Bibliographic record
Abstract
Technology Focus In the late 1990s, a friend in the office showed me a cobbled-together neural network in Excel that was driving (then Hyprotech, now Aspen) Hysys such that the calculation speeds were significantly faster than those in the more-rigorous but wandering solution that Hysys alone could yield. While interesting and a good solution for that problem, it seemed to me at the time something that a hobbyist would do in the evenings. During the past few years, it has become painfully obvious that such a dismissive conclusion is outdated. Artificial-intelligence (AI) -based methods have become mainstream engineering, and we as practitioners need to have a firm understanding of the principles and be ready to apply them when the opportunities arise. AI-related papers now make up a significant fraction of the overall literature in intelligent fields. For example, during the past year, Ikezue and Onukwuli have shown how neural networks can be used to control gas production in multilaterals. Baarimah et al. used a variety of techniques, including neural networks and fuzzy logic, to predict reservoir-fluid properties and demonstrated why this could be a better approach to a single correlation choice. Alzate et al. used neural networks to generate and test artificial well logs to assist in geomechanical evaluations. Note that these papers and others presented in the last year come not only from academic institutions but increasingly from operators and service providers reporting how these techniques are being used to make business decisions. Why have these technologies gained traction? The answer, of course, is partly that the technologies have matured, though the basics have clearly been in place for some time. The more important trends are a vast increase in the availability of data and the increasing need to make business decisions before the physics of the problem is understood well enough to form a reasonable physical model. AI has emerged as a go-to solution type to help us build physical insight into problems where the complexity or heterogeneity of the problem makes a comprehensive physical model still too difficult to pose. Among the articles featured in the section, you will find some enlightening examples that illustrate the utility of these approaches. JPT Recommended additional reading at OnePetro: www.onepetro.org. OTC 25391 A Review of Intelligent- Completions Installations: Lessons Learned From Electric/Hydraulic, Hydraulic, and All-Electric Systems by Maciel Potiani, Baker Hughes, et al. SPE 169388 Generating Synthetic Well Logs by Artificial Neural Networks Using MISO-ARMAX Model in Cupiagua Field by G.A. Alzate, Universidad Nacional de Colombia, et al. SPE 170113 An Integrated Application of Cluster Analysis and Artificial Neural Networks for SAGD-Recovery- Performance Prediction in Heterogeneous Reservoirs by Amirian Ehsan, University of Alberta, 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.337 | 0.197 |
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".