摘要
Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据NewsRx记者在中国大庆的新闻报道，研究表明：“水泥系统的性能决定了它的潜在应用，然而，在先进的油田开发中，创造一种凝结时间可控、注入能力强、抗压强度高的可靠水泥封堵系统往往需要大量的时间和资源。”通过对超细水泥的初凝时间、终凝时间、粘度和抗压强度的分析，建立了基于机器学习的XGBoost模型，以优化封堵系统性能，降低成本，提高效率。本研究的资金来源包括中国石油创新基金、吉林大学无机合成与修复化学国家重点实验室开放项目基金。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Daqing, People’s Republ ic of China, by NewsRx journalists, research stated, “The properties of a cement system dictate its potential applications, yet creating a reliable cement plugg ing system with controllable setting times, robust injection capacity, and high compressive strength often requires a lot of time and resources in advanced oilf ield development. To address this issue, a machine learningbased XGBoost model was created to optimize plugging system performance, reduce costs, and enhance e fficiency by analysing the initial setting time, final setting time, viscosity, and compressive strength of ultra-fine cement.” Financial supporters for this research include PetroChina Innovation Foundation, Opening Project Foundation of State Key Laboratory of Inorganic Synthesis and P reparative Chemistry of Jilin University.
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