Production Optimization in Waterfloods with a New Approach of Inter-Well Connectivity Modeling
Bibliographic record
Abstract
Abstract We present a novel methodology to model inter-well connectivity in mature waterfloods and achieve an improved reservoir energy distribution and sweep pattern to maximize production performance by adjusting injection/production strategy on well control level. The method involves a reduced-physics based fast numerical tracer test on each well that yields inter-well connection strength or well allocation factors, and then a data-driven efficiency model on each inter-well connection calibrated automatically from the production/injection history of the reservoir. The latter one identifies the undesired connection suffered from water cycling or aquifer coning. A producer-injector/aquifer communication network is established which enables instantaneous forecast on reservoir response to various hypothetical well control strategies. An optimization algorithm is applied to improve the flux pattern by strengthening efficient connection and weakening inefficient connection. The objective of the optimization can be to maximize oil production or to minimize water cut. A set of physics constraints (lift limits, injection limits, liquid capacity, etc.) and economic constraints (e.g. oil target) can be enforced in the optimization process. The methodology is tested based on a full-scaled history-matched simulation model of a real carbonate field. The field is a large waterflood with 200+ wells and 5 years history, and the current water cut is above 30% and rapidly increasing. The full field simulation model was regarded as the true reservoir in this study. The network model was trained up to a time point, and then used to optimize the waterflooding strategy for six months. Reservoir response to the optimized strategy was simulated on the full field model, as well as the historical strategy and do-nothing strategy. The results demonstrated that the optimized strategy maintained oil production and reduced water production by 50% without adding new well, while the historical operation satisfied the oil target by drilling tens of new wells and scarifying water-cut. This new approach models the key physics in waterflooding with a network model, and uses the model to optimize well control strategy effectively. The model reduces the non-critical physics and incorporates data-driven techniques, therefore it is fast to build, calibrate and run. Compared to traditional simulation modeling, this approach can be used by production engineers to guide operations in a daily to weekly manner.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".