Unplanned Casing Shoe Setting Led to a New Well Schematic Optimized Design for Futures Wells at Quesqui Field
Bibliographic record
Abstract
Abstract This paper documents the successful optimization of well schematic after an unplanned casing setting. That failure required the use of an Ultra Low Invasion Reinforcement Technologies while drilling Q-49 well in Mexico, which changed the well schematic design for all future wells. The use of these technologies allowed for the effective expansion of the operating window, ultimately leading to a successful completion of the drilling stage without the need for additional casing. The study highlights best practices and lessons learned during the drilling process. The implementation of Wellbore Shielding technology (also known as Ultra Low Invasion) in the recent field development has proven to be an extremely resistant and flexible solution for real-time hole reinforcement while drilling. This technology has enabled continuous operations without compromising well integrity. Constant monitoring of concentration levels through SBT (Sand Bed Test) enables early detection of unfavorable conditions and prompt action, as demonstrated in the case study to be presented. The use of software while drilling also led to early detection and monitoring of the fluid conditions. This application allowed the operator to increase the operative window over 900 psi, without destabilizing the wellbore. The drilling fluid density used exceeded well above the maximum permissible level of the density gradient, however no destabilization was observed. This solution also resulted in the elimination of an additional casing to isolate the two formation pressures. Since two different formation pressures had to be drilled with the same drilling fluid, the use of this technology represented a dramatic reduction of fluid loss rate, from 95 bbl/h to less than 1.9 bbl/hr. This case study demonstrates how using an appropriate formation-strengthening technology, which is proven to significantly increase the operating window and significantly reduce lost circulation under dynamic conditions without suspending operations for loss control, optimizes operating costs and improves operating conditions, leading to the well's success.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".