Achieving Productivity and Operational Efficiency, and High-Quality Data Through Automation in Well Log Data Quality Control and Acceptance Process Using AI/ML Techniques

Abstract

Well log data and reports volume generated by oilfield operations in Kuwait Oil Company (KOC) have increased tremendously over the last decade. Currently, KOC receives around 100 to 200 well log packages every week. Each package may contain hundreds of well log files. Mostly, the processes to receive, manage and load this amount of data into the Corporate Database were manual, involving 7 to 9 resources. Logging companies may have to wait a minimum of a week to receive the decision if their data delivery is approved or rejected. The Exploration and Production Information Management (E&P IM) team started an initiative to accelerate and automate these processes by developing and implementing a solution using Artificial Intelligence and Machine Learning techniques to increase productivity and operational efficiency and achieve high-quality data. The main results are high-quality data that meet E&P IM Well Log Standard and Specification, the processing time is reduced by 80%, and the operational efficiency is improved by 66%, allowing the resources assignment to more valuable activities.