<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Xiaoyang Du | Geosystem Innovation Laboratory</title><link>https://wjin33.github.io/GIL/author/xiaoyang-du/</link><atom:link href="https://wjin33.github.io/GIL/author/xiaoyang-du/index.xml" rel="self" type="application/rss+xml"/><description>Xiaoyang Du</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><image><url>https://wjin33.github.io/GIL/author/xiaoyang-du/avatar_hu15451091116481512995.png</url><title>Xiaoyang Du</title><link>https://wjin33.github.io/GIL/author/xiaoyang-du/</link></image><item><title>Xiaoyang Du</title><link>https://wjin33.github.io/GIL/author/xiaoyang-du/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://wjin33.github.io/GIL/author/xiaoyang-du/</guid><description>&lt;p>Xiaoyang is a Ph.D. student in Petroleum Engineering at Texas A&amp;amp;M University focused on subsurface lithology characterization and data-driven formation evaluation. His research integrates quantitative XRD and XRF characterization of drill cuttings with mud-logging, drilling, and well-log data to investigate relationships among mineral composition, geochemistry, formation properties, and drilling response. These efforts aim to establish a multimodal subsurface characterization database and develop physics-constrained machine-learning methods for rapid lithology classification and mineral-composition estimation. Before joining Texas A&amp;amp;M, his research focused on hydraulic fracturing, including treatment-pressure prediction, screenout-risk assessment based on net-pressure response characteristics, and pumping-schedule optimization.&lt;/p></description></item></channel></rss>