Lacustrine shale oil is the hottest recent topic in the main basins of China. To better understand the reservoir controlling factors, operators acquire borehole images, nuclear magnetic resonance (NMR), and geochemical logs, which help them identify the sweet spots in shale oil formations. These reveal that that the layering textures have a close relation to key reservoir parameters, such as porosity, and total organic carbon (TOC), etc.

The objective of this study is to develop and apply a "layer intensity index" method to the lacustrine shale oil formations of Sichuan Basin. To do this, we developed a quantitative estimation workflow based on borehole image processing, and obtained a "layer intensity index" after image flattening, binary processing, and horizontal edge detection on resistivity image logs. With calibrations of core data, the layer intensity index can reflect the degree of layer development in the shale oil formation. Based on this parameter, the shale oil formation could be classified into three categories based on this parameter: highly layered zone, medium layered zone, and massive zone.

In this study, the typical shale oil zones are identified based on the integration of the layer intensity index and other key petrophysical parameters. A good shale oil zone has relatively high layer intensity, TOC, porosity, and clay content. In this study we found that the layer intensity index is well-correlated with the effective porosity (PHIE) and TOC. Finally, the high TOC, high effective porosity, and highly layered pay zone is recognized as sweet spots of the shale oil reservoir. The evaluation result was then provided to the operator for horizontal well drilling design.

The layer intensity index was obtained using an innovative process workflow in the lacustrine shale oil formation of the Sichuan Basin. This quantitative layer intensity analysis has enriched the application of resistivity image logs and could be widely used in other similar shale oil reservoirs.

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