Construct a Drilling Complexity Intelligent Prediction Model Based on the Case-Based Reasoning

In order to diagnose and predict the drilling complexities before drilling operations, and provide the drilling operators on well site with some hints, so that they are mentally aware of what kind of drilling complexity will occur in the future, and they could take the corresponding preventive measures to prevent the occurrence of some drilling complexities with the lowest possible economic cost. The handling methods of the drilling complexity cases that have occurred are treatment solutions made by the experts on well site based on their professional knowledge and years of rich drilling experience, which has a very important reference value for the later drilling operations. On the basis of case-based reasoning method, according to the adjacent well data, the computer technology, the artificial intelligence, and the data mining technology, this paper will construct a drilling complexity intelligent prediction model to utilize an open-source software and regression analysis method of causal relationship model. Use the ROC curve and confusion matrix to evaluate the performance of the drilling complexity intelligent prediction model, and the accuracy of the model is between 70–80%. It is recommended to use more drilling complexity cases to train the model in the later stage to improve the accuracy of the prediction model.