In drilling engineering, Key Performance Indexes (KPIs) are import aids for management optimization and technical improvement, such as drilling time, monthly footage, rate of penetration and non-productive time,etc.

Almost all oil companies have deployed database systems to manage drilling information, where maybe millions of daily reports are incorporated. While in international oil companies, some shortcomings of conventional data management system are gradually shown: reliability decreasing of data analysis reports during transferring in different levels due to manual intervention, huge amount of unstructured data in losing risk, low automation of KPIs analysis, low tolerance of data processing procedure to poor quality data, various data standards of different fields, poor performance in KPIs visualization.

To solve the above problems, an integrated and intelligent drilling KPIs analysis system based on modern data science techniques were developed and deployed in an international oil company.

The system architecture was designed in 4 sections: unstructured data processing unit, database and management system, KPIs extraction engine and data visualization dashboard.

Unstructured data processing unit was designed to convert reports form heterogeneous data sources to the main database, including Excel, pdf and isometric databases. A 5-layers data quality control procedure was designed to ensure the reliability of data for KPIs analysis. Specified extraction algorithms was designed considering several realities, such as reports or data missing, and data error. A distance-based model to evaluate the similarity of two wells by well properties was developed, providing an intelligent way for benchmark and knowledge sharing between wells. A drilling anomaly detection model was developed by deep learning and natural language processing to solve the problem of ununified coding system in different fields. A data visualization dashboard with high efficiency, reliability and flexibility was designed to provide information for different users.

By deploying the system, tens of thousands of daily reports from heterogeneous data sources were automatically incorporated to the main database, and millions of dollars of manually processing cost were saved. The data analysis standard was unified by the system, making it possible for benchmarking and knowledge sharing in different oilfields. Thousands of wells’ information was activated by the system, and the outputs provide support for management optimization, technical improvement, drilling performance benchmarking and feasibility evaluation of new investment. The time for users to analysis the KPIs of afield was shorten form several weeks to almost zero and the data quality was validated and improved in real time by the feedback of KPIs analysis.

A 7-layer convolutional neural network was trained based on 1700 wells’ data to detect anomaly in daily reports with accuracy of 85%. A new distance-based model was developed to evaluate the similarity of two wells.