首页|Application of Rough Neural Network to forecast oil production rate of an oil field in a comparative study
Application of Rough Neural Network to forecast oil production rate of an oil field in a comparative study
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NSTL
As real production data of a well have an irregular pattern, accurate prediction of oil rate demands a powerful model to capture the nonlinear behavior of data. In addition to the traditional methods such as Decline Curve Analysis (DCA), Machine Learning methods can be considered as one of the effective predictive tools of engineers. This paper employs a comparative approach to find an appropriate network to forecast the oil production rate of an Iranian Oil field. The performance of several networks such as Rough Neural Network (Rough NN), Long-Short-Term Memory (LSTM), Artificial Neural Network (ANN) with only dense layer, and ID Convolutional Neural Network (CONV~(-1)D) were monitored by investigating various statistical parameters such as error value, the cross plot of real data and predicted data and error distribution. A combination of five inputs static and dynamic parameters was considered as input to the model. All networks were trained for 80% of all data (10025 points) and the rest were divided equally for testing and validation. The highest performance was observed in the results of Rough-NN with a coefficient of determination of 0.82 for predicting test data. The results showed an accuracy slightly less than Rough-NN for the case of CONV~(-1)D (R2 = 0.79). However, the worst performance referred to ANN and LSTM where their R-squared was about 0.54. Finally, the precision of the superior network (Rough-NN) was compared with DCA for several wells. It was revealed that DCA cannot predict the oil production rate accurately specifically when the well comes into production after a long shut-in. In contrast, Rough-NN is capable of predicting the oil production rate accurately regardless of the production history of the well.
Deep learningMachine learningRough neural networkOil rate forecastingData Driven
NSTL
