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The term "Real-Time Optimization" (RTO) has rapidly found its way into common usage in the oil and gas industry, as it already has in many others. However, RTO in the oil and gas industry is usually used more as a slogan rather than describing a system or process that truly optimizes anything at all, let alone does so in real-time. In this paper, we describe what RTO means in the exploitation of hydrocarbons and what technologies are available now and are likely to be available in the future. We discuss how it is misunderstood and what real financial benefits await those who adopt it. Furthermore, we are working toward developing a method of classification to allow us to establish where a field operation lies on the RTO ladder, and to help plan a strategy to generate the benefits that moving up the RTO ladder can offer on specific fields and assets. The paper also describes a new SPE Technical Interest Group (TIG), explaining why it has been formed, and outlining its objectives and some planned deliverables. Real-time Optimization - Concepts and Definitions What is optimization? Intuitively most people agree on what we mean by "optimize." This comes down to understanding the dictionary definition; that is, to make the most of; to plan or carry out an economic activity with maximum efficiency; to find the best compromise among several often conflicting requirements, as in engineering design. Therefore, examples of what is usually meant by optimization in the oil and gas industry include:Maximizing hydrocarbon production or recovery,Finding the best solution in the region of physical and financial constraints to produce a decision,Maximizing net present value (NPV) through changes in capital expenditure (CAPEX) and/or operational expenses (OPEX). These elements, in turn, improve financial efficiency in portfolio management and risk analysis, andAdvanced real-time optimization: behavioral prediction and inference, pattern recognition to identify states of a group of wells, continuous adaptation and self-tuning ability. Although we may readily agree on these (and other) descriptions of what would be the outcome of optimization, agreeing what it actually means appears to be more complex. The reason for this is that the term optimization is usually used very loosely, whereas it needs to be defined rigorously and mathematically, while honoring the real-life physical system constraints that exist in the overall production process.

This paper looks to the natural world for solutions to many of the challenges associated with the design of fixed-wing cross-domain vehicles. One example is the common murre, a seabird that flies from nesting locations to feeding areas, dives underwater to catch prey and returns. This hunting expedition provides an outline of a possible mission for a cross-domain vehicle. While the challenges of cross-domain vehicles are many, the focus of this paper was on buoyancy management and propulsion. Potential solutions to each challenge, inspired by multiple animals that cross between aerial and underwater domains, are investigated. From these solutions, three design concepts are considered, a quadrotor/fixed-wing hybrid, a vertical takeoff and landing (VTOL) tailsitter aircraft, and a waterjet-assisted takeoff vehicle. A comparison was made between the capability of each concept to complete two missions based on the common murres' hunting expedition. As a result of this comparison, the VTOL tailsitter design was selected for further study. In-depth design was conducted and a prototype vehicle was built. The completed vehicle prototype successfully conducted submerged operation as well as four air flights. Flights consisted of egress from water, flight in air, ingress into water in each flight, and water locomotion. A total of 11 min, 23 s of flight time was recorded as well as underwater swims down to 12 ft (3.7 m) below the surface.

The U.S. Defense Advanced Research Projects Agency's (DARPA) Neovision2 program aims to develop artificial vision systems based on the design principles employed by mammalian vision systems. Three such algorithms are briefly described in this paper. These neuromorphic-vision systems' performance in detecting objects in video was measured using a set of annotated clips. This paper describes the results of these evaluations including the data domains, metrics, methodologies, performance over a range of operating points and a comparison with computer vision based baseline algorithms.