Development Status of Logging-Based Lithology Identification Technology for Shale Formations

doi:10.7657/XJPG20240614

Abstract

Abstract:

Shale reservoirs contribute the most promising unconventional oil and gas resources in China and have become a hotspot in unconventional oil and gas exploration and development. Shale formations in China are mostly continental, with varying lithologies, diverse minerals, poor physical properties, strong heterogeneity, and poor continuity. These characteristics make it difficult to accurately identify lithology only using conventional logging interpretation methods, which in turn hinders the effective characterization of shale reservoirs and severely constrains reserves estimation and oil/gas development activities. In order to effectively identify the lithology of shale formations, the logging-based lithology identification technologies at home and abroad were systematically reviewed, and the lithology identification technologies based on logging interpretation and logging techniques were introduced. The logging lithology identification technologies based on machine learning were dissected in respect to their principles, advantages, disadvantages, and applicability. Finally, the prospects of logging-based lithology identification technologies for shale formations were proposed.

Key words: shale, logging-based lithology identification, machine learning, logging response, neural network, logging interpretation

CLC Number:

P631.842

CHEN Xiujuan, FENG Zhentao, ZENG Furong, HU Jianbo, XU Song. Development Status of Logging-Based Lithology Identification Technology for Shale Formations[J]. Xinjiang Petroleum Geology, 2024, 45(6): 742-752.

Figures/Tables 6

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