Characterization of a reservoir ooid shoal complex and Artificial Neural Networks application in lithofacies prediction: Mississippian St. Louis formation, Lakin fields, western Kansas

Keithan G. Martin, Matthew W. Totten, Abdelmoneam Raef*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Residing in the Hugoton embayment of western Kansas, the Lakin field has produced over 1.8 million bbl of oil from reservoir quality zones within the St. Louis Limestone (Mississippian, Meramecian) oolitic deposits. This study focuses on improving the understanding of the orientation, geometry, and spatial distribution of ooid shoal complexes in Kearny County, Kansas. To this end, the interpretation of petrophysical log data integrated with a facies analysis conducted on 400 ft (122 m) of core from three separate intervals provided the basis, in this study, for understanding the vertical stacking patterns and how facies transitions relate to reservoir quality zones within the ooid shoal complexes. A supervised Artificial Neural Network (ANN) has been trained and validated, based on input-layer of geophysical well-logs and an output-layer of core-lithofacies, for lithofacies prediction where core samples were not available. The porous ooid grainstone is the principal reservoir facies, with an average log-derived porosity measurement of eighteen percent. Ooid shoal complexes are present in St. Louis Zone B and C, with thicknesses reaching up to 10 ft (3 m) and 24 ft (7 m) respectively. These complexes are NW-SE trending and laterally extend up to 16 mi (26 km), with economically viable patches covering up to 8 mile (13 km), and record roughly 2 mile (3 km) in width. The apex of the shoal contains the highest porosity values, with significant porosity reduction towards the shoal margins due to the increased cementation. This study provides information on the characteristics of oolitic deposits for the purpose of understanding controls on reservoir heterogeneity to aid in finding additional hydrocarbon reserves in the St. Louis Limestone in western Kansas.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJournal of Petroleum Science and Engineering
Volume150
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Artificial Neural Network (ANN)
  • Hugoton embayment
  • Lakin field
  • Lithofacies
  • Meramecian
  • Oolitic Shoal
  • St Louis limestone
  • Western Kansas

ASJC Scopus subject areas

  • Fuel Technology
  • Geotechnical Engineering and Engineering Geology

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