摘要：In the Chaixi region of the Qaidam Basin's Qigequan tectonic zone, the compact sandstones are characterized by their low porosity and permeability, featuring intricate pore-throat formations, varied lithologies, assorted clay minerals, and pronounced unevenness among the reservoirs. There's a weak link between reservoir metrics and logging reactions, making it challenging to assess these reservoir parameters. The microscopic pore structure of the reservoir can be illustrated through both the nuclear magnetic resonance relaxation time distribution and the capillary pressure curve. By using fractal dimensions to classify the reservoir, a conversion model between the transverse relaxation time in nuclear magnetic resonance logging and the capillary pressure in the mercury injection curve is established, enabling the conversion of pseudo-capillary pressure curves. Key elements of the pseudo-capillary pressure curve, specifically discharge and drive pressure, median pressure, and sorting coefficient, were analyzed and integrated with the generalized regression neural network for accurate reservoir type classification. An efficient categorization of reservoir types was accomplished by isolating three key elements from the pseudo capillary pressure curve-displacement pressure, median pressure, and sorting coefficient-and integrating them with the generalized regression neural network. Utilizing a rock physics framework, a correlation between transverse relaxation time of nuclear magnetic resonance and relative permeability conversion was formulated to accurately forecast the rate of water generation in the reservoirs of the western Qaidam Basin. The anticipated outcomes demonstrated a strong link with the real rate of water production. This technique presents an innovative method to forecast the comparative permeability of oil-water stages and the rates of water generation in compact sandstone reservoirs.

关键字：dense sandstone reservoir; T2-Pc Modelling; fractal dimension; T2-Kr Modelling; projected water yield

ISSN号：2296-6463

卷、期、页：卷13

发表日期：2025-03-04

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,SCIE（科学引文索引网络版）

发表期刊名称：Frontiers in Earth Science

参与作者：彭建,吕志斌

通讯作者：朱杰君

第一作者：陈双全

论文类型：期刊论文

论文概要：朱杰君,彭建,吕志斌,陈双全，Reservoir type classification and water yield prediction based on petrophysical conversion models，Frontiers in Earth Science，2025，卷13

论文题目：Reservoir type classification and water yield prediction based on petrophysical conversion models