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

In this study, three-dimensional (3-D) geostatistical models were constructed to quantify distributions of sandstone and mudstone. We propose a new method that employs weight coefficients to balance the sandstone and mudstone data from irregular well patterns during stochastic modeling. This new method begins with classifying well groups according to well distribution patterns; areas with similar well distribution patterns are classified within the same zone. Then, the distributions of sandstone and mudstone for each zone are simulated separately using the sequential indicator simulation (SIS) method, and the relevant variogram parameters for each zone are computed. In this paper, we used block S6 of the Sulige Gas Field in Ordos Basin in China as a case study. We evaluated the quality of each set of parameters through the vacuation checking method; certain wells were removed to generate equiprobable realizations using different seed numbers. Subsequently, the variogram parameters for the entire S6 area were obtained by assigning different weight coefficients to the parameters of each zone. Finally, a quality assessment of the sandstone and mudstone models of the S6 area was conducted using the horizontal wells, which were not involved in the stochastic modeling process. The results show that these variogram parameters, which were calculated using weight coefficients, are reliable

Geostatistical Three-Dimensional Modeling of a Tight Gas Reservoir: A Case Study of Block S6 of the Sulige Gas Field, Ordos Basin, China