Drilling Fluids: Tackling Drilling, Production, Wellbore Stability, and Formation Evaluation Issues in Unconventional Resource Development
Bibliographic record
Abstract
Summary URTeC 1576637 Unconventional resources encompass a wide range of reservoir types. The art of designing and engineering drilling fluids for unconventional resources should not just enable rotary drilling, but also prevent wellbore instability, protect production, and facilitate formation evaluation. In some cases, geosteering, based on logs or cuttings analysis, is part of the drilling process. In addition, many unconventional wells have trajectories that require excellent lubricity to drill. Drilling fluids must be tailored to achieve the goals for each well. Examples of how drilling fluids have been developed for low-porosity microfractured gas sand, oil shale, and tar sands are presented. In low-permeability gas sands in South Asia, preserving natural fractures in the near wellbore region was critical to obtaining maximum production. In Canadian tar sands, preventing hole washout in the production interval was critical for obtaining maximum production. In the Texas oil shale, replacing oil-based drilling fluid with water based alternatives had implications for formation evaluation, wellbore stability and well length in the production interval. Emphasis is placed on the design process for developing drilling fluid systems that contribute to meeting critical objectives for each project.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".