Proxy Modeling in Production Optimization

Abstract Proxy models are becoming more widely used as they can simplify highly complex processes with reasonable accuracy. Especially in risk analysis, where complex relationships between the uncertainty parameters exist, proxy models are used in form of response surfaces to accelerate interpretation and optimization methods. However, the use of proxy models is rarely seen in production optimization. When the data gathering from wells and surface equipment is fully automated, production optimization can be performed almost in real-time. The bottleneck in this workflow is the high computational effort of simulation models and the large number of input variables to optimize. This disadvantage can be overcome by mimicking the behavior of the system, such as the coupling of a simulation model and the surface network model, by using a computational efficient method. The requirements for such proxy models are high, since they have to capture highly non-linear trends hidden in a small number of representative samples. This paper presents the usage of neural networks as proxy models. For the production optimization process, genetic algorithms are used. Their advantage lies in the ability to handle a large number of input variables. The neural network operates as fitness function for the genetic algorithm. The optimization result can be achieved extremely fast (within seconds), allowing optimization in near real time. A real life example is also presented in this work.