An overview of the geology and production of the Fuling shale gas field,Sichuan Basin,China

The Fuling shale gas field in China is the largest shale gas field as well as the largest of its type discovered in any Lower Paleozoic formation.In this study,the geology and production of the upper and lower gas layers in the Fuling shale gas field are evaluated in terms of structure,shale quality,fault,initial production,and estimated ultimate recovery(EUR).The shale in the lower gas layer of the Jiaoshiba anticline is a high-quality reservoir,where the space is dominated by organic pores in kerogen,and the gas content is high.The shale gas wells reveal relatively high initial production and EUR.However,the shale in the upper gas layer of the Jiaoshiba anticline has reservoir space mainly composed of clay mineral pores and organic pores within bitumen,and the gas content is low.In terms of structure,primary gas migration may occur in the upper gas layer,resulting in free gas accumulation in the structural high,where the development effects are generally better than those in the structural low.The lower gas layer in the Pingqiao anticline,is the main interval for shale gas accumulation and development due to the high-quality shale.Under the influence of faults,the efficiency of exploration wells emplaced on top of the anticline is much lower a compared with those in the flanks.The residual synclines close to the Sichuan Basin,including the Baima and Baitao anticlines,are characterized by more recent uplifts,larger area,greater distance from the deep and large faults,and early fracture closure.Therefore,we recommend that the shale gas exploration and development should be carried out preferentially in areas close to the center of the residual synclines,featuring relatively high-pressure coefficient and moderate burial depth.

Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews

The increasing Chinese online reviews contain rich product demand information,especially for search products.This study suggests a product feature extraction model from online reviews based on multi-feature fusion named PFEMF(products features extraction based on multi-feature fusion)model.Combining sentence and word characteristics of Chinese online reviews,the model explores the lexical features,frequency features,span features,and semantic similarity features of words.And then,they are fused to identify the features that customers are concerned about most by sequential relationship analysis.The identified product feature provides direction for product innovation and facilitates the product selection for customers.Finally,the study takes iPad Air as an example to prove this model.The results show that the extraction performance of the PFEMF model is superior to the traditional term frequency-inverse document frequency(tf-idf)algorithm,word span algorithm,and semantic similarity algorithm.

Collaborative Design in PDM / 3D CAD Integrated Environment

Some key issues in supporting collaborative design in product data management（PDM ） system and 3D computer aided design（CAD） system integrated environment are analyzed. The general architecture of the integrated environment is divided into five tiers and employs the transparently integrated mode, with the mode, function calling and information exchanging among independent PDM and CAD processes are carried out via message translation /parse approach. Product layout feature（PLF ） model definition is presented, PLF model is used to represent design intention at the preliminary design phase. The collaborative design methodology employing the PLF model in PDM/3D CAD integrated environment is analyzed. The design methodology can speed up the design process, reduce the investment and improve the product quality.

Exploiting Consumer Reviews for Product Feature Ranking

Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.

TV column packaging design analysis

Nowadays, as the increasing marketization of dissemination medium, the competition between the dissemination medium gets hot up. The social television has now entered a new period in which the central task is to compete. ＂Brand＂ ＂marketing strategies becomes more important. Brand columns are important. It is to the television channel what product to enterprise. Famous-brand column brings famous-brand column and social benefit. As well as building and maintaining a good brand column needs high quality program content and high standard program form. As well as exquisite column decoration, thereby, TV column packaging became essential.

Generating Domain-Specific Affective Ontology from Chinese Reviews for Sentiment Analysis

Considering the diversities and ambiguities of opinion expressions in Chinese online product reviews,normal sentiment analysis technologies have exposed their inadequateness in both classification accuracy and identifying effectiveness.We propose a novel approach which can easily identify product features and corresponding opinions by building a domain-specific affective ontology and thus mapping comment sentences to the objects defined in the affective ontology.Ontology is created automatically by processing the online reviews;both product features and affective words are presented as nodes which are connected to each other by their semantic relationship.Furthermore,in order to increase the accuracy,we introduce a dynamic polarity detection technique for affective words whose sentimental tendencies are dependent on particular contexts.The experimental results clearly demonstrate the performance improvement of our approach compared with others in real world online product reviews for classification tests.