Liyuan Xin^1,2,3, Xiang Rao^1,2,3,*, Xiaoyin Peng^1,2,3, Yunfeng Xu^1,2,3, Jiating Chen^1,2,3

Energy Engineering, Vol.119, No.5, pp. 1905-1922, 2022, DOI:10.32604/ee.2022.019556

Abstract The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs. Considering that the construction of geological models with traditional numerical simulation software is complicated, the computational efficiency of the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the process of model optimization, machine learning methods have been used for fast reservoir simulation. However, traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex network model. This paper aims to predict the production performance of… More >

Lujie Chen¹, Liang Zheng¹, Maosen Lian¹, Shouhua Luo^1,*

Molecular & Cellular Biomechanics, Vol.17, No.2, pp. 85-92, 2020, DOI: 10.32604/mcb.2019.07386

Abstract Micro-CT provides a high-resolution 3D imaging of micro-architecture in a non-invasive way, which becomes a significant tool in biomedical research and preclinical applications. Due to the limited power of micro-focus X-ray tube, photon starving occurs and noise is inevitable for the projection images, resulting in the degradation of spatial resolution, contrast and image details. In this paper, we propose a C-GAN (Conditional Generative Adversarial Nets) denoising algorithm in projection domain for Micro-CT imaging. The noise statistic property is utilized directly and a novel variance loss is developed to suppress the blurry effects during denoising procedure. Conditional Generative Adversarial Networks (C-GAN)… More >