This paper presents a methodology to evaluate the degree of uncertainty of basement fracture reservoir performances when a reservoir has special events like water-breakthrough. Basement fracture reservoir is a particular feature with water-cut performances compared to other common reservoirs due to extremely high fracture permeability and heterogeneity of reservoir properties.Proxy models are used in this paper to estimate production history and uncertainty. They are useful to reduce the uncertainty contained in a simulation model due to unknown parameters such as reservoir permeability, aquifer size and influx rate, and so on. From a real field application of the proposed methodology, we conclude that the uncertainty of estimated ultimate recovery (EUR) can be significantly reduced when we have water-breakthrough data and proxy models can be utilized to extract probabilistic ranges of EUR with high confidence. This paper proposes to use only high quality history matching (HQHM) models to configure neural network for a proxy modeling to estimate the uncertainty of EUR. The proxy model to be extracted from HQHM model will reduce the error of estimation and reveal real uncertainty due to uncertain parameters not from the proxy model itself.

Economical development of marginal fields is one of the key issues for the oil & gas industry. Risk analysis and optimization technique can be applied to ensure the economics of E&P projects in terms of the development scenarios, cost optimization, and production schemes. Depending on the reliability of reservoir data and the easiness of development, a field operator can exploit the whole field at the same time or choose a phased development strategy. Especially for a basement fracture reservoir, which has high uncertainty in connectivity among fractures, it is better to develop and produce oil from more reliable part of reservoir and then extend to the remaining parts. Even through this strategy, it is often necessary to face the difficulty in the determination of the proper development concepts such as a separate wellhead platform or sub-sea completion. The number and locations of wells also should be optimized to increase the net present value of the project. This paper presents a workflow to determine the development feasibility of oil and gas fields under the uncertainty of reservoir characterization using the neural network and Monte-Carlo simulation. In this paper, we focus on the evaluation of risk and uncertainty of a project's economics under the uncertainty with the connectivity of main producing area and the remaining area. Through the application of the proposed method to a real field in Vietnam, we show that the connectivity between adjacent areas in the basement reservoir is critical to estimate ultimate recovery and to evaluate the project economics. The production incremental through the development of the remaining area will be mainly affected by the production of the existing wells depending on the connectivity. From the evaluation of net present values according to the development concept and the schedule, this paper shows that the project's economics can be improved and better bases for decision making can be provided.