Integration of History Matching And Uncertainty Analysis

Abstract

Abstract The most common procedure to perform a production history matching is to start with a base model and modify reservoir and fluid properties to adjust simulation results with the production history of the field. This paper presents an example of a different procedure. The history matching starts with an uncertainty analysis where several possible models are generated and the models that do not reproduce the behaviour of the reservoir are discarded:allowing a faster history matching process;increasing confidence in the process; and,adding uncertainty analysis to the production prediction. The methodology starts with a dynamic sensitivity analysis based on simulation of models where uncertain attributes are tested and compared with a base model. The attributes are then selected and combined in a derivative tree. Several models are evaluated and those that do not match the production and pressure history are discarded, reducing uncertainty in prediction of the behaviour of the reservoir. This methodology was motivated by a reservoir with unexpected behaviour that yielded a difficult history matching when considering the usual procedures. Techniques were developed to analyze reservoir performance and differential pressure maps between zones. New approaches to assess connectivity between zones were used to give alternate structural models to the sensitivity analysis. This methodology may be helpful during the first years of production when uncertainties are still significant and when typical procedures to perform production history matching are unable to solve the problem efficiently. Introduction History matching is one of the most important activities during the development and management of petroleum reservoirs. Matched models are necessary:to ensure reliable production forecasts; and,to increase the confidence in understanding the geological and reservoir models. Petroleum reservoirs are very complex structures and several uncertainties are present in the process promoting low confidence in reservoir simulation models. During the first years of production, uncertainties still play a major role in the process making it more complex and allowing multiple acceptable solutions(1). As production increases, the quantity of data permits better matching but the complexity of the process increases because of the high number of wells and objective functions of the optimization process, which usually are the difference between simulation results and observed data for pressure and each of the production phases. These different levels of uncertainty and procedures, as functions of reservoir development stages, add an extra complexity to the usual procedures(2, 3). Due to these difficulties, this paper presents a different approach. Instead of starting with a base (deterministic) model that is modified to reproduce production history, it is possible to start with an uncertainty analysis, consider several possible realizations of the reservoir model, and then to decrease the level of uncertainty in the production prediction by discarding models that do not satisfy specific conditions. There are several advantages in the proposed methodology: 1) the dynamic behaviour of the history matching is better handled with this approach since previous simulation runs can be used at any moment of the analysis;