Bibliographic record
Abstract
In reviewing the fluids-related papers to be featured in this issue, I was struck by the wide range of choices that exists for drilling and completion fluids today. We are no longer constrained to the use of a handful of basic water- or oil-based fluids, clear brines, or simple pneumatic fluids to do our job. Likewise, we are no longer limited by a few surface measurements and basic concepts to describe and understand the physical behavior of the fluids that are used. Clearly, this increased selection and our ability to test, predict, monitor, and understand the behavior of all wellbore fluids have contributed greatly to the success of our industry in drilling increasingly complex and challenging wells. Professionals who make decisions about fluids have more challenges than ever before. These challenges include overcoming marketing glitz and information overload to understand when and where true benefits can be derived from use of nonstandard fluids and more-complex-fluid analysis. Normally, I get on my "plastic-viscosity/yield-point" soapbox here, but what comes to mind is that we are challenged to remember and use the basic engineering science behind what we are doing, most of which was described by the previous generation of SPE professionals in the classic papers with paper numbers below 10000. I encourage you to read the summaries and review the list of "additional reading" papers that follow. Then I challenge you to take the next step and read the entire paper for the topic that is of greatest interest to you. And once you have done that, review the references and consider the classic SPE papers that laid the foundation for the work being presented—if you have never read them, do yourself a favor and take the time to do so. Regardless of whether you are evaluating the most-challenging high-cost well to be drilled this year helping to access the next deepwater giant field or a series of assembly-line low-cost wells that help the industry access unconventional resources, knowing your fluids choices and understanding when and where to use them can make the difference between success and failure. Drilling and Completion Fluids additional reading available at the SPE eLibrary: www.spe.org SPE 99080 "How To Unify Low-Shear-Rate Rheology and Gel Properties of Drilling Muds: A Transient Rheological and Structural Model for Complex-Well Applications" by B. Herzhaft, Inst. Français du Pétrole, et al. SPE 96342 "Selection and Evaluation Criteria for High-Performance Drilling Fluids" by K. Morton, Chevron Energy Technology Co., et al. SPE 97018 "Evaluation of Equivalent Circulating Density of Drilling Fluids Under High-Pressure/High-Temperature Conditions" by O.O. Harris, SPE, U. of Oklahoma, et al.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.079 |
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".