Event-driven Information Integration for the Digital Oilfield

Abstract

Several operations in the Exploration and Production (E&P) sector are event-driven in nature and are supported by specialized systems and applications. Narrow focus of applications results in application silos that restrict the information sharing across verticals, which is a critical requirement for coordinated cross-functional efforts. Effective response to events warrants due emphasis on an integration strategy that facilitates desired information flow across verticals. Event-driven methods can be used to make strategic asset management decisions across silos in real-time, thus reducing response time and costs while improving asset performance.Complex event processing is an emerging research area that involves detecting complex events, processing the events, deciding actions for each event and notifying the relevant personnel about the event. In the E&P sector, the adoption of CEP and messaging-based systems in conjunction with semantic methods can facilitate components of the oilfield to communicate in real-time across different software platforms. Such an approach helps not only in detecting complex events across various sources, but also in processing them and deciding the actions to be taken, with the help of a knowledge base -thereby reducing information overload.Consider a typical application scenario -a pump failure event in an oilfield, which should elicit response not only by the pump operator but also by the maintenance engineers, production managers, reservoir engineers and other involved personnel. A proactive event-driven system enables quick detection of the failure across heterogeneous data sources and takes corrective actions while notifying the appropriate personnel. This facilitates effective communication across the teams and software systems involved.We propose a semantic complex event processing architecture for the digital oilfield that facilitates enterprise information integration. We delineate an illustrative use case of such integration for production optimization. Value propositions of the proposed framework include efficient interaction patterns, reduction in data seeking efforts, faster response times, building of consistent best practices and management by exception.

The process-oriented event model (PoEM)

“…Due to high frequency of event-related information in the O&G industry [1] and criticality of quick responses for business as well as health, safety and environmental aspects, it is pertinent to use event processing approaches that can handle structured, semi-structured and unstructured information extracted from system silos. We have described how CEP-based approaches can offer faster response times, reduced data seeking efforts, efficient interaction patterns, and management by exception for the digital oilfield [15].…”

“…Here, the CEP system should be able to determine the content of the notification message N shown in Equation 15. Event notification is typically limited to core event detection information but we introduce additional background information to enrich the notification message.…”

“…In addition to expected time of completion, the CEP system might include best practices related to the required action and additional information that is available in the background knowledge base (KB). N = β, α, τcomp, event-context (15) Using this approach it is possible to determine how long a subscription is valid. Subscriptions identified for an event can be maintained until the goal state is achieved.…”

Big Data in E&P: Real-Time Adaptive Analytics and Data-Flow Architecture

“…CEP capabilities are valuable in oilfield-event exception management, to issue alarms and alert operators so that they can avert a problem before it happens (Patri et al 2012). However, stream computing goes beyond CEP by providing a full modeling and simulation development and runtime environment that can be used to more finely control and optimize a process, taking the industry a major step closer to automation.…”

Predicting Failures from Oilfield Sensor Data using Time Series Shapelets

“…The shapelets found are visually interpretable, making deeper root cause analysis possible. Such time series mining methods are directly related to events in the oilfield, and as described in [2], there are several value propositions for efficient event processing [6].…”