Unlocking the Power of Artificial Intelligence: Building Digital Twins with Classification Algorithms for Optimized Geothermal Drilling
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Keywords:
Geothermal Energy, Digital Twin, Artificial Intelligence, Drilling, Classification AlgorithmsAbstract
Geothermal energy has emerged as a promising renewable energy source due to its sustainability for long-term power generation. Effective drilling practices are crucial for the successful utilization of geothermal resources, as they directly impact the productivity and operational efficiency of geothermal systems. However, optimizing drilling operations in the geothermal sector presents unique challenges due to the complex subsurface conditions and the need for continuous monitoring and optimization. In recent years, the integration of artificial intelligence (AI) technologies has shown great promise in improving various industrial processes. The energy sector, including geothermal energy, has started leveraging AI techniques to enhance operational efficiency, reduce costs, and minimize environmental impacts. Among the diverse AI methods, machine learning algorithms have gained significant attention for their ability to analyze large datasets, extract patterns, and generate predictive models. This paper focused on the potential benefits and challenges of utilizing AI, particularly the classification algorithms, in the context of geothermal drilling. Such as Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine (LightGBM) are a powerful machine learning algorithms known for its effectiveness in handling structured datasets and achieving high predictive accuracy.
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