Magnesian clay minerals and their influence on pores in the first member of Middle Permian Maokou Formation, southern Sichuan Basin
Liyu ZHANG; Donghua YOU; Rong LI; Lingjie YU; Qianglu CHEN; Youxiang LIU; Lingfang ZHOU · 2025 · Shiyou shiyan dizhi
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
In recent years, multiple wells in the Sichuan Basin have produced industrial gas from the marlstone/marl-bearing limestone strata in the first member of the Middle Permian Maokou Formation (Mao 1), demonstrating promising exploration potential. Previous studies have suggested that the marlstone/marl-bearing limestone in Mao 1 exhibits self-generation and self-storage characteristics, forming low-porosity, low-permeability fracture-porosity type tight carbonate reservoirs with diverse storage spaces. Notably, diagenetic shrinkage pores and fractures formed during the transformation of sepiolit
Abstract by Liyu ZHANG; Donghua YOU; Rong LI; Lingjie YU; Qianglu CHEN; Youxiang LIU; Lingfang ZHOU, Shiyou shiyan dizhi (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.11781/sysydz2025050974