Optimization of Steam Injection Processes in Reservoirs with Subsidence and Uplift
Bibliographic record
Abstract
Abstract Bang-Bang theory provides on-off control of injection and production in hydrocarbon reservoirs and improves recovery by means of simply switching the wells or valves on and off at optimum cycles. In this study, we extend the concept to steamflloding along with including the geomechanics factor in softer formations where subsidence/uplift are observed along with injection or withdrawal of fluids. In this study, to model the above-mentioned phenomenon and apply it to different types of reservoirs with varying geomechanical properties, a full physics commercial simulator has been used. The simulator has been coupled to an optimizer and uncertainty analysis tool to exhibit the significance and applicability of Bang-Bang Control. For a clear depiction of the simulation results for the reservoir model, a broad theoretical background of the approach is provided. For robust reservoir managmentment to be taken, considering economically tight constraints, it is imperative to illustrate the cause-effect relationship or significance of every decision and control variable used for obtaining the results. For this purpose, results from the optimizer and uncertainty analysis tool proves valuable as it helps to accurately identify the injection-production strategies as well as optimum injection rates that increase the recovery rates. To further improve reservoir management strategies using Bang-Bang Control, it is understood that geomechanical behavior and reservoir response to different strategies must be clearly identified. The use of optimal control theory (and Bang-Bang control) for steamflooding are not recent in the industry, but studies that include geomechanical effects during Steamflooding have not been widely undertake. Therefore, the authors aim to highlight the performance and potential of the application of this method for steamflooding in reservoirs that undergo subsidence and uplift at multiple levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".