توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Development of an Adaptive Neuro–Fuzzy based Model for Prediction of Minimum Miscibility Pressure
نویسنده(ها): ، ،
اطلاعات انتشار: دومین کنگره مهندسی نفت ایران، سال
تعداد صفحات: ۱۰
In this paper, a neuro–fuzzy hybrid approach was used to construct a CO2 MMP predicting system during design a gas injection project. In particular, we used an adaptive network–based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. In neuro–fuzzy inference system, zero order Sugeno–type inference technique was used to perform approximate reasoning of fuzzy input variables. In addition, hybrid learning algorithm, combining back propagation learning and linear least–squares estimator, was preferred for the adaptation of free parameters. Consequently, neuro–fuzzy model was compared with results obtained using multiple linear regression methodology in addition to other conventional models to make comparison among different techniques. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for MMP forecasting<\div>

۲Minimum miscibility pressure prediction using support vector machines
نویسنده(ها): ، ،
اطلاعات انتشار: دومین کنگره مهندسی نفت ایران، سال
تعداد صفحات: ۱۱
Miscible gas injection processes are among the effective methods for enhanced oil recovery. A key parameter in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from gas injection is highly dependent on the MMP. Because experimental determination of MMP is very expensive and time–consuming, searching for fast and robust mathematical determination of gas–oil MMP is usually requested. This paper introduces Support Vector Machines (SVM), a relatively new powerful machine learning method based on statistical learning theory, into MMP forecasting. The validity of this new model was successfully approved by comparing the model results to the experimental gas–oil MMP and the calculated results for the common gas–oil MMP correlations. The new model yielded the accurate prediction of the experimental gas–oil MMP with the lowest average relative and average absolute error among all tested gas–oil MMP correlations. In addition, the new model could be used for predicting the gas–oil MMP at higher fractions of non–CO2 components<\div>
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