A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery
Bibliographic record
Abstract
Abstract One of the detrimental issues in upstream oil industry is permeability impairment in oil reservoirs. The permeability may be reduced because of scale deposition originated from injected water incompatibility with the in‐situ water. To tackle this issue and monitor permeability impairment owing to scale deposition, an inventive evolutionary approach of Artificial Neural Network (ANN) is utilized which is based on the bio‐inspired science. This approach is optimized by various optimization algorithms which provide a high‐precision decision‐making process with low uncertainty associated with the interconnected weights of the developed neural network model. The constructed intelligent approaches are evaluated using extensive experimental data from the open literature. Moreover, two regression models are developed to highlight the robustness and precision of the addressed techniques. For model validation, the predictions obtained from the smart technique as well as the regression method are compared with the formation damage experimental data. Based on various performance criteria, it was obtained that the predictions from the optimized genetic algorithm have infinitesimal average relative and absolute deviation from the experimental data (< 0.1 %). The results obtained in the current research prove that implication of HGAPSO‐ANN in monitoring of formation damage associated with scale deposition in porous media results in reliable estimation of permeability impairment. This can result in designing a more reliable reservoir simulation model that captures the permeability impairment due to scale deposition, hence providing thorough action plans for developing EOR technologies that may cause scale deposition‐related formation damage.
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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) | 0.000 | 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 | 0.001 | 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".