Exploring Immersive Interfaces for Well Placement Optimization in Reservoir Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the oil and gas industry's ultimate goal is to uncover efficient and economic ways to produce oil and gas, well optimization studies are crucially important for reservoir engineers. Although this task has a major impact on reservoir productivity, it has been challenging for reservoir engineers to perform since it involves time-consuming flow simulations to search a large solution space for an optimal well plan. Our work aims to provide engineers a) an analytical method to perform static connectivity analysis as a proxy for flow simulation, b) an application to support well optimization using our method and c) an immersive experience that benefits engineers and supports their needs and preferences when performing the design and assessment of well trajectories. For the latter purpose, we explore our tool with three immersive environments: a CAVE with a tracked gamepad; a HMD with a tracked gamepad; and a HMD with a Leap Motion controller. This paper describes our application and its techniques in each of the different immersive environments. This paper also describes our findings from an exploratory evaluation conducted with six reservoir engineers, which provided insight into our application, and allowed us to discuss the potential benefits of immersion for the oil and gas domain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it