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.

Recent developments in mathematical techniques have allowed the development of a software package to simulate and optimise the flow of hydrocarbons through oil and gas production and distribution networks of arbitrary connectivity and complexity. It has been successfully applied to a wide variety of operational problems including de-bottlenecking, optimisation of compressor strategies and determining the optimal lift-gas allocation to networks of gas lifted wells. This paper discusses the innovative mathematical concepts at the heart of the algorithm taking account of both optimisation and modelling aspects. It demonstrates how an integrated approach to optimisation and simulation based on Sequential Linear Programming (SLP) techniques provides the user with substantially enhanced flexibility and reliability compared with traditional nodal analysis type approaches and how these benefits can be realised in concrete practical examples. Descriptions of the behaviour of physical objects in a production or distribution network is commonly given in the form of measured data sets or discrete performance tables rather than analytical functions. A final focus of this paper is the discussion of how such data sets can be used directly and efficiently in an optimisation context. Introduction Throughout field life hydrocarbon production facilities need to be designed, built, maintained and altered to meet the developing needs of the hydrocarbon reservoirs to which they are connected. Computer models of the behaviour of the facilities are an essential pre-requisite to the efficient management of this process. In this paper we introduce a simulation and optimisation method for hydrocarbon production networks based on a technique called sequential linear programming (SLP). In the sections below we first introduce the concept of SLP and how it can be applied to hydrocarbon production. We then proceed to discuss the key advantages of our technique over other more traditional methods. Additionally, we point out how recent developments in mathematical modelling and optimisation have allowed us to enhance the robustness and speed of our approach. Since its commercial release in early 1999 our technique has been successfully applied to a wide variety of production system design and optimisation problems. For this paper we have chosen two recent case studies. The first is a de-bottlenecking study to enhance the production rates achieved from a medium sized gas gathering network. The second study demonstrates how the software package can be used to optimise the usage of produced associated gas in a highly complex multiphase hydrocarbon production system. Sequential Linear Programming (SLP) Sequential linear programming is a family of techniques for the solution of large-scale non-linear optimisation and simulation problems. The philosophy of the approach was first introduced by Griffith and Stewart who applied it to hydrocarbon processing systems.1 For a recent description of the approach see e.g. Bazaraa et al.2

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRecent developments in mathematical techniques have allowed the development of a software package to simulate and optimise the flow of hydrocarbons through oil and gas production and distribution networks of arbitrary connectivity and complexity. It has been successfully applied to a wide variety of operational problems including de-bottlenecking, optimisation of compressor strategies and determining the optimal lift-gas allocation to networks of gas lifted wells. This paper discusses the innovative mathematical concepts at the heart of the algorithm taking account of both optimisation and modelling aspects. It demonstrates how an integrated approach to optimisation and simulation based on Sequential Linear Programming (SLP) techniques provides the user with substantially enhanced flexibility and reliability compared with traditional nodal analysis type approaches and how these benefits can be realised in concrete practical examples. Descriptions of the behaviour of physical objects in a production or distribution network is commonly given in the form of measured data sets or discrete performance tables rather than analytical functions. A final focus of this paper is the discussion of how such data sets can be used directly and efficiently in an optimisation context.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA total system production optimization model has been implemented in a complex gas-lifted offshore operation, resulting in production gains and operating cost reductions. Whereas previous optimization models considered only the wells and production gathering network, the new model is able to consider the combined performance of the total system, including downhole well configurations, the complex production gathering and lift-gas distribution pipeline networks, separators, compressors and pumps. The model is applicable to most gas-lifted fields, and will be particularly beneficial when applied to those with complex production systems, and where compressors are a constraint on total system performance.The output from the optimization model principally comprises recommended values for individual well gas lift injection rates, separator pressures, compressor discharge pressures and compressor utilization. Field results are presented in the paper to demonstrate how implementing the optimizer's recommendations in the field resulted in economic benefits through increased production and reduced operating costs. Also described is how the model allows field operations engineers to re-optimize field control parameters on a more frequent basis and with less manpower than previously.The successful implementation of a complex model with such a broad scope is as dependent upon the implementation process as it is upon the technology. Therefore, in addition to describing the details of the model itself, this paper will cover the issues that arose during the implementation and how they were resolved. These include the level of manpower and support required, project organization and execution, and the processes required to sustain the benefits after the initial optimization gains have been realized.

A total system production optimization model has been implemented in a complex gas-lifted offshore operation, resulting in production gains and operating cost reductions. Whereas previous optimization models considered only the wells and production gathering network, the new model is able to consider the combined performance of the total system, including downhole well configurations, the complex production gathering and lift-gas distribution pipeline networks, separators, compressors and pumps. The model is applicable to most gas-lifted fields, and will be particularly beneficial when applied to those with complex production systems, and where compressors are a constraint on total system performance. The output from the optimization model principally comprises recommended values for individual well gas lift injection rates, separator pressures, compressor discharge pressures and compressor utilization. Field results are presented in the paper to demonstrate how implementing the optimizer's recommendations in the field resulted in economic benefits through increased production and reduced operating costs. Also described is how the model allows field operations engineers to re-optimize field control parameters on a more frequent basis and with less manpower than previously. The successful implementation of a complex model with such a broad scope is as dependent upon the implementation process as it is upon the technology. Therefore, in addition to describing the details of the model itself, this paper will cover the issues that arose during the implementation and how they were resolved. These include the level of manpower and support required, project organization and execution, and the processes required to sustain the benefits after the initial optimization gains have been realized. Introduction Dubai Petroleum Company (DPC) has implemented a production optimization tool that has yielded production gains and operating cost reductions. The field optimization software is used to model the complex production networks associated with the gas-lifted fields, including the downhole well configurations and the surface facility components such as gas compressor trains, pipelines and surface pumps. Key benefits realized from all fields were a 3% total production increase, a 4% reduction in lift gas requirements and a 3% reduction in operating costs. Field operations support was critical to the project's success by continuously tracking the operational parameters throughout implementation to validate the recommendations and results. The project was planned to be executed in three phases, including a pilot study, to assess the value of a full field model and to identify and resolve implementation challenges. The full field model was implemented during 2003 and produced several key learnings about the level of manpower and support required, the importance of accurate well model tuning, and the value that a detailed compressor model can add to a system highly dependent on compressor efficiency. Challenges associated with the gas lift control systems, which are nearing obsolescence, were also identified and created a need for alternative strategies depending on the length of time that the gas lift rate reallocation would be in effect. The full field optimization process utilizes an integrated approach to address operational challenges. A team of engineers and operations personnel now proactively manages events based on a well-defined strategy. The optimization model has allowed gas lift reallocations to be performed on a more frequent basis with less manpower. Based on these reallocations, production increases have been realized and the fields are currently operating at the historically lowest separator pressures. Offline studies have been performed to recommend process equipment modifications and justify major equipment overhauls. The integrated network model has also been used as a predictive tool to forecast the impact of ambient conditions and scheduled maintenance on production rates. The results are currently being monitored to determine the value of adding a fully automated interface to the system model software package.