Model Based Production Optimization Applied to Offshore Fields

Abstract

Abstract The search for improvements in the production efficiency and operational cost is one of the main challenges for the production engineers responsible for an asset, mainly when the asset is located in a Brown Field. The use of a decision support system based in mathematical optimization can help the engineers to maximize the production satisfying constraints imposed by the reservoir, wells, pipelines and platform process plant. Furthermore, the efficient use of the available resources promoted by this kind of tool can also be responsible for a decrease in the operational cost. This decision support system is also very important during the occurrence of an unexpected operational problem, allowing the engineers to evaluate a huge number of scenarios in a short period of time, and resulting in an increase of the decision quality and a decrease in the time of the decision making process. In addition, the possibility to estimate the impact of the availability of critical process equipments (Ex.: compressors, separators, etc) in the total oil and gas production of the platform can add value to the integrated planning activities. This paper will report the development of a decision support system (called BR-SiOP) based on mathematical optimization and designed to support the engineers in the production optimization process. Currently, the system has been successfully used by engineers from three different offshore fields located in Campos Basin. Some real cases will also be presented aiming to demonstrate the benefits of the tool. Introduction The production of an offshore asset involves decisions that are made by different groups and at different time scales (Campos and Teixeira, 2011)(Bieker, et al., 2007). Based in the concept of integrated operation management (named GIOp at Petrobras), decisions are made in three different loops that are classified according to the time horizon involved in the decision making process: fast loop, medium loop and long loop. The decisions from the fast loop are usually made in the platform or by a support team located onshore. These decisions are usually related with production monitoring and control activities or with integrated planning. The main objective of the decisions made in this loop is to keep the process stability and safety, as well as the operational continuity. The medium loop decisions are usually related with the production optimization process and are made by the production engineers located onshore. Decisions made in this loop are usually aiming to increase platform production and efficiency, reduce operational cost and production losses. The long loop involves long term decisions related with reservoir management and platform logistics. Decisions made in this loop are usually aiming to increase reservoir recovery factor or optimize the use of the resources. Production engineers usually take into consideration many things during the decision making process. The measured data is used to understand process behavior, identify opportunities for improvements and calibrate simulation models. His experience, that could be defined as the knowledge or skills gained by the engineer across the years that he worked in the asset, is fundamental to help him to understand the asset problems and evaluate the alternatives to solve them. The simulation model is used to predict process behavior and evaluate the impact of changes in the operational conditions. These three things (data, experience and model) are traditionally used by the engineer during the decision making process. The inclusion of a decision support system, based in mathematical optimization, in the decision making process can bring many benefits like:Increase the robustness and reliability of the decision making process because it become less dependent of the experience of the decisor;Reduce the time of the decision making process;Fast response to abnormal situations;