Deep learning–assisted elastic parameter prediction from the digital rock images with geophysical constraints

2020 ◽  
Author(s):  
Rongang Cuia ◽  
Danping Cao ◽  
Zhaolin Zhu ◽  
Qiang Liu ◽  
Yan Jia
Keyword(s):  
Deep Learning ◽  
Elastic Parameter ◽  
Digital Rock ◽  
Parameter Prediction

Multi-Parameter Prediction for Steam Turbine Based on Real-Time Data Using Deep Learning Approaches

2021 ◽  
Author(s):  
Lei Sun ◽  
Tianyuan Liu ◽  
Yonghui Xie ◽  
Xinlei Xia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Plant ◽  
Steam Turbine ◽  
Real Time ◽  
Learning Approaches ◽  
Time Data ◽  
Time Parameters ◽  
Process Prediction ◽  
Parameter Prediction

Abstract Accurate and real-time parameters forecasting is of great importance to the turbine control and predictive maintenance which can help the improvement of power system. In this study, deep-learning models including recurrent neural network (RNN) and convolutional neural network (CNN) for multi-parameter prediction are proposed, and are applied to predict real-time parameters of steam turbine based on data from a power plant. Firstly, the prediction results of RNN and CNN models are compared by the overall performance. The two models show good performance on forecasting of six state parameters while RNN performs better. Moreover, the detailed performance on a certain day show that the relative error of two models are both less than 2%. Finally, the influence of model designs including loss function, training size and input time-steps on the performance of RNN model are also explored. The effects of the above parameters on the prediction performance, training and prediction time of the models are studied. The results can provide a reference for model deployment in the power plant. It is convinced that the proposed method has a high potential for dynamic process prediction in actual industrial scenarios through the above research.

Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

2019 ◽  
Vol 31 (12) ◽  
pp. 8561-8581 ◽  
Author(s):  
Zeeshan Tariq ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Elastic Parameter ◽  
Data Driven ◽  
Parameter Prediction

Elastic parameter server load distribution in deep learning clusters

2020 ◽  
Author(s):  
Yangrui Chen ◽  
Yanghua Peng ◽  
Yixin Bao ◽  
Chuan Wu ◽  
Yibo Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Load Distribution ◽  
Elastic Parameter

3D tight sandstone digital rock reconstruction with deep learning

2021 ◽  
Author(s):  
Jiuyu Zhao ◽  
Fuyong Wang ◽  
Jianchao Cai
Keyword(s):  
Deep Learning ◽  
Tight Sandstone ◽  
Digital Rock

scholarly journals Estimation of Rock Physical Parameters Based on Digital Rock Physics Image, Case Study: Blok Cepu Oil Field, Central Java, Indonesia

2018 ◽  
Vol 16 (1) ◽  
pp. 21
Author(s):  
Handoyo Handoyo ◽  
Fatkhan Fatkhan ◽  
Fourier D. E. Latief ◽  
Harnanti Y. Putri
Keyword(s):  
Digital Image ◽  
Digital Simulation ◽  
Rock Physics ◽  
Elastic Parameter ◽  
Oil Field ◽  
P Wave ◽  
Rock Properties ◽  
Physical Parameters ◽  
Digital Rock Physics ◽  
Digital Rock

Modern technique to estimate of the physical properties of rocks can be done by means of digital imagingand numerical simulation, an approach known as digital rock physics (DRP: Digital Rock Physics). Digital rockphysics modeling is useful to understand microstructural parameters of rocks (pores and rock matrks), quite quickly and in detail. In this paper a study was conducted on sandstone reservoir samples in a rock formation. The core of sandstone samples were calculated porosity, permeability, and elasticity parameters in the laboratory. Then performed digital image processing using CT-Scan that utilizes X-ray tomography. The result of digital image is processed and done by calculation of digital simulation to calculate porosity, permeability, and elastic parameter of sandstones. In addition, there are also predictions of p-wave velocity and wave -S using the empirical equations given by Han (1986), Raymer (1990), and Nur (1998). The results of digital simulation (DRP) in this study provide a higher than the calculations in the laboratory. The digital rock physicsmethod (DRP) combined with rock physics modeling can be a practical and rapid method for determining the rock properties of tiny (microscopic) rock fragments

3D Tight Sandstone Digital Rock Reconstruction with Deep Learning

2021 ◽  
Author(s):  
Jiuyu Zhao ◽  
Fuyong Wang ◽  
Jianchao Cai
Keyword(s):  
Deep Learning ◽  
Tight Sandstone ◽  
Digital Rock
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