Multi-Objective History Matching Allowing for Scale-Difference and the Interwell Complication

This study presents a new multi-objective history matching model to predict the individual well performance. Typical single-objective history matching, reducing the linearly averaged form of different-scaled objectives, has not covered the individual well performance properly. Previous multi-objective history matching, which could demonstrate the individual performance, shows the poor applicability as the number of objective function increases. This work aims to develop the accurate and diversity-preserved methodology to accomplish the global optimization. The scheme consists of dynamic goal programming and successive linear objective reduction incorporated with non-dominated sorting genetic algorithm-II. Dynamic goal programming grants priorities to solutions satisfying the expectation values for the objective functions with goal adjustment. SLOR removes redundant objective functions at the fitness evaluation in genetic algorithm. For the case study of waterflood history matching, the model is less sensitive to the form of objective functions and gridblock size. This study proves that reflecting relativity of different performances is able to improve prediction ability of the conventional single- and multi-objective approaches. The model provides a reliable range of uncertainty from diversity-preserved concept. The developed multi-objective optimization algorithm can easily apply to solve the convergence problem and the unrealistic estimation caused by scale-difference and the complication among multi-objective functions.