Collaborating on Real-Time Geomechanics across Organizational Boundaries
Bibliographic record
Abstract
Abstract Real-time geomechanics techniques contribute to managing the risks and challenges faced during the realization of a well, providing, in particular, decision-making support to drillers for managing the safe mud-weight window and wellbore instability and for bridging knowledge of the rocks’ mechanical properties to drilling practices. Wellbore instability is often not caused by geomechanical conditions alone and is, in many cases, caused by the combination of geomechanics and the drilling process. This paper describes the application of real-time geomechanics to a remote exploration project located in a challenging field and how operations benefited from this technique. A previously drilled well in the field presented serious wellbore instability issues during tripping. The instability was suspected to have caused problems during the wireline logging phases, which made it impossible to complete the formation evaluation program. The stakeholders were based in four different locations: the rig site, the operator’s office, the operator’s real-time center and the service company’s real-time center. Interaction among stakeholders was coordinated through the operator and service company real-time centers. The use of available infrastructure and remote collaboration software created a collaborative environment that successfully supported the operator’s decision-making process, with the following specific achievements: well total depth selection based on maximum allowable equivalent circulation density before incurring in losses; avoidance of wellbore instability events during drilling, tripping or logging phases; and first operator to achieve successful wireline fluid sampling in the deeper formation of this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".