This paper presents a data driven approach for failure prediction for Electrical Submersible Pumps (ESP). ESP system is well known as an effective artificial lift method which has been applied to about 20 percent of almost one million wells worldwide. Well failures lead to production loss and generally the repair cost for an ESP is usually much higher than those of other artificial lift systems, thus predicting ESP failures before they occur will be valuable. We apply advanced machine learning techniques for predicting ESP failures using electrical and frequency data from the field. Data from real-world assets using ESP systems is analyzed to learn examples of normal well and failure conditions. A generalized Support Vector Machine (SVM) trained with a set of selected features is developed and this approach is tested on real world data. Our results show that this approach works well based on feedback from subject matter experts on the results.

This paper discuses intelligent techniques used to monitor and correct operational abnormalities in Autonomous Underwater Vehicles. Neural Networks are usually utilised in the diagnosis section, while Fuzzy Logic is implemented in the prognosis and remedy sections. The performance of an AUV’s sub-system has a great affect on the overall success of the vehicle. Once a sub-system becomes faulty, the various components associated with the control of the AUV may get influenced, which can degrade the overall performance of the integrated system or make it invalid altogether, [1]. Such failures may result in large amounts of wasted time, loss of data and increases in mission costs.

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The emerging trend in Oil and Gas industries for multi-disciplinary teams spread across diverse geographical locations is virtual team working. This concept is a key enabler in a digital oilfield environment where real time communication enables efficient decision making between field and office operations. The Shell Smart Fields Program, Shell's digital oilfield initiative, has deployed the use of Collaborative Work Environments (CWE) as an enabler to optimize operational value across its operating units globally.The fundamental pillars supporting a CWE implementation are 1) People, 2) Work Process, 3) Tools and Applications and 4) Facility. During the design and implementation phases of building a CWE, emphasis is typically placed on work process, tools and applications, and facility while the people aspect is embedded within other improvement areas. The Smart Fields Program has observed that integration of the People aspect is critical to ensure a CWE's success. Without an effectively and efficiently trained workforce, the new CWE processes will not flow as intended. To address this, Shell has developed a tested methodology focused on driving the required behavioral change to achieve the necessary performance. This paper will focus on the importance of the people aspect in a CWE implementation, based on a 2009 improvement effort centered on Human Factors Integration (HFI) and behavioral change coaching. In particular, this paper will address:• The role of HFI / coaching in CWE implementations • Identification of relevant people issues • How to provide continued / ongoing support to the 'digital' workforce • Identification within the organization of ownership for the new collaborative behaviors • The necessary organizational structure required to support new ways of working • Required behavioral changes to support the future workforce • Key differences between staffing operations in a current oilfield versus a digital oilfield • Lessons learned from deployment of above points

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