Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.

Study on wave energy resource assessing method based on altimeter data——A case study in Northwest Pacific

Wave energy resource is a very important ocean renewable energy. A reliable assessment of wave energy resources must be performed before they can be exploited. Compared with wave model, altimeter can provide more accurate in situ observations for ocean wave which can be as a novel method for wave energy assessment.The advantage of altimeter data is to provide accurate significant wave height observations for wave. In order to develop characteristic and advantage of altimeter data and apply altimeter data to wave energy assessment, in this study, we established an assessing method for wave energy in local sea area which is dedicated to altimeter data.This method includes three parts including data selection and processing, establishment of evaluation indexes system and criterion of regional division. Then a case study of Northwest Pacific was performed to discuss specific application for this method. The results show that assessing method in this paper can assess reserves and temporal and spatial distribution effectively and provide scientific references for the siting of wave power plants and the design of wave energy convertors.

Pupil apodization for increasing data storage density

We introduce a technique for increasing density in optical data storage systems. This technique is based on the use of a superresolving filter at the pupil of a confocal readout system. The main characteristic of this confocal readout system is that the light beam traverses twice through the pupil filter. We describe how to analyze the system performance for general filters, but we focus the study on filters with no focus displacement. Although the storage density attainable depends on the filter characteristics, we show that the storage density can be easily duplicated.

Mining hourly population dynamics by activity type based on decomposition of sequential snapshot data

The dynamic population distributions by activity type(e.g.working,shopping or in-home)are vital for resource allocation,urban planning and epidemic containment.Although studies have incorporated individual-level human mobility data to map population distribution by activity type,access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity knowledge.This paper presents a method for generating the population dynamics by activity type.We first introduce more readily available sequential snapshot data to construct the population mixture model,then decompose the population mixture,and finally estimate the dynamic population size for each activity.We test the method in the central districts of Guangzhou city,China,based on real-time Tencent user density data.Correlation analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working,stay-at-home,and socializing activities.The temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial patterns.We also find that there is an underlying relationship between a region’s function and its dynamic population structure.The presented method has great potential for application and could provide new insight for studying urban dynamic functions.

RADIAL BASIS FUNCTION INTERPOLATION IN SOBOLEV SPACES AND ITS APPLICATIONS

In this paper we study the method of interpolation by radial basis functions and give some error estimates in Sobolev space H^k（Ω） （k 〉 1）. With a special kind of radial basis function, we construct a basis in H^k（Ω） and derive a meshless method for solving elliptic partial differential equations. We also propose a method for computing the global data density.

Improved Flight Conflict Detection Algorithm Based on Gauss-Hermite Particle Filter

In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.