Quantitative scale statistics of modern braided rivers- Taking the braided river section of the Yarlung Zangbo River as an example
现代辫状河定量规模统计——以雅鲁藏布江辫状河段为例

摘要

辫状河砂体储层是我国中新生代含油气盆地中重要的陆相碎屑岩储层类型，心滩砂体作为其核心构型单元，其定量表征对储层预测与建模具有重要意义。本文基于Google Earth遥感影像，以雅鲁藏布江中游典型砂质辫状河段为研究对象，系统测量了心滩长度、宽度、面积、辫状河道宽度及辫流带宽度等38组地貌参数，通过统计回归建立了各参数之间的定量关系模型。结果表明：心滩长度与宽度、心滩宽度与面积、心滩宽度与辫流带宽度、辫状河道宽度与辫流带宽度均呈现显著正相关关系。研究进一步结合沉积背景分析，明确了该定量模型的适用条件为强物源供给、常年流水、地形平缓的砂质辫状河环境。本成果丰富了辫状河储层地质知识库，可为相似沉积背景下的地下辫状河储层构型刻画与三维地质建模提供定量依据，提升稀井网区储层预测精度。

Abstract

Braided river sand bodies constitute a significant type of continental clastic reservoir in the Meso‑Cenozoic petroliferous basins of China. Channel bar sand bodies, as the key architectural element of such reservoirs, are of great importance for reservoir prediction and modeling through quantitative characterization. Based on Google Earth remote‑sensing images, this study selects a typical sandy braided‑river reach in the middle reaches of the Yarlung Zangbo River as the research object. A total of 38 sets of geomorphic parameters, including channel‑bar length, width, area, braided‑channel width, and braided‑belt width, were systematically measured. Statistical regression was employed to establish quantitative relational models among these parameters. The results demonstrate significant positive correlations between bar length and width, bar width and area, bar width and braided‑belt width, as well as between braided‑channel width and braided‑belt width. Furthermore, by integrating sedimentary background analysis, the applicable conditions of the quantitative models are clarified: they are suitable for sandy braided‑river environments characterized by strong sediment supply, perennial flow, and gentle topography. This study enriches the geological knowledge database of braided‑river reservoirs and can provide a quantitative reference for the architectural characterization and 3‑D geological modeling of subsurface braided‑river reservoirs under similar sedimentary settings, thereby improving reservoir prediction accuracy in areas with sparse well networks.