Empirically evaluating virtual reality’s effect on reservoir engineering tasks
Bibliographic record
Abstract
By leveraging the strengths of virtual reality (VR) technologies, we have created a prototype reservoir model analysis VR tool aimed to advance reservoir analysis workflows beyond conventional methods by improving how one understands, analyzes, and interacts with reservoir model visualizations. We present and discuss the results of a usability study conducted with reservoir engineering experts to evaluate this tool and help determine in what ways virtual reality may offer an improved experience over conventional approaches when performing three common reservoir model analysis tasks: the spatial filtering of model cells using movable planes, the cross-comparison of multiple models, and well path planning. The study found that accomplishing these tasks with our VR tool was generally regarded as easier, quicker, more effective, and more intuitive than traditional model analysis software while maintaining a feeling of low task workload on average. Overall, participants provided positive feedback regarding their experience with using VR to perform reservoir engineering work tasks and found VR improved multi-model cross-analysis and rough object manipulation in 3D, indicating the potential for VR to be better than conventional means for some work tasks. They also expressed they could see it best utilized as an addition to current software to assist reservoir model analysis workflows, however, there were some concerns voiced that would be best addressed before VR was fully adopted into their work.
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 | 0.009 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".