Wellbore Trajectory Prediction Using Machine Learning and Co-located Well Comparison

The system performs preliminary actions by training the machine learning model with historical wellbore data before actual well planning. This pre-training phase stores learned patterns and relationships in the model, enabling accurate predictions during actual use without requiring complex real-time computations. The model learns from past data including wellbore paths, geological formations, and drilling parameters to establish predictive capabilities in advance.

The system creates a virtual copy of the wellbore path by generating a hypothetical wellbore path that mirrors the intended trajectory through the target stratigraphic interval. This virtual model is then compared against actual drilled wellbore paths from co-located wells to identify deviations and improve prediction accuracy. The copying approach allows virtual testing and optimization without physical drilling risks.

The machine learning model performs self-service by automatically learning patterns from historical wellbore data during the training phase. Once trained, the model independently generates predictions for new wellbore paths without requiring manual analysis of detailed drilling plans. The system serves itself by utilizing the trained model's internal knowledge to make predictions, reducing the need for extensive human intervention and data processing time.

The system performs preliminary data processing by training the model with historical data before actual well planning. This pre-computation phase stores extracted patterns and relationships in the model's structure, enabling rapid predictions during actual use. The time-consuming data analysis is performed once during training rather than repeatedly for each new wellbore plan.

The system implements feedback by comparing the hypothetical wellbore path generated by the machine learning model against actual wellbore paths from co-located wells. This comparison provides feedback on prediction accuracy and identifies systematic deviations. The feedback loop allows the model to learn from real-world outcomes and improve future predictions, enhancing reliability while maintaining efficiency through automated comparison processes.

The system performs preliminary validation by generating a hypothetical wellbore path before actual drilling using the trained machine learning model. This pre-prediction phase allows assessment of potential wellbore trajectories and identification of high-risk deviations from the target stratigraphic interval. By evaluating the hypothetical path in advance, the system can alert operators to potential issues before committing to the actual drilling operation.