Abstract
In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.
Highlights
The Tahe Triassic oil field block 9 reservoir has serious heterogeneity, wide interlayer distributed, and complex connectivity
In this paper, we present a prediction model to predict the oil production from Tahe oil field based on an optimized Random Vector Functional Link (RVFL) network [19]
This experiment was conducted to show the effectiveness of the Spherical Search Optimizer (SSO)-RVFL algorithm in predicting the oil production
Summary
The Tahe Triassic oil field block 9 reservoir has serious heterogeneity, wide interlayer distributed, and complex connectivity. Monteiro et al [16] applied both data analysis and multiphase flow simulation to address uncertainties in oil flow rate forecasting They evaluated the proposed method with production test data of 13 production wells at a representative Brazilian offshore field. In this paper, we present a prediction model to predict the oil production from Tahe oil field based on an optimized Random Vector Functional Link (RVFL) network [19]. The RVFL enhances the training of the neural network by generating the weights of input layer to hidden layer randomly It has been applied in various applications, including prediction problems. Proposed an improved Random Vector Functional Link (RVFL) model, based on a new optimization algorithm called the Spherical Search Optimizer (SSO). The improved model is called SSO-RVFL which is applied for predicting oil production from Tahe oil field in China.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
