Abstract
In the process of oilfield development, it is important to predict the oil and gas production. The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period. Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.
Highlights
In recent years, owing to the persistent instability of international oil prices, shortage of oil and gas resources, high production costs, and other reasons, oilfields have higher requirements for the prediction of oil and gas production
To ensure the authenticity of the data, this paper proposes a model based on a multi-input convolutional neural network AlexNet [8]
Limited by the size of the data, the classic AlexNet convolutional neural network model was proposed for the ImageNet competition [18], which is mainly used for high-resolution and large image recognition
Summary
Received 11 September 2018; Revised 20 November 2018; Accepted 22 November 2018; Published 4 December 2018. In the process of oilfield development, it is important to predict the oil and gas production. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance
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