Applications of Electric Logs and Geomechanical Models to Optimize Drilling and Well Completions in the Burgos Basin
Bibliographic record
Abstract
Abstract Electric logs and borehole imaging were used to build a geomechanical model to predict wellbore instability during drilling and to optimize casing designs and completions to control sand production in the Burgos Basin. This paper describes how geomechanical models can explain several problems in drilling and completion during the development of some of the fields in Northern Mexico. The total stress tensor model, the pore pressure model, and the mechanical properties model are discussed. Validation of the model is a critical step before it can be applied to design new wells. The geomechanical model was used to design a new well and to optimize drilling, completion, and production techniques for the reservoir. The model was used to optimize the placement and orientation of perforations, evaluate proposed hydraulic fracturing designs, and select the critical draw-down to produce a well. Actual drilling, completion, and production events for the new well were analyzed, and conclusions about how the geomechanical model supported the complete design are also included in this paper.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".