# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
## 2 Related Work
### Machine Learning and Ensemble Learning for Drilling Cost Prediction  
The application of machine learning and ensemble learning techniques has significantly transformed drilling cost forecasting, providing improved accuracy and robustness compared to traditional methods. [PERSON] [16] proposed the use of machine learning methods for cost forecasting in the energy sector and subsequently introduced an ensemble approach to validate its effectiveness in improving prediction accuracy. [PERSON] et al. [17] developed a Genetic Algorithm-Back Propagation (GA-BP) neural network-based model specifically for ultra-deep well drilling cost prediction. This model exploits the global optimisation capabilities of genetic algorithms in conjunction with the learning capabilities of back-propagation neural networks and demonstrates particular effectiveness in dealing with the inherent complexities of ultra-deep well drilling. [PERSON] et al. [18] introduced a stacked generalisation ensemble model to optimise and predict the rate of penetration (ROP) in gas wells. A case study conducted in Xinjiang showed that ensemble methods significantly improved prediction accuracy compared to individual models. The stacked model outperformed single models such as random forest and gradient boosting, demonstrating its superior predictive capability.  
[PERSON] and [PERSON] [9] investigated the potential of machine learning and data analytics to improve drilling efficiency in the vicinity of neighbouring wells. Their methodology uses historical data from neighbouring wells to optimise drilling parameters and reduce costs. It was shown that transfer learning techniques can be effectively used to apply knowledge from one well to improve predictions for nearby wells. [PERSON] et al. [19] conducted a comparative analysis of machine learning techniques for predicting ROP in geothermal wells, using a case study from the FORGE research site. They evaluated the performance of algorithms, such as random forests, gradient boosting and neural networks, and found that random forests achieved the highest accuracy in their case study. The authors highlight the importance of representative training data and feature engineering for effective ROP modelling.  
[PERSON] and [PERSON] [20] explored the potential of machine learning to improve drilling efficiency in the oil and gas industry. The study demonstrated the potential of AI-driven approaches to optimise drilling operations and reduce associated costs. The researchers investigated the applicability of a number of machine learning algorithms to different aspects of drilling optimisation. [PERSON] et al. [21] developed a novel model for predicting the rate of penetration in drilling operations using convolutional neural networks (CNN). The approach demonstrated the potential of deep learning techniques to capture complex patterns in drilling data, thereby enabling more accurate ROP predictions. [PERSON] et al. [22] proposed an intelligent drilling methodology for oil and gas wells using response surface methodology and artificial bee colony optimisation. Their hybrid approach showed promise in optimising drilling parameters and reducing costs. These studies illustrate the growing prevalence of advanced machine learning and ensemble techniques in addressing the complexities of drilling cost prediction and optimisation and represent a significant advance in the field.