Oil-gas reservoir in the Mesozoic strata in the Chaoshan depression,northern South China Sea:a new insight from long off set seismic data

The Chaoshan depression,a Mesozoic basin in the Dongsha sea area,northern South China Sea,is characterized by well-preserved Mesozoic strata,being good conditions for oil-gas preservation,promising good prospects for oil-gas exploration.However,breakthrough in oil-gas exploration in the Mesozoic strata has not been achieved due to less seismic surveys.New long-off set seismic data were processed that acquired with dense grid with single source and single cable.In addition,the data were processed with 3D imaging method and fi ner processing was performed to highlight the target strata.Combining the new imaging result and other geological information,we conducted integrated interpretation and proposed an exploratory well A-1-1 for potential hydrocarbon.The result provides a reliable basis for achieving breakthroughs in oil and gas exploration in the Mesozoic strata in the northern South China Sea.

A DEMATEL Approach Based on Fuzzy Sets for Evaluating Critical Factors of Gas Turbine in Marine Engineering

In power production,gas turbines are commonly used components that generate high amount of energy depending on size and weight.They function as integral parts of helicopters,aircrafts,trains,ships,electrical generators,and tanks.Notably,many researchers are focusing on the design,operation,and maintenance of gas turbines.The focal point of this paper is a DEMATEL approach based on fuzzy sets,with the attempt to use these fuzzy sets explicitly.Using this approach,the cause–effect diagram of gas turbine failures expressed in the literature is generated and aimed to create a perspective for operators.The results of the study show that,"connecting shaft has been broken between turbine and gear box"selected the most important cause factor and"sufficient pressure fuel does not come for fuel pump"is selected the most important effect factor,according to the experts.

Coal and gas outburst prevention using new high water content cement slurry for injection into the coal seam

As coal and gas outburst is one of the most serious mine disasters, it is very important to at least control it if not prevent it from occurring. Injecting cement slurry or grouting into the coal seam can strengthen the seam, increase its rigidity coefficient(f), and reduce the volumetric expansion due to gas energy release.This paper reports the results of laboratory experiments on cement-based high water content slurry having different water-cement ratios(W/C) to be used for coal injection. The results show that as the W/C increases, the mobility of the slurry and its setting time increase. The compressive strength and rupture strength, however, are reduced. Furthermore, high W/C grout shows early strength after 7 days, which can be 80% of its 14-day compressive strength. To achieve rapid setting and early strength, the addition of Na_2SiO_3has proven to give the best result, when the concentration of the additive is 3%. The initial and final setting times are 13 and 21 min shorter than samples without Na_2SiO_3, while the compressive strength is more than double. As a retarder, the initial setting time can be extended to 83 min when tartaric acid of 0.4% concentration is added. Through the orthogonal experiment, the optimum recipe of the new high water content slurry has been determined to be: W/C = 2, tartaric acid = 0.2%, Na_2SiO_3= 3%, and12% bentonite. Reinforcement by injection simulation experiments show that the grouting radius of the new slurry mix is 250 mm when the applied grouting pressure is 60 k Pa, 7-day rupture strength and compressive strength are 5.2 and 6.4 MPa, respectively, and are 37% and 88% higher than ordinary cement grout. It can be concluded that the newly developed slurry mix is more effective than the ordinary mix for reinforcing coal and controlling gas outburst.

Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.

Transcriptome analysis of the influence of CPPU application for fruit setting on melon volatile content

In fruit production,the application of the plant growth regulator 1-(2-chloro-4-pyridyl)-3-phenylurea(CPPU)dulls the fruit aroma.Gas chromatography-mass spectrometry and transcriptome analyses were performed on CPPU-treated and pollinated fruits to determine how CPPU affects the production of aroma in melon fruit.The results showed that the contents of two important esters(benzyl acetate and phenethyl acetate)in the CPPU-treated fruits were significantly lower than those in the pollinated fruits.Transcriptome sequencing data revealed that most differentially expressed genes were involved in“phenylalanine metabolism”pathway,and their expression was significantly decreased in the CPPU-treated fruits.Further analysis showed that the phenylalanine content in the CPPU-treated fruits was significantly higher than that in the pollinated fruits.In summary,CPPU application interferes with phenylalanine metabolism in melon fruits and affects the production of aromatic esters.

A Generalized Rough Set Modeling Method for Welding Process

Modeling is essential, significant and difficult for the quality and shaping control of arc welding process. A generalized rough set based modeling method was brought forward and a dynamic predictive model for pulsed gas tungsten arc welding (GTAW) was obtained by this modeling method. The results show that this modeling method can well acquire knowledge in welding and satisfy the real life application. In addition, the results of comparison between classic rough set model and back-propagation neural network model respectively are also satisfying.

A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise

In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.

MAXIMAL ATTRACTORS FOR THE COMPRESSIBLE NAVIER-STOKES EQUATIONS OF VISCOUS AND HEAT CONDUCTIVE FLUID

This article is concerned with the existence of maximal attractors in Hi （i = 1, 2, 4） for the compressible Navier-Stokes equations for a polytropic viscous heat conductive ideal gas in bounded annular domains Ωn in Rn（n = 2,3）. One of the important features is that the metric spaces H（1）, H（2）, and H（4） we work with are three incomplete metric spaces, as can be seen from the constraints θ 〉 0 and u 〉 0, with θand u being absolute temperature and specific volume respectively. For any constants δ1, δ2……,δ8 verifying some conditions, a sequence of closed subspaces Hδ（4） H（i） （i = 1, 2, 4） is found, and the existence of maximal （universal） attractors in Hδ（i） （i = 1.2.4） is established.

Application of causality diagram in system safety analysis

Causality Diagram (CD) is a new graphical knowledge representation based on probability theory. The application of this methodology in the safety analysis of the gas explosion in collieries was discussed in this paper, and the Minimal Cut Set, the Minimal Path Set and the Importance were introduced to develop the methodology. These concepts are employed to analyze the influence each event has on the top event ? the gas explosion, so as to find out about the defects of the system and accordingly help to work out the emphasis of the precautionary work and some preventive measures as well. The results of the safety analysis are in accordance with the practical requirements; therefore the preventive measures are certain to work effectively. In brief, according to the research CD is so effective in the safety analysis and the safety assessment that it can be a qualitative and quantitative method to predict the accident as well as offer some effective measures for the investigation, the prevention and the control of the accident.