New method of calculating formation parameters of low permeability gas reservoir

The data of modified isochronal testing of gas well is just used to calculate gas well deliverability. Fully utilizing well test data make it possible to obtain formation parameters, such as gas well deliverability, effective permeability and skin factor at the same time. Based on transient flow theory, the pressure drawdown equation of gas unsteady seepage can be deducted. One simulated case is used to illustrate the applicability of the proposed method. The result of analyzed case shows that the proposed method can provide accurate estimate of formation permeability and skin factor compared with the method of Homer curves.

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.

The Structure of the Galactic Halo

We used the star counts in 21 BATC fields obtained with the National Astronomical Observatories （NAOC） 60/90 cm Schmidt Telescope to study the structure of the Galactic halo. Adopting a de Vaucouleurs r1/4 law halo, we found that the halo is somewhat flatter （c/a - 0.4） towards the Galactic center than in the anticentre and antirotation direction （c/a 〉 0.4）. We also notice that the axial ratios are smaller （flatter） towards the low latitude fields than the high latitude fields, except for a few fields. We provide robust limits on the large-scale flattening of the halo. Our analysis shows that the axial ratio of the halo may vary with distance and the observation direction. At large Galactocentric radii, the halo may not have a smooth density distribution, but rather, it may be largely composed of overlapping streams or substructures, which provides a support for the hybrid formation model.