Decision Making Methodology in a Risk Prone Environment - Application to Production Scheme Management

Petroleum field understanding and management is always associated to uncertainties, whose importance varies during all the production period. However, even if Monte-Carlo technique is well known to investigate uncertainty assessment problems, in case of reservoir simulation it is not relevant anymore due to both the high number of simulations required, and the high computation time per simulation. <br><br>In recent years, probabilistic forecasting has gained popularity and has become the preferred approach when assessing the value of a project, given the uncertainty of many input variables. Reservoir understanding and production forecasting may involve as uncertain parameters both continuous uncertain parameters, which vary inside a range of possible values, and discrete parameters to model physical or geological possible scenarios, or options to be evaluated and tested to optimize the field management.<br><br>Experimental design technique has proven its efficiency to assess risk on reservoir performances related to uncertainties on continuous uncertain parameters. On the other hand, decision tree analysis is a widely validated technique, used when a problem involves subsequent decisions, to model nested scenarios and configurations as well as decision options. It is extremely useful for quantifying the impact of each scenario and configuration and to set up the value of each possible decision, finally leading to discriminate the optimal decision among all possible options.<br><br>We present here an integrated approach, which by taking the best of both experimental design techniques and decision tree analysis will allow to manage decision making in an uncertain framework, taking into account risk associated with both technical reservoir uncertain parameters and economical uncertain parameters.<br><br>This methodology has been applied to a reservoir synthetic case to highlight the need to integrate all available sources of uncertainty, both on technical and economical parameters, to avoid sub-optimal decisions in terms of maximizing the economical value of the field.