Attempt of Lithology Prediction from Surface Drilling Data and Machine Learning for Scientific Drilling Programs

摘要

Japan Agency for Marine-Earth Science and Technology (JAMSTEC) operates the scientific deep-sea drilling vessel Chikyu for scientific studies on past climate changes,long-term crust transportation,biological and abiotic processes associated with hydrocarbon production,and earthquakes,the last being one of the most important research topics.Chikyu conducted the Japan Trench Fast Drilling Program (JFAST) to obtain core samples from sediment layers under the seabed for study on the large Tohoku earthquake which triggered the devastating tsunami in 2011.Chikyu has also started to conduct the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) as the Nankai Trough is one of the most active earthquake zones.As one of the primary goals of scientific drilling is to evaluate sediment properties,it is highly beneficial to characterize the lithology of the drilling layer during drilling operations.Even an approximation of these properties could potentially provide valuable information for conducting coring operations.Thus,a previous study attempted to discuss the properties of sediment layers using surface drilling data,and estimated the shear stress of the sediment.In this study,we summarize the method applied in the previous paper for estimating the torque at the drill bit from the surface measured drilling data for NanTroSEIZE deep riser drilling operation.This data was then used to directly calculate the shear stress of the sediment.The drilling torque at the bit differed from the surface torque,where this difference increased with increasing drilling depth.Our estimations of the drilling torque were validated using torque data obtained from logging while drilling (LWD) operations.We also used machine-learning approaches to predict the lithology,where learning data was created from surface drilling data and lithology information from core samples obtained during past scientific drilling operations.Machine learning was then applied using neural network algorithms by tuning the number of layers to create a predictive model.This paper discusses the preliminary attempt to predict the lithology using machine-learning approaches for NanTroSEIZE and JFAST data.