Intelligent Field Real-Time Data Reliability Key Performance Indices

Abstract

Abstract The dependency on intelligent field real-time data has significantly increased in the past few years for oil and gas operations. The high frequency real-time data is the baseline of critical analysis and decisions that can lead to maximize oil and gas recovery, increase revenue, and reduce environmental impact. A continuous massive amount of intelligent field real-time data flow is acquired from numerous instruments and transmitted through several distributed systems located in different area networks. The challenge that is facing the oil and gas companies is to keep the continuous data flow reliable. To achieve this objective, it is mandatory to continuously monitor the health of the field data quality and flow, instruments and communication. In addition, any unreliable data or communication failure must be addressed immediately and treated in highest priority to ensure high availability of the reliable data ready to be processed and analyzed. This paper will highlight Saudi Aramco's experience to improve intelligent field data reliability by developing key performance indices (KPIs). Those indices classify the data reliability into three main categories: Data Definition and Configuration, Data and Systems Availability, and Data Quality. Each category consists of a group of indices that contribute to the main category, and each category contributes to the overall data reliability KPI. Introduction As intelligent field technology evolves in the upstream oil and gas industry; the need to have a continuous and reliable feed of real-time data has significantly increased. Measuring the efficiency across various intelligent field infrastructure nodes remains a challenging task, especially for large oil and gas companies. Without a clear KPI measurement across various intelligent field nodes; the tracking of infrastructure deployment progress — as compared to the original plan — becomes a very difficult task. Therefore, pursuing the mission to identify infrastructure reliability, performance and value estimation becomes even more difficult. Establishing KPIs against best practices does not only help the company realize the full potential value from the investment, but also to build an infrastructure foundation that is flexible and reliable. This is essential to feed the integration and optimization layers in the intelligent field projects with quality data that can be converted into knowledge. Production and reservoir management engineers are required to take and implement immediate decisions to prolong the reservoir, the wells and the instrument's lives. These decisions must be taken based on a reliable data that comes from the instrumentations in the field and well-sites. Saudi Aramco has developed and implemented several intelligent field standards, specifications and guidelines to treat and manage the enormous real-time data in terms of integrity, quality, availability and reliability. The ultimate goal for these measures and enforcements is to ensure the high availability of reliable intelligent field real-time data (Al-Amer et al 2013). Intelligent field network implementation consists of two major components. The first component is the real-time data capturing and transmission systems which include instruments, remote terminal units (RTUs), supervisory control and data acquisition (SCADA), etc., in addition to the second component, which is data management and reliability (Naser and Awajy 2011).