Bibliographic record
Abstract
As 2022 drew to a close, we saw the emergence of ChatGPT, a development that left me both fascinated and slightly apprehensive. Initial posts on LinkedIn and similar platforms focused on its impressive capabilities, yet it wasn’t long before discussions of its inherent biases began to surface. Artificial intelligence (AI) and machine learning (ML) technologies have rapidly progressed and have significantly affected traditional reservoir engineering, bringing innovative methodologies to reservoir simulations. However, it is essential to understand that these AI and ML technologies are only as effective and trustworthy as the data they are trained on. Limiting the data we feed these systems might inadvertently restrict their predictive power and the scope of their solutions. As we increasingly rely on these data-driven tools for decision-making, we must be cautious of the conclusions they draw and the narratives they generate. These can subtly shape our viewpoints, highlighting the need for a firm understanding of fundamental principles. I am reminded of a term coined by Daniel Yang: the “Nintendo Engineer,” an engineer whose thought process is guided by simulations rather than the other way around. With this in mind, returning to the foundational principles underpinning our field is crucial. This approach could counterbalance our reliance on AI and ML technologies, helping us maintain a well-rounded perspective. The papers I am recommending for your reading embody this mindset. The first paper highlights these technologies’ practical challenges and constraints, offering valuable insights for reservoir engineers. The authors emphasize the need for a balanced approach that combines these advanced techniques with informed decision-making. They also highlight that only some optimization problems can be solved with a plug-and-play approach and that the engineer has to frame the problem in a manageable and meaningful way. The second paper addresses the limitations of the conventional Stone II three-phase permeability model and presents a novel, robust alternative. It takes a fundamentals-based approach to develop an alternative model for the three-phase relative permeability model. Inherent in this recommendation is my bias toward heavy oil because it is the area I currently work in. The third paper showcases the remarkable potential of AI in supplementing traditional reservoir simulation. The paper’s real-world application demonstrates the practicality and adaptability of AI in reservoir engineering and simulation. I hope you enjoy reading this selection of papers and find them enlightening. Recommended additional reading at OnePetro: www.onepetro.org. URTEC 2021-5549 Use of Machine Learning Production Driver Cross Sections for Regional Geologic Insights in the Bakken Three Forks Play by T. Cross, Novi Labs, et al. SPE 214219 Business Intelligence Dashboarding Application in Reservoir Simulation by Ke Wang, ADNOC, 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".