Facies and Petrophysical Modeling of Triassic Chang 6 Tight Sandstone Reservoir, Heshui Oil Field, Ordos Basin, China
Author(s) -
Khawaja Hasnain Iltaf,
Dali Yue,
Wurong Wang,
Wan Xiaolong,
Shixiang Li,
Shenghe Wu,
Ruijing Liu,
Zhan Weijia,
Siraj Mehboob,
Sajjad Ahmad Shah,
Rizwan Sarwar Awan,
Muhammad Usman Tahir
Publication year - 2021
Publication title -
lithosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.737
H-Index - 43
eISSN - 1941-8264
pISSN - 1947-4253
DOI - 10.2113/2021/9230422
Subject(s) - facies , petrophysics , geology , reservoir modeling , geostatistics , petrology , kriging , variogram , structural basin , geomorphology , porosity , petroleum engineering , spatial variability , geotechnical engineering , machine learning , statistics , mathematics , computer science
Tight sandstone reservoirs are widely distributed worldwide. The Upper Triassic Chang 6 member of the Yanchang Formation is characterized by low permeability and porosity. The facies model offers a unique approach for understanding the characteristics of various environments also heterogeneity, scale, and control of physical processes. The role of subsurface facies features and petrophysical properties was unclear. Notable insufficient research has been conducted based on facies and petrophysical modeling and that demands to refine the role of reservoir properties. To tackle this problem, a reservoir model is to be estimated using various combinations of property modeling algorithms for discrete (facies) and continuous (petrophysical) properties. Chang 6 member consists of three main facies, i.e., channel, lobe main body, and lobe margin facies. The current research is aimed at comparing the applicability and competitiveness of various facies and petrophysical modeling methods. Further, well-log data was utilized to interpret unique facies and petrophysical models to better understand the reservoir architecture. Methods for facies modeling include indicator kriging, multiple-point geostatistics, surface-based method, and sequential indicator simulation. Overall, the indicator kriging method preserved the local variability and accuracy, but some facies are smoothed out. The surface-based method showed far better results by showing the ability to reproduce the geometry, extent, connectivity, and facies association. The multiple-point geostatistics (MPG) model accurately presented the facies profiles, contacts, geometry, and geomorphological features. Sequential indicator simulation (SIS) honored the facies spatial distribution and input statistical parameters. The porosity model built using sequential Gaussian simulation (SGS) showed low porosity (74% values <2%). Gaussian random function simulation (GRFS) models showed very low average porosity (8%-10%) and low permeability (less than 0.1 mD). These methods indicate that Chang 6 member is a typical unconventional tight sandstone reservoir with ultralow values of petrophysical properties.
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