总有机碳的地球物理响应——页岩气储层岩石-物理模型新突破

页岩气等非常规资源在油气勘探开发领域变得越来越重要,并成为建产目标。由于对总有机碳(TOC)等储层特征的地球物理响应的识别难题,对非常规储层特征地球物理响应的准确识别是开发页岩气面临的挑战。埃克森美孚公司开发了一套改进的各向异性岩石-物理模型,除了矿物成分、孔隙度、流体等地震响应和电性资料外还包括总有机碳(TOC)的地球物理响应。这个建模结果表明了有机质含量的增加通常会造成P波阻抗的降低,Vp/Vs比率也降低,同时速度的各向异性和电阻上升。

Assembling aggregation-induced emission with natural DNA to maximize donor/acceptor ratio for efficient light-harvesting antennae

Arranging dense donors around a single acceptor for the assembly of effi-cient light-harvesting antennas is a long-standing challenge due to the intractable aggregation-caused quenching of dense donors.Herein,we designed a cationic aggregation-induced emission(AIE)amphiphile to self-assemble with natural DNA duplexes.As an efficient donor,the as-prepared cationic AIE amphiphile could be densely attached to the phosphate groups of natural DNA duplexes by using the smaller cationic trimethylammonium.The long alkyl chain between the cationic trimethylammonium and the AIEfluorophore allowed for avoiding the insuffi-cient binding caused by the steric hindrance of the AIEfluorophore,resulting in a remarkably high donor/acceptor ratio comparable to that of the widely developed custom DNA assemblies.The proposed self-assembly strategy provided novelflex-ible avenues for the assembling offinely controlled and efficient light-harvesting systems into natural DNA with little synthetic modifications and low cost.

Design of Fault Detection Observer Based on Hyper Basis Function

In this paper, we propose the Hyper Basis Function （HBF） neural network on the basis of Radial Basis Function （RBF） neural network. Compared with RBF, HBF neural networks have a more generalized ability with different activation functions. A decision tree algorithm is used to determine the network center. Subsequently, we design an adaptive observer based on HBF neural networks and propose a fault detection and diagnosis method based on the observer for the nonlinear modeling ability of the neural network. Finally, we apply this method to nonlinear systems. The sensitivity and stability of the observer for the failure of the nonlinear systems are proved by simulation, which is beneficial for real-time online fault detection and diagnosis.

Cloud type identification for a landfalling typhoon based on millimeter-wave radar range-height-indicator data

As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.

对页岩气储层地球物理响应的认识

页岩气等非常规资源正逐渐成为重要的勘探开发目标。为认识页岩气储层的地球物理响应,利用受地质和地层评价分析约束的岩石物理关系计算波阻抗和Vp/Vs(纵横波速度比)等有效属性。数值研究表明矿物成分(例如,泥、石英和方解石)和TOC(总有机碳含量)等现场岩石参数以及其之间的相互作用会严重影响富含有机质岩石的地球物理响应,因此,为页岩气储层的地球物理特征提供了依据。