Nowadays, there is a massive amount of real time data that flows from the intelligent sensors to the petroleum engineers desktop. High quality data is required to utilize in production workflows and technical studies. The reliable transmitted data as real-time pressure and temperature will enable the petroleum engineer to track the performance of the wells, generate production strategies, and monitor the real-time production, and a lot of more workflows remotely from his/her desktop without jeopardizing the well condition or the reservoir structure. In Saudi Aramco, monitoring the quality of real-time data for validity, accuracy, consistency and intelligent field equipment integrity is accomplished via the utilization of state-of-art intelligent field data health monitoring platform. It provides an alarming system of problematic data and equipment. It also generates a daily data quality report for a specific intelligent field data transmission node. This paper is a continuation of the technical paper titled "Intelligent Field Real-Time Data Reliability Key Performance Indices" (IPTC-17997-MS, Kuala Lumpur, Malaysia, 10-12 December 2014). This paper will highlight the tangible benefits of implementing the new monitoring platform in four different examples that show the importance of having intelligent field data reliability tracking solution in each oil and gas company to improve the data reliability. Making the decision process accurate, through utilizing an advanced automated data management platform, is crucial butreceiving real-time quality data is essential.

The dependency on well-head and field real time data has significantly increased in the past few years in the oil and gas industry. The data is used to maximize oil and gas recovery, increase revenue and improve health and safety. This new mode of operation mandates the availability of reliable real time information sent by the different well and field digital equipment for optimum right-time decision making. The challenge is in the fact that intelligent field components include many different layers and nodes that increase the complexity of such assurance. Therefore, the health and the data transmitted from these components, such as digital gauges (e.g., multi-phase flow meters and permanent downhole monitoring systems) and the communication network must be continuously monitored and well maintained. This paper will highlight a newly developed comprehensive and interactive monitoring and key performance indices reporting solution to quantify, assess and visualize the health of the different intelligent field components. The new system is using advance technologies, such as GIS maps to locate fields, equipment, real-time data historian servers, and networks to highlight their availability and reliability status. This solution provides real time alerting and alarming in case of transmission failure, well data integrity situation or inaccurate data detected using an automated diagnostic engine, thereby maximizing safety and increasing accuracy and data reliability. IntroductionIntelligent (or digital) field is basically a remote acquisition and utilization of real-time surface and subsurface equipment data to monitor and control field processes in a collaboration environment to reduce production cost, real-time optimization and maximize field live value . Intelligent field concept is being a mandatory in the dynamic oil and gas handling operations. In order to achieve that goal, the right data need to be delivered in the right time using the right optimization tools. The realtime data is the foundation layer for any optimization process. With the current dynamic operation mode, a huge amount of data is received in a real-time basis which is required to be validated and verified.

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).