Complex-Event Processing for the Intelligent Field

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 167817, ’Implementation of Complex-Event Processing for the Intelligent Field,’ by Muhammad Al-Gosayir, Moayad Al-Nammi, Waleed Awadh, Abdullah Al-Bar, and Nasser Nasser, Saudi Aramco, prepared for the 2014 SPE Intelligent Energy Conference and Exhibition, Utrecht, the Netherlands, 1-3 April. The paper has not been peer reviewed. Every day, reservoir and production engineers are presented with massive streams of real-time data from all kinds of intelligent-field equipment. The existing systems for data cleansing and summary are based on batch processing; hence, engineers find it challenging to make the right decision on time and do not have the capacity to instantly detect interesting patterns as data arrive in streams. This paper addresses the architecture, implementation, and benefits of complex-event processing (CEP) as a solution for the intelligent field. Introduction CEP is an innovative, rising technology designed to handle large amounts of data with minimal latency in real time. This technology can aid in the detection of trends and anomalies in the data stream such as unusual buildup or drawdown in well pressure in real time. Furthermore, CEP is an event-driven solution, meaning that it is triggered by events such as changes in downhole pressure in order to perform computational logic to calculate average reservoir pressure. This process will in turn provide reservoir and production engineers with clean real- time data and notifications of prominent events. Overview of Intelligent-Field Structure. For the implementation of the intelligent field, Saudi Aramco has adopted a four-layered architecture. These layers are surveillance, integration, optimization, and innovation, as shown in Fig. 1. The surveillance layer is responsible for continuous monitoring of real-time production data and makes use of data-management tools to ensure the validity of the data. The integration layer processes real-time data to detect trends and anomalies. These anomalies are referred to reservoir engineers for analysis and resolution. The optimization layer streamlines field-optimization capabilities and management recommendations. The innovation layer stores event knowledge and triggers optimization processes and actions throughout the field’s life cycle. This final layer captures “intelligence” and injects it into the system. Real-time data traverse multiple zones of instrumentation, networks, and data servers before they reach the corporate database and the engineer’s desktop (Fig. 2). A typical piece of data captured by a sensor will be transmitted automatically from the sensor and will pass through multiple data servers connected through wired/wireless networks. The data eventually reside in the corporate database.