Granulometric analysis as the main method to justify the formation conditions of reservoirs JK(2-5) in Em-Egovskoe field (Western Siberia) - Lidiya Konstantinovna Kudryashova (Известия Томского политехнического университета: Инжиниринг георесурсов, 2019)
The relevance of the discussed issue is caused by the increasing significance of the recoverable oil reserves in reservoirs of deep horizons. This requires more detailed study of their formation conditions. Granulometric and mineralogical investigations of terrigenous sediments allow obtaining the basic ideas on their sedimentation conditions. In this case, the combination of the existing methods enables to address some issues of facies analysis. The main aim of the study is to define the facies sedimentation environment by granulometric and mineralogical investigations on the example of horizons JK2-5 in Em-Egovskoe area, Krasnoleninskoe field. The methods used in the study: granulometric and mineralogical investigations: generalized description of the environment by Furchtbauer and Muller; definition of deposit genesis by sorting ratio and features of asymmetry (K. Berlikke diagram); determination of sedimentation conditions by asymmetry and excess ratio (G.F. Rozhkov dynamogenetic diagram); identification of the way to transfer clastic particles in aqueous environment (R. Passegi genetic diagram); authigenic mineral composition analysis in rocks. The results. The author proposes a complex use of methods to determine the genesis by the granulometric and mineralogical analyzes according to the certain plan. According to this plan, the precipitation formation conditions were clarified by each subsequent method and this enables to determine more accurately sedimentation formation. Based on granulometric and mineralogical studies, the author made the conclusion that the sediments studied had been formed within the estuary. The results of the study show that the sedimentation conditions may be different under the same dynamic accumulation conditions and similar particle granulometric distributions. The efficiency of the facies prediction can be enhanced only through the integrated analysis.