Selection of floating platforms is influenced by many factors, such as water depth, risers, capacity of production, experience of operators, etc. Choosing floating platforms for a specific oil/gas field in deepwater must be based on the consideration of particular condition of that field. This paper investigates the application of floating platforms (including Spar, TLP, SEMI and FPSO) in deep water developments all around the world, and selects nine of the influencing factors to build a model for deepwater floating platforms selection by a method of BP (Back-propagation) Artificial Neural Networks (ANN) improved using the L-M algorithm. This prediction model was applied in West Africa oil/gas Fields Egina. The result shows that improved prediction model for deepwater floating platform selection using L-M algorithm has a high convergence speed and good accuracy. In addition, the prediction result of this model for Egina is similar to that of analysis according to experience. Also, analysis for causes of errors in mathematical model lays the groundwork for model optimizing.
Development of oil/gas fields in deepwater divides into three aspects, including surface floating platform, subsea production system and gas/oil transportation. Although it is a global trend to use full functional subsea production systems without any surface structure for deepwater offshore field developments, floating platforms are now most widely used because many critical technologies in subsea production system remain immature. Selection of floating platforms is a determinative factor in planning a good development scheme for oil/gas fields in deepwater. A wide array of platform types is available, and each type has its specific application in deepwater oil/gas fields. And nine factors that influence the selection of floating platforms are considered. Although some errors exist in this model, it provides a quantitative method that is more scientific than methods based on experience.
