The array laterolog is an important tool for complex formation logging evaluation due to its high resolution and large detection depth.However,its logging responses are seriously affected by leakage events due to the ...The array laterolog is an important tool for complex formation logging evaluation due to its high resolution and large detection depth.However,its logging responses are seriously affected by leakage events due to the surrounding rock and by mud invasion.These factors must be considered when inverting array lateral logging data,so that the inversion results reflect the true formation conditions as much as possible.The difficulties encountered in the inversion of array lateral logging data are:too many inversion parameters cause the calculation of the Jacobian matrix to be difficult and the time required to select the initial inversion values due to the slow forward-modeling speed.In this paper,we develop a fast processing method for array laterolog data.First,it is important to clearly define the main controlling factors for the array laterolog response,such as thickness,the surrounding rock,and invasion.Second,based on a depth-window technique,processing the array laterolog data for the entire well is transformed into multiple 2 D inversions of the layers using a series of continuous depth windows.For each formation in a depth window,combined with the1 D equivalent fast-forward algorithm,rapid extraction of the radial resistivity profile of the formation is achieved.Finally,the 1 D inversion result is used as the initial state to further eliminate the influence of surrounding rocks and layer thicknesses on the apparent resistivity response.Numerical simulation results show that the factors affecting the response of the array laterolog are the invasion properties,the layer thicknesses,and the surrounding rocks;the windowing technique greatly reduces the number of inversion parameters needed and improves the inversion speed.A real application of the method shows that 2 D inversion can rapidly reconstruct the actual resistivity distribution and improve the accuracy of reservoir saturation calculations.展开更多
While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal proces...While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.展开更多
基金supported by the National Science and Technology Major Project of China(NO.2017ZX05005-005-005,NO.2016ZX05014-002-001 and No.2016ZX05002-005-001)the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA14010204
文摘The array laterolog is an important tool for complex formation logging evaluation due to its high resolution and large detection depth.However,its logging responses are seriously affected by leakage events due to the surrounding rock and by mud invasion.These factors must be considered when inverting array lateral logging data,so that the inversion results reflect the true formation conditions as much as possible.The difficulties encountered in the inversion of array lateral logging data are:too many inversion parameters cause the calculation of the Jacobian matrix to be difficult and the time required to select the initial inversion values due to the slow forward-modeling speed.In this paper,we develop a fast processing method for array laterolog data.First,it is important to clearly define the main controlling factors for the array laterolog response,such as thickness,the surrounding rock,and invasion.Second,based on a depth-window technique,processing the array laterolog data for the entire well is transformed into multiple 2 D inversions of the layers using a series of continuous depth windows.For each formation in a depth window,combined with the1 D equivalent fast-forward algorithm,rapid extraction of the radial resistivity profile of the formation is achieved.Finally,the 1 D inversion result is used as the initial state to further eliminate the influence of surrounding rocks and layer thicknesses on the apparent resistivity response.Numerical simulation results show that the factors affecting the response of the array laterolog are the invasion properties,the layer thicknesses,and the surrounding rocks;the windowing technique greatly reduces the number of inversion parameters needed and improves the inversion speed.A real application of the method shows that 2 D inversion can rapidly reconstruct the actual resistivity distribution and improve the accuracy of reservoir saturation calculations.
基金partially supported by the Research Grants Council of the Hong Kong SAR, China (Project CUHK 415712)the Ministry of Education Academic Research Fund (AcRF) Tier 2 in Singapore under Grant No. T208B1218
文摘While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.
