Abstract
Running simulation models is CPU intensive. In computing expensive tasks such as parameter screening, sensitivity and risk analysis (uncertainty analysis) and production optimization, it can be useful to establish a simple surrogate model (proxy model) that mimics the simulation model with regard to a specific target value (for example, total production) in order to reduce the computing time and to study the available uncertainties in the reservoir and their impacts on production.
 Artificial Neural Networks (ANN) are one of the main tools used in machine learning. The quality of the ANN as a proxy model is dependent on how the experiments that were used to make and train it are designed. In particular, it is crucial to understand the input parameters such that their respective dependencies, correlations, and ranges are incorporated in the modelling. A combination of simulation runs should be set up that can be used to train the ANN. This task is usually referred to as the design of experiments (DOE) which gives the most informative data sets to train ANN.
 In this study DOE was used to train the ANN in an oil reservoir under gas injection scenario and the trained ANN, in turn, was applied to create the production profiles which were further used for risk analysis.
 The accuracy of the results obtained in this study indicates that ANN as a proxy model combined with DOE as a sampling method for training purpose is a fast and reliable tool that can replace the simulator. This dynamic proxy model can be used for risk analysis, production optimization and production forecasting of oil reservoirs under Enhanced Oil Recovery (EOR) methods.
Highlights
Petroleum reservoir management is a dynamic process that recognizes the uncertainties in the reservoir performance resulting from our inability to fully characterize reservoirs and flow processes
A proxy model which is an analytical model can be developed and trained to give the same results as simulator and the simulator will be replaced by this proxy model and it can be used for sensitivity analysis, risk analysis, optimization and prediction
Sensitivity analysis indicated that pore volume multiplier, transmissibility multiplier in Z direction, gas injection rate and critical gas saturation were the most influential uncertain parameters on total oil production
Summary
Petroleum reservoir management is a dynamic process that recognizes the uncertainties in the reservoir performance resulting from our inability to fully characterize reservoirs and flow processes. It seeks to mitigate the effects of these uncertainties by optimizing reservoir performance through a systematic application of integrated, multidisciplinary technologies. A simulation case is put together by a huge number of input parameters. Nasser Alizadeh likely to influence the results of interest The step in assessing uncertainty is to find out the impacts of the most influential uncertain parameters on simulated results, which is called risk analysis. A proxy model which is an analytical model can be developed and trained to give the same results as simulator and the simulator will be replaced by this proxy model and it can be used for sensitivity analysis, risk analysis, optimization and prediction
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