Abstract
With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.
Highlights
Reservoir heterogeneity is one of the important characteristics in reservoir description, which has a significant impact on fluid flow and oil recovery
In the process of model training, the principle of the back propagation algorithm is basing on the chain rule, and the number of hidden layers is related to the complexity of the derivation in the chain rule
Other parameters are fixed, and the AARD value calculated by the model is used to observe the influence of different layers in the hidden layer on the prediction accuracy
Summary
Reservoir heterogeneity is one of the important characteristics in reservoir description, which has a significant impact on fluid flow and oil recovery. It provides fundamental information to build reliable reservoir models and plays a crucial role in reservoir development, especially the vertical heterogeneity, which is a key element in predicting the distribution of remaining oil in the reservoir. Permeability as a primary property for characterizing heterogeneity can show the fluids’ ability of flowing underground when subjected to applied pressure gradients. Different types of data that can be directly obtained in oil fields have been studied and utilized to predict vertical permeability in reservoir. Deutsch [1] presented a consistent numerical modelling framework to obtain the vertical permeability basing on core data, conventional well logs, highresolution image logs, and detailed geological interpretation. Russell et al [2] utilized the existing HighResolution Dipmeter Tool, Formation MicroScanner, and conventional log data to characterize and extrapolate geological heterogeneity.
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