Cost estimation is an essential part of every drilling project's well planning, and it involves predicting the rate of penetration (ROP) accurately. The ROP represents the amount of time required to drill a given depth, and maximizing it helps minimize the costs associated with the drilling budget. However, predicting the ROP accurately is challenging because it depends on numerous variables, including drilling parameters, drilling fluid properties, and drilled formation characteristics.
One approach to improving the accuracy of ROP prediction is by using machine learning techniques, such as gradient boosting. In a recent study conducted in the Rumaila oilfield, the researchers used gradient boosting to predict the ROP based on drilling operation parameters and drilling fluid properties for two wells used for training and testing and one well used for implementation. The results of the study showed that gradient boosting was successful in predicting the ROP, with R2 training and testing values of 0.9947 and 0.8611, respectively. This means that the model was highly accurate and could be used to improve cost estimation in drilling projects.
Overall, the use of machine learning techniques such as gradient boosting can help enhance the accuracy of cost estimation in drilling projects by predicting the ROP more accurately, minimizing the costs associated with the drilling budget, and improving the overall efficiency of the drilling process.