An approach to automatical well logging depth matching with the use of statistical methods

Abstract

© SGEM2019. The geological modeling is an actively developing area of petroleum geoscience. One of the limiting factors for geological modeling is the processing of input data, which is a routine process and can be automatized. The paper describes the screening for an effective approach and development of the workflow allowing automatical depth matching of well logs with the use of statistical methods of data analysis basing on the real set of wells. The purpose of this work is to study the machine learning algorithms application for the well logging depth matching for the deposits of the Bobrikian horizon in one of the Tatarstan oilfields. Several alternative approaches and mathematical realizations for a set of well logs of standard, radioactive and electric logging were considered. As a result, the authors choose the optimal algorithm that allows to automatically perform depth matching of the well logs for object of study basing on the comparison of results obtained by different methods.