1. School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan
063000, China
2. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083,
China
3. Petroleum Exploration & Production Research Institute of Northwest Oilfi eld Company Sinopec, Urumqi 830011, China
Abstract：
The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs. The tidal flat zone in the Shunbei area is currently in the detailed exploration stage, requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment. However, using a single method for sand body prediction has yielded poor results. Seismic facies analysis can effectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fi ne details.In contrast, single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends. Therefore, this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis, using the lower section of the Kepingtage Formation in the Shunbei area as a case study. First, seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies, while principal component analysis (PCA) was applied to improve the depiction of fi ne sand body details from seismic attributes. Based on 3D seismic data, well-logging data, and geological interpretation results, a detailed structural interpretation was performed to establish a high-precision stratigraphic framework, thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies. Subsequently, core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars. Next, PCA was employed to select the seismic attributes most sensitive to sand bodies in different sedimentary facies. Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies. A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identification, effectively capturing their contours and shapes. This method, which integrates seismic facies, single-well sedimentary microfacies, and machine learning techniques, enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.
基金资助:This work was supported by the Collaborative Project Grant from the Exploration and Development Research Institute of SINOPEC Northwest Oilfield Company (Grant No. KY2021-S-104).
作者简介: Corresponding Author: Dr. Ruizhao Yang, Professor and Ph.D. advisor, currently serves in the School of Geosciences and Surveying Engineering at China University of Mining and Technology (Beijing). His research areas include integrated studies in oil and gas exploration and development, high-resolution seismic research in coalbed methane and coal fields, and the exploration and development of unconventional resources. Email: yrz@cumtb.edu.cn
2025, Vol. 22 
