Machine Learning-Based Prediction of Scale Inhibitor Efficiency in Oilfield Operations
Why this work is in the frame
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Bibliographic record
Abstract
Water injection is widely recognized as one of the most important operational approaches for enhanced oil recovery in oilfields. However, this process faces significant challenges due to the formation of sulfate and carbonate mineral scales caused by high salinity in both injected water and formation water. To address this issue, the use of mineral scale inhibitors has emerged as a valuable solution. In this study, we evaluated the performance of seven machine learning algorithms (Gradient Boosting Machine; k-Nearest Neighbors; Decision Tree; Random Forest; Linear Regression; Neural Network; and Gaussian Process Regression) to predict inhibitor efficiency. The models were trained on a comprehensive dataset of 661 samples (432 for training; 229 for testing) with 66 features including temperature; concentrations of various ions (sodium; calcium, magnesium; barium; strontium; chloride; sulfate; bicarbonate; carbonate, etc.), and inhibitor dosage levels (DTPMP, PPCA, PBTC, EDTMP, BTCA, etc.). The results showed that GPR achieved the highest prediction accuracy with R2 = 0.9608, followed by Neural Network (R2 = 0.9230) and Random Forest (R2 = 0.8822). These findings demonstrate the potential of machine learning approaches for optimizing scale inhibitor performance in oilfield operations
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it