The Challenges with Drilling Real-Time Data and Proposed Data Management Framework

Abstract

Abstract One of the main pillars of the fourth industrial revolution (IR4.0) is the utilization of Internet of Things (IoT) components, which is a broad type of devices that include most types of sensors. These sensors are measuring primary physical states and transmitting their readings to a data acquisition system, which then supplies these data to consuming applications. In modern organizations, these applications are designed to use the sensors’ data to produce information that can be used by systems and users to make critical decisions. The effectiveness of the made decisions relies on the accuracy and quality of the gathered data from the used sensors. For this reason, the management of the streamed sensor data is crucial. In this paper, a Real-Time Data Management framework is proposed. The framework manages several key components of the data journey, such as data transmission standards, data quality protocol, communication infrastructure monitoring and automated notification process to address any issue in real-time. The first step is to transform all rigs to adopt the latest industrial standards for drilling real-time sensors data transmission. Then establish a data quality measurement using a set of different metrics that can reflect an objective explanation of the state of a given data streamed by a sensor or a system of connected sensors. It also helps in troubleshooting a faulty system and identify the nature of the problem and the root cause of it, especially in large systems with thousands of connected sensors. Finally, real-time monitoring of the full journey of the data and its components and notify relevant teams as issues rises. Successful employment of the automated drilling real-time data management framework will result in an autonomous process to manage a large fleet of operating rigs with minimal to no human intervention. It will also enable the full utilization of high-quality data for many successful IR4.0 solutions. In this paper, a wide group of real-time data quality assessment measures are discussed to show their importance and application. These measures will help data managers find issues in field streamed real-time data. This paper can be a reference for any data quality assessment project as the discussed measures can be applicable to all real-time data types.