Optimizing Fishbone Multilateral Well Productivity Forecasting with Machine Learning Techniques

Fishbone drilling (FbD), a premier method in multilateral well drilling, has been adopted in numerous global hydrocarbon fields. Its application leads to notable improvements in recovery rates while diminishing carbon emissions from the drilling process. The essence of FbD lies in constructing several minor holes that radiate in diverse directions. However, a significant obstacle with Fishbone drilling is the absence of comprehensive models to understand the impact of each Fishbone variable on total production, and their interplay. This research delves into the intricacies of fishbone well productivity prediction by leveraging a suite of machine learning algorithms, including RandomForest, GradientBoost, LinearSVC, AdaBoost, and KNeighbors. Through extensive model evaluation metrics such as MSE, RMSE, MAE, and the R-squared score, the study offers insights into the relative weightage of input features, with bottom-hole pressure emerging as paramount. The culmination of the research is the DrillFish Web App, an innovative platform designed for industry professionals to predict fluid flowrate and optimize fishbone drilling designs.