Characterization of operational safety in offshore oil wells

The main concern of activities developed in oil and gas well construction is safety. But safety during the well construction process is not a trivial subject. Today risk evaluation approaches are based in static analyses of existent systems. In other words, those approaches do not allow a dynamic analysis that evaluates the risk for each alteration of the context. This paper proposes the use of Quantitative and Dynamic Risk Assessment (QDRA) to assess the degree of safety of each planned job. The QDRA can be understood as a safe job analysis approach, developed with the purpose of quantifying the safety degree in entire well construction and maintenance activities. The QDRA is intended to be used in the planning stages of well construction and maintenance, where the effects of hazard on job sequence are important unknowns. This paper also presents definitions of barrier, and barriers integrated set (BIS), and a modeling technique showing their relationships. (c) 2006 Elsevier B.V. All rights reserved.

Automatic Classification of Three-Phase Flow Patterns of Heavy Oil in a Horizontal Pipe Using Support Vector Machines

The pipe flow of a viscous-oil-gas-water mixture such as that involved in heavy oil production is a rather complex thereto-fluid dynamical problem. Considering the complexity of three-phase flow, it is of fundamental importance the introduction of a flow pattern classification tool to obtain useful information about the flow structure. Flow patterns are important because they indicate the degree of mixing during flow and the spatial distribution of phases. In particular, the pressure drop and temperature evolution along the pipe is highly dependent on the spatial configuration of the phases. In this work we investigate the three-phase water-assisted flow patterns, i.e. those configurations where water is injected in order to reduce friction caused by the viscous oil. Phase flow rates and pressure drop data from previous laboratory experiments in a horizontal pipe are used for flow pattern identification by means of the 'support vector machine' technique (SVM).