The Factor of Safety-Constrained Model Predictive Controller Design for Closed-Loop Reservoir Management
Bibliographic record
Abstract
A closed-loop optimal control strategy is extensively used in all engineering disciplines to achieve desired control while maximizing performance. Balancing the economics and safety in petroleum reservoirs calls for a closed-loop control scheme in its operation. With the objective of maximizing the oil production rate (OP) and tracking the factor of safety (FoS) within the safety limit, a stochastic optimization-based model predictive controller (MPC) formulation is proposed in this paper. In this work, we build a deterministic proxy model for the OP and polynomial chaos expansion (PCE)-based model for the FoS with the well bottom hole pressure (well BHP) as the inputs using the CMG-STARS and FLAC3D simulator data. These models are used to formulate the MPC to determine the optimal dynamic well bottom hole pressure (MOP). Results are compared with a fixed MOP and to a static measure directly based on the PCE model of the FoS.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".