This paper discussed the potential relationship between the productive capacity and the logging response of a deeply buried, gas-bearing and tight sandy gravel formation in the Daqing oilfield,and probed the method to predict the productivity of the gas-bearing formation employing the neural network technique based on the logging data.

Use of the resistivity curve for identification of oil and gas water beds in the compact conglomerate reservoir is always a headache in logging interpretation owing to the limitation of the measuring principle of conventional electric logging method.

Analysis on main controlling factors of physical property of tight sandstone reservoir;

Application of temporary and shielding plugging technology to improve well logging responses for tight sandstone gas reservoir;

Current status and prospect of key techniques for exploration and production of tight sandstone gas reservoirs in China;

Four different methods,including support vector machine(SVM),artificial neural network(ANN),multiple regression analysis(MRA) and parameter product decision(PPD),were applied to the gassiness evaluation of forty gas-bearing layers in the tight sandstones of Tabamiao Area in Ordos Basin.

Xujiahe Formation in the south of west Sichuan is composed of tight sandstones with unconventional extra-low porosity and permeability.

Distinguishing gas-bearing formation from compact sandstone with pattern recognition;

Using NMR information toevaluate the tight sand gas reservoir;

Pore structure of tight sand reservoirs with low porosity and low permeability is an important factor influencing the storage capacity and recovery ratio, thus study of pore structural features is of great significance.

Using the computer modeling method,research has been done on the relations between the width and length of the fractures in tight sand,and the positive differential pressures in boreholes.