Abstract
The characteristics of laminae are critical to lacustrine shale strata. They are the keys to the quality of source rocks and reservoirs, as well as engineering operations in shale plays. This study uses organic geochemistry, thin section identification, X-ray diffraction, field emission scanning electron microscopy, and other analytical methods, to reveal the detailed lamination texture and vertical distribution of laminae in the second Member of the Kongdian Formation in Cangdong Sag. The principal results are as follows: (1) A classification of laminae is proposed to characterize reservoir and geochemical properties. The five types of laminae are as follows: feldspar-quartz laminae (FQL), clay laminae (CLL), carbonate laminae (CAL), organic matter laminae (OML), and bioclastic laminae (BCL). There are also four significant lamina combinations (with the increasing TOC values): FQL-CLL combination, FQL-CLL-BCL combination, FQL-CLL-OML combination, and FQL-CAL-CLL-OML combination; (2) differences between laminae occur because of the variability in pore types and structures. There appears to be a greater abundance of intercrystalline pores of clay minerals in the FQL, CAL, BCL, and OML, and well-developed organic pores in the CAL and CLL, and the counterparts of intragranular pores of bioclastic material in the BCL. This detailed characterization provides the following comparative quantification of the thin section porosity of laminae in the second Member of the Kongdian Formation can be differentiated: CAL > FQL > OML > BCL > CLL; (3) differentiation between vertical distributions of laminae is carried out in a single well. The FQL and CLL are widely distributed in all the samples, while the BCL is concentrated in the upper part of the second Member of the Kongdian Formation, and CAL is concentrated in the lower part. This detailed classification method, using geochemical analysis and vertical distribution descriptions, offers a detailed understanding of lamination texture and its effects on reservoir and geochemical properties, which will provide a scientific guidance and technical support to better estimate reservoir quality and to identify new sweet spots in the second Member of the Kongdian Formation in the Cangdong Sag.
Highlights
The feldspar-quartz laminae (FQL) and clay laminae (CLL) are widely distributed in all the samples, while the bioclastic laminae (BCL) is concentrated in the upper part of the second Member of the Kongdian Formation, and carbonate laminae (CAL) is concentrated in the lower part
total organic carbon content (TOC) of the FQL-CLL combination ranges from 2.1% to 4.4%, with an average value of 3.0%; TOC of the FQL-CLL-BCL combination ranges from 1.9% to 5.7%, with an average of 3.4%; TOC of the FQL-CLL-organic matter laminae (OML) combination ranges from 2.0% to 9.3%, with an average of 5.1%; and TOC of the FQL-CAL-CLL-OML
The laminae types and combinations found in the second Member of the Kongdian Formation in the Cangdong Sag, Bohai Bay Basin are analyzed and their effects on reservoir properties determined by an integrated approach, including petrology, mineralogy, organic geochemistry, and reservoir evaluation techniques
Summary
The Chang 73 sub-member in the Ordos Basin was identified as having four major types of laminae: tuff-rich laminae, organic-rich laminae, silt-sized feldspar-quartz laminae, and clay laminae [16]. Another method divided shales in the Yanchang Formation into ‘dark’. Others proposed a four-fold model—thinly-bedded dolomitic shale, lamellar mixed shale, lamellar felsic shale, and bedded dolomitic shale—according to the dominant rock types, rhythmic structures, and logging curve characteristics [29] To clarify this situation, it is vital to carry out a correlated study of the influence of lamination texture on reservoir quality characterization. The conclusions will provide valuable guidance for shale oil exploration and development in the study area but will supplement the geological theory and support the technological development of lacustrine shale oil reservoir evaluation and sweet spots optimization
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