ARTIFICIAL NEURAL NETWORK WITH EXPERIMENTAL DESIGN: A FAST TOOL FOR RISK ANALYSIS AND FORECASTING IN AN OIL RESERVOIR, A CASE STUDY
Keywords:Proxy Modelling, Artificial Neural Network, Uncertainty Analysis, Design of Experiment, Immiscible Gas Injection
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.
Birang, Y., Dinarvand, N., Shariatpanahi, S. F., Edalat, M. 2007. Development of a new artificial-neural-network model for predicting minimum miscibility pressure in hydrocarbon gas injection. SPE 105407, Middle East Oil &Gas Show and Conference, Bahrain, March 11-14.
Demuth, H., Beale, M. 2002. Neural network toolbox user’s guide. The MathWorks, Inc.
Du, Y., Weiss, W. W., Xu, J., Balch, R. S., Li, D. 2003. Obtain an optimum artificial neural network model for reservoir studies. SPE 84445, Annual Technical Conference and Exhibition, Colorado, October 5-8.
He, Z., Yang, L., Yen, J., Wu, C. 2001. Neural-network approach to predict well performance using available field data. SPE 68801, Western Regional Meeting, March 26-30.
Huang, Y. F., Huang, G. H., Dong, M. Z., Feng, G. M. 2003. Development of an artificial neural network model for predicting minimum miscibility pressure in CO2 flooding. Journal of petroleum science and engineering 37, 83-95.
Jalali, J., Mohaghegh, S. D. 2009. Reservoir simulation and uncertainty analysis of enhanced CBM production using artificial neural networks. SPE 125959, Eastern Regional Meeting, West Virginia, September 23–25.
Kabir, C. S., Chawathe, A., Jenkins, S. D., Olayomi, A. J., Aigbe, C., Faparusi, D. B. 2002. Developing new fields using probabilistic reservoir forecasting. SPE 77564, Annual Technical Conference and Exhibition, Texas, 29 September-2 October.
Lechner, J. P., AG, O., Zangl, G. 2005. Treating uncertainties in reservoir performance prediction with neural networks. SPE 94357, Europec/EAGE Annual Conference, Spain, June 13-16.
Parada Minakowski, C. H. 2008. An artificial neural network based tool-box for screening and designing improved oil recovery methods. Doctoral Thesis, Pennsylvania State University, USA.
Reis, L. C. 2006. Risk analysis with History matching using experimental design or artificial neural networks. SPE 100255, Europec / EAGE Annual Conference and Exhibition, Austria, June 12-15.
Steppan, D. D., Werner, J., Yeate, R. P. 1998. Essential Regression and Experimental Design for Chemists and Engineers.
White, C. D., Royer, S. A. 2003. Experimental design as a framework for reservoir studies. SPE 79676, Reservoir Simulation Symposium, Texas, February 3-5.