Stuck Pipe Prediction in Geothermal Well Drilling at Darajat Using Statistical and Machine Learning Application

Stuck pipe during Geothermal well drilling is one of the problems that affect non-productive time (NPT) which prolong the drilling schedule. This problem directly increases drilling cost due to increasing overall drilling time, consumables, mud, drill bit and as well as sacrifices its well when there is no more progress to reach the targeted reservoir depth. Also the stuck pipe problem will create a domino effect that might impact next well drilling and another site drilling schedule. Stuck pipe problem is made from many contributor factors and also related to other problem cases such as lost circulation, wellbore instability, temporary zone closure, and cementing job trouble. The current analytical method mostly uses one or two dominant variables to predict stuck pipe problems which limitedly represents drilling conditions, lithology and wellbore conditions. A combination of statistical method and machine learning approach is developed and comprehensively used to predict stuck pipe problem that represents different well lithology, wellbore and drilling condition. Historical data that represents the conditional situation and electronic data recorder is used to predict the stuck pipe problem which different for each of the well. Many factors contribute to successful drilling activities and no one single factor, which dominantly becomes the root cause of the problem. A statistical approach such as principal component analysis, logistic regression and discriminant analysis is very helpful to interpret data before the next step of the analysis. Those statistical analyses should be the baseline of data interpretation before creating an algorithm to use predictive analysis using machine learning and deep learning.