Examples of Right-Time Decisions From High Frequency Data

Abstract

Asset performance can often be improved through continuous monitoring and/or through better utilization of information extracted from the high frequency data that are becoming more readily available in today's digital world. ExxonMobil has a long history of applying advanced technologies in asset management. Today, we continue to use new hardware, integrated software, and improved data infrastructure to enhance asset management workflows. ExxonMobil is taking an enterprise-wide approach (Reece et. al. 2008) to implementing digital technology in asset management. This paper presents four examples where ExxonMobil has taken advantage of high frequency data for timely asset management decisions. These four examples represent implementation at four different operational scales: for reservoir management, for well management, for facility management, and for plant management. The four examples are:For a West African reservoir: Permanent downhole pressure gauge data have added value in reservoir modeling that in turn provided a method for calculating reservoir rates;For South Texas gas wells: Real-time data access and charting capabilities were implemented and advanced data analysis explored to identify well events and manage well work-over activities aided by artificial intelligence;For Norwegian oil fields: An integrated facility model was developed and tuned for surveillance and operation of a network of wells and production facilities that are shared by multiple fields;For an Australian production plant complex: Production from offshore platforms, a gas plant, a crude stabilization plant, a fractionation plant, and a tank farm was optimized with high frequency data and automatic process control. Introduction ExxonMobil has a long history of applying advanced technologies in asset management. As early as 1967, a CPC (Computerized Production Control) program was implemented in the company's US production assets. More recently, various ExxonMobil business units have implemented permanent monitoring (Chorneyko, 2006), automated artificial lift systems, and other digital technologies for efficiency improvement. Since 2000, the company has moved toward global standardization and best practices. The EM2010 (Reece et. al., 2005) program, which is currently in progress, has developed a business-driven vision and roadmap to a more integrated and automated subsurface work environment. Today, advances in digital technology are delivering many software, hardware, and infrastructure improvements that provide capabilities in remote real-time data access, right-time data analysis, right-time visualization, right-time optimization, and on-demand remote operability. The intersection of digital technology and asset management is an area that, if exploited, can improve operation and recovery. We believe these opportunities will increase as the use of digital technologies in asset management grows and as digital technology allows semi-automation of workflows. The digital oil field has been a corporate strategic focus area since 2000 for many companies in the oil and gas industry. Major international oil companies have trademarked their approaches (e.g. Smart FieldsTM, Field of the FutureTM, and i-FieldsSM). ExxonMobil's approach to digital oilfields is to focus on the application of digital technologies to improve and enhance asset management workflows, fully leveraging our global functional organization and worldwide standardized asset management processes.

An Enterprise-Wide Approach to Implementing ‘Digital Oilfield'

“…Real-time use of Permanent Downhole Gauge Data -In-house tools and customized vendor products to improve transient analysis for high-frequency real-time data 5,6 .…”

Advanced Machine Learning Methods for Production Data Pattern Recognition

“…ExxonMobil has a long history of applying a disciplined and value-driven approach to the deployment of advanced technologies in asset management. In order to effectively implement and sustain these advancements, we are concurrently working on addressing both the technical challenges mentioned above as well as organizational and change management concerns, and this has been reported in a number of prior publications (Shyeh et al, 2008;Killian et al, 2012;El-Bakry et al, 2012).…”