate water. The virgin zone contains oil and connate water. Sensitivity studies were performed with respect to the reservoir and infiltration rate and the solutions were analyzed. It is shown how the existence of the transient temperature profile and mud filtrate invasion significantly affects the apparent MWD resistivity readings.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".