摘要
机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑发布了关于人工智能的新研究结果。根据NewsRx编辑在中国北京的新闻报道，研究表明，"钻井液是高效钻井的关键"。国家自然科学基金资助本研究。本文从中国石油大学的研究中得到一句话:“然而,钻井液的胶凝性能受各种复杂因素的影响,传统方法效率低、成本高,人工智能和数值模拟技术已成为各学科的主流工具。本文综述了专家系统、人工神经网络(ANNs)、支持向量机(SVMs)、人工神经网络(ANNs)、人工神经网络(SVMs)、人工神经网络分析了目前这些研究中存在的问题，指出将这两种技术应用于钻井液凝胶性能研究的挑战包括难以获得现场数据和过于理想化的模型假设。可以估计，52.0%的论文与人工神经网络有关，泄漏问题是研究钻井液凝胶性能的主要问题，占该领域研究的17%以上。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news reporting out of Beijing, People’s Republic o f China, by NewsRx editors, research stated, “Drilling fluid is pivotal for effi cient drilling.” Financial supporters for this research include National Natural Science Foundati on of China. Our news journalists obtained a quote from the research from China University of Petroleum: “However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transf ormative tools in various disciplines. This work reviews the application of four artificial intelligence techniques-expert systems, artificial neural networks ( ANNs), support vector machines (SVMs), and genetic algorithms-and three numerica l simulation techniques-computational fluid dynamics (CFD) simulations, molecula r dynamics (MD) simulations, and Monte Carlo simulations-in drilling fluid desig n and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling flu id gelation performance research include difficulties in obtaining field data an d overly idealized model assumptions. From the literature review, it can be esti mated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation perf ormance, accounting for over 17% of research in this area.”
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