首页|Application of deep learning on well-test interpretation for identifying pressure behavior and characterizing reservoirs
Application of deep learning on well-test interpretation for identifying pressure behavior and characterizing reservoirs
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NSTL
Pressure transient well test analysis is an important tool for identifying reservoir characteristics.However,the reliability of the results from well test analysis could be uncertain due to the analysts'lack of experience.This study aims to apply one-dimensional convolutional neural networks(ID CNN)and build an automatic interpretation model of well test data.The model can automatically identify not only the curve type but also the associated parameters.We integrate this automatic interpretation model with four classic well test models,with no model architecture adjustment and hyper-parameters.We validate the results that the curve classification accuracy reaches 97 %,and the median relative error of the curve parameter inversion is approximate 10 %.In addition,the performance of ID CNN is compared to the artificial neural network(ANN)and two-dimensional convolutional neural networks(2D CNN).Results show that the ID CNN has a faster training speed and has better accuracy in parameter inversion than ANN and 2D CNN.Finally,the automatic interpretation model is further validated with three field cases.
Well testingOne-dimensional convolutional neural networkAutomatic interpretationType identificationParameter evaluation
Peng Dong、Zhiming Chen、Xinwei Liao
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State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum at Beijing,China
NSTL
