Bibliographic record
Abstract
Technology Focus To be quite honest, I am not sure exactly what an "intelligent field" is. Reading through recent literature, I was impressed with the breadth of the intelligent-field topics—topics such as intelligent-well systems, wireless technology, robotics, barriers to implementation, and organizational effects were covered. I struggled to find a common thread that defines the intelligent field. As the deadline for this issue approached, I turned to Webster's Dictionary for help. Webster defines intelligence in terms of the capabilities to reason, plan, solve problems, think abstractly, use language, and learn. Now I was on to something (I thought). Until recently, we would have said these capabilities are all very human, but as technology advances, machines, and now even oil fields, appear to be developing these capabilities. Does this mean as our oil fields are becoming more intelligent, they are becoming more human? Next, I looked for a common thread in the value proposition cited for the intelligent field. The goals of the intelligent field usually are cited as increasing production, reducing capital and operating expenditures, improving total hydrocarbon recovery, and improving safety and environmental performance, but these are the same goals we have had in the upstream oil and gas industry for many years. I returned to the recent literature and extracted what I believe to be the technical capabilities that are required of an intelligent field—continuous monitoring of surface and subsurface operating conditions, assimilation and analysis of huge amounts of complex data from diverse sources, automated recommendation of corrective and proactive actions, and optimization of the total asset over its life cycle. However, these technical capabilities cannot be exploited without the advancement and evolution of work processes and the capabilities of our people. As you read through the literature, remember that the promises of the intelligent field cannot be delivered through technology alone. As technologies advance, we must commit to the advancement of our processes and to the development of our people. Only through a holistic approach to technology, process, and people can we realize the goals of the intelligent field. Intelligent Fields Technology additional reading available at the SPE eLibrary: www.spe.org SPE 110296 • "Production Optimization by Real-Time Modeling and Alarming: The Sendji Field Case" by Jacques Danquigny, SPE, Total, et al. SPE 110525 • "Optimizing the Production System Using Real-Time Measurements: A Piece of the Digital-Oilfield Puzzle" by Robert B. Thompson, Aethon, et al. SPE 112152 • "A Standard Solution for Upstream Oil and Gas Surveillance" by Mark L. Crawford, SPE, ExxonMobil, 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.155 | 0.095 |
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".