A scenario management platform that incorporates statistic and simulation for unconventional field development

Producing from shale formations has been made profitable because of technological advancements. However, the complexity and uncertainties of the unconventional reservoir make it hard to estimate the assets and maximize the value. Reservoir simulation is a powerful tool to estimate the performance of reservoir but calibrating models and optimizing the development plan can take lots of human efforts and computation time, especially when we need multiple models to stress the uncertainties. Many of methods have been developed to improve the efficiency of simulations and reduce the number of simulations needed. There are also analytical packages to help understand the results of simulations from the statistical point of view and build economic models. Thus, an efficient way to incorporate the necessary tools and methods from different sources can be helpful for the decision-making process. The designed scenario management platform can help to understand the uncertainties and to make decisions by analyzing the possible scenarios and correlated data. Connected by the data structure management system, the system is equipped with four primary modules, sampling, modeling, calculation interfaces, and visualization tools. The modules can work separately to carry out works like a predictive statistical model, lunch a batch of simulation according to the template and uncertainties, sampling improve the model or according to a distribution, access the model and presenting results. They can also be used together to do more comprehensive work like history matching and well spacing. This thesis presents a few of the technics that are implemented in this platform that can be helpful to understand the uncertainties. We also show some of the applications enabled by the modules of this system and some of the visualization ideas to diagnose the models.