English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Due to poor physical properties and strong heterogeneity of Daniudi tight gasfield (China), traditional methods are not ideal for predicting reservoir permeability. Based on geoscience data mining algorithms and modeling techniques, this parameter is predicted and characterized from a new perspective. The high precision hydraulic unit information was outputted by the BP neural network and the interwell database was predicted by sequential indicator simulation based on the logging and core data, the exponential and power law relationship functions were selected to participate in the construction of the permeability model. The results showed that the facies-controlled modeling system for permeability based on the hydrodynamic characteristics of tight gas reservoirs could effectively improve the accuracy of reservoir prediction, and the logging information in the longitudinal direction and the facies information in the plane were combined by the hydraulic unit.

Facies-Controlled Modeling for Permeability of Tight Gas Reservoir Based on Hydrodynamic and Geophysics Characteristics