Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization
Bibliographic record
Abstract
In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".