Application of Real-Time Resistivity and Annular Pressure Data in Reducing Lost-Circulation Events
Bibliographic record
Abstract
Abstract Lost circulation is one of the major risks associated with drilling in a deepwater or subsalt environment. The downtime spent regaining circulation and the associated well control issues increase the already high operating costs and introduce critical safety concerns. This paper illustrates how formation resistivity and annular pressure measurements, combined with time-lapse logging data, can be used to determine a more accurate fracture pressure, enabling cost-effective real-time drilling decisions. Two examples are presented to demonstrate that an analysis of the resistivity and pressure data, viewed in both time and depth domains, contributes to a better understanding of fracture behavior. Lost-circulation problems occurring in weak formations far below the casing shoe can be located with logging data. Additional information enables relevant-time drilling decisions such as selecting proper mud weight, spotting fluids, and optimizing cementing programs. The first example shows how abnormal real-time resistivity readings, suggesting the initiation of fractures, were confirmed with time-lapse measurements made while tripping out of the hole. The real-time resistivity data showed elevated resistivities suggesting fracture growth. This interpretation was confirmed with time-lapse measurements. The analysis provided the location of the problem zone, the formation type, and the wellbore pressure activating the fractures. In the second example, a minor water kick prompted the acquisition of a real-time openhole leakoff test followed by real-time resistivity logging. The additional information provided a better understanding of the initiated fracture characteristics and enabled drilling the section to total depth without mud losses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".