Exploration Missed Opportunities Identification using Total Gas Augmented Machine Learning

Accurately identifying net pay zones is crucial for maximizing hydrocarbon recovery and optimizing field development. Traditional methods often struggle with geological complexities. This study integrates total gas (TGAS) measurements from mud logs, gamma ray (GR), and resistivity (RT) from well logs to enhance net pay zone predictions. TGAS reflects the gas content of the subsurface formations which complements conventional logs.

We utilized the Random Forest Classification algorithm, which is adept at handling complex geological data. This involved preprocessing TGAS, GR, and RT data through cleansing, normalization, and division into training and validation sets. The model focused on maximizing recall to identify potential hydrocarbon zones, accepting a higher false positive rate for broader detection.

The model’s performance was evaluated across 12 wells. While the training data showed perfect scores the test data indicated overfitting with lower scores (F1 at 0.62, precision at 0.60, and recall at 0.63). Some wells, such as K1 (recall 0.34), performed better, though others had poor recall due to a lack of true positive identifications.

To minimise false negatives, future recommendations include adjusting classification thresholds, cost-sensitive learning, and ensemble methods. Integrating data science enhances efficiency, reduces costs, and maintains competitiveness in the energy sector.