Drilling Problems Forecast Based on Neural Network

Abstract

This paper poses and solves the problem of using artificial intelligence methods for processing big volumes of geodata from geological and technological measurement stations in order to identify and predict complications during well drilling. Big volumes of geodata from the stations of geological and technological measurements during drilling varied from units to tens of terabytes. Digital modernization of the life cycle of well construction using machine learning methods contributes to improving the efficiency of drilling oil and gas wells. The clustering of big volumes of geodata from various sources and types of sensors used to measure parameters during drilling has been carried out. In the process of creating, training and applying software components with artificial neural networks, the specified accuracy of calculations was achieved, hidden and non-obvious patterns were revealed in big volumes of geological, geophysical, technical and technological parameters. To predict the operational results of drilling wells, classification models were developed using artificial intelligence methods. The use of a high-performance computing cluster significantly reduced the time spent on assessing the probability of complications and predicting these probabilities for 7-10 minutes ahead. A hierarchical distributed data warehouse has been formed, containing real-time drilling data in WITSML format using the SQL server (Microsoft). The module for preprocessing and uploading geodata to the WITSML repository uses the Energistics Standards DevKit API and Energistic data objects to work with geodata in the WITSML format. Drilling problems forecast accuracy which has been reached with developed system may significantly reduce non-productive time spent on eliminating of stuck pipe, mud loss and oil and gas influx events.