GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Computer Network Information Center, Chinese Academy of Sciences  (1)
Material
Publisher
  • Computer Network Information Center, Chinese Academy of Sciences  (1)
Language
Years
  • 1
    In: China Scientific Data, Computer Network Information Center, Chinese Academy of Sciences, Vol. 8, No. 1 ( 2023-3-31)
    Abstract: The abundance of widely-developed carbonate rocks of the Leikoupo Formation in Central Sichuan Basin shows good exploration prospects and great exploration potential. Carbonate reservoirs in central Sichuan Basin are well formed, and various pores are well developed and preserved. Supergene dissolution, buried dissolution, and dolomitization have improved reservoir physical properties and is conducive to the reservoir permeability. Pore types including intragranular dissolved pores, intergranular dissolved pores, mold pores, etc. brings out connected dissolution fractures. At the top, a good paleoweathering crust reservoir is formed under karstification. By collecting the strata of Leikoupo Formation encountered in Well LG 19, Well LG 173, Well ZT 1, Well TN 112, Well Y 105, and Well PL 19 in the Central Sichuan Basin as the research object, we produced a dataset of rock micro thin section data. Leikoupo Formation in the Central Sichuan Basin is mainly composed of algal laminar dolomite, sandy dolomite, and breccia dolomite. The sedimentary facies are carbonate evaporation limited platform facies. The subfacies can be divided into tidal flat and intra platform beach subfacies. The tidal flat has microfacies such as cloud flat and gypsum cloud flat. And the intra platform beach can be subdivided into bioclastic beach and sandy beach. This dataset can be used to divide the sedimentary microfacies of the Leikoupo Formation in this area, study the control of the formation and distribution of micro relative reservoirs, determine the favorable reservoir microfacies, and provide effective guidance for oil and gas exploration and development. The image dataset can further provide basic data support for the production of machine learning image dataset.
    Type of Medium: Online Resource
    ISSN: 2096-2223
    Language: Unknown
    Publisher: Computer Network Information Center, Chinese Academy of Sciences
    Publication Date: 2023
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...