Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling
Bibliographic record
Abstract
Water coning in petroleum reservoirs leads to lower well productivity and higher operational costs. Adequate knowledge of coning phenomena and breakthrough time is essential to overcome this issue. A series of experiments using fractured porous media models were conducted to investigate the effects of production process and pore structure characteristics on water coning. In addition, a hybrid artificial neural network (ANN) with particle swarm optimization (PSO) algorithm was applied to predict breakthrough time of water coning as a function of production rate and physical model properties. Data from the literature combined with experimental data generated in this study were used to develop and verify the ANN‐PSO model. A good correlation was found between the predicted and real data sets having an absolute maximum error percentage less than 9%. The developed ANN‐PSO model is able to estimate breakthrough time and critical production rate with higher accuracy compared to the conventional or back propagation (BP) ANN (ANN‐BP) and common correlations. The presence of vertical fractures was found to accelerate considerably the water coning phenomena during oil production. Results of this study using combined data suggest the potential application of ANN‐PSO in predicting the water breakthrough time and critical production rate that are critical in designing and evaluating production strategies for naturally fractured reservoirs. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1905–1919, 2014
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".