Bibliographic record
Abstract
Resourceful field development can be achieved only with proper management of uncertainties inherent to any project. This situation is even more noticeable in certain deepwater offshore scenarios in which difficulties of well testing and fluid and core sampling exist. Particularly for cases in which the increasing development complexity is associated directly with uncertainties in fluid and reservoir characterization, a probabilistic analysis, instead of a deterministic one, is the natural way to proceed. In recent years, besides standard reservoir simulation, experimental-design techniques have been introduced to assess uncertainties related to reservoir properties and the project economic aspects. Experimental design is a statistical technique that allows attaining maximum information in a given process at a minimum cost. It allows screening of uncertain reservoir variables and determining which variables, as well as which interactions between them, have the largest effect on the project outcome. On the basis of this information, a more reliable uncertainty distribution can be established. During the past year, an impressive number of papers were presented at the Offshore Technology Conference and various SPE conferences having the main focus on the evaluation and management of uncertainties in field development. Other significant points mentioned in many works were the importance of team-work and the development and implementation of new technologies. Clearly, these aspects are heavily dependent on synergy among geophysicists; geologists; and reservoir, drilling, and production engineers. This month, the featured papers address many challenges found in field development. They also present actual and interesting solutions to challenges by stressing the importance of teamwork and proper management of uncertainty. I hope you enjoy them. Field Development Projects additional reading available at the SPE eLibrary: www.spe.org SPE 100253 "Schedule Optimization To Complement Assisted History Matching and Prediction Under Uncertainty" by H.A. Jutila, SPE, Energy Scitech Ltd., et al. IPTC 10966 "Reservoir-Screening Methodology for Horizontal Underbalanced-Drilling Candidacy" by T. van der Werken, SPE, Weatherford, et al. Available at the OTC Library: www.otcnet.org OTC 17915 "Kizomba A and B: Projects Overview," by B.D. Boles, ExxonMobil Development Co., et al. OTC 18298 "The K2 Project: A Drilling Engineer's Perspective," by J.R. Sanford, SPE, Eni Petroleum, 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.120 |
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".