 The Quantification of Uncertainties in Production Prediction Using Integrated Statistical and Neural Network Approaches: An Iranian Gas Field Case Study

Uncertainty in production prediction has been subject to numerous investigations. Geological and reservoir engineering data comprise a huge number of data entries to the simulation models. Thus, uncertainty of these data can largely affect the reliability of the simulation model. Due to these reasons, it is worthy to present the desired quantity with a probability distribution instead of a single sharp value. For the case-study, numbers of parameters which are believed to contribute largely the uncertainty of Field Gas Production Total are recognized. A sensitivity analysis was done to find the most significant initial parameters. Screening experiments are designed in order to recognize the main factors and the significant interactions of factors that we need to certainly include in the response function. Later, experiments of response surface are designed objective to model the response surface function of Field Gas Production Total. This has been done based on applying two methods, Response Surface Methodology and Artificial Neural Networks. The probability distribution of Total Field Gas Production was then plotted using Monte Carlo simulation.