摘要
根据NewsRx记者来自华盛顿特区的新闻报道，由一名新闻记者兼机器人和机器学习每日新闻的工作人员新闻编辑，发明者库克，大卫（Lakeway，TX，US）于2024年2月8日提交的专利申请于2024年6月6日在NLI NE公布。本专利申请未转让给公司或机构。新闻编辑从发明者提供的背景信息中获得以下引文：“由于储层性质的多样性，水平井大规模压裂后天然稀土产量存在显著差异。水力压裂优化对于天然资源开采中低产能储层的增产和开发非常重要。优化水力压裂工艺以最大限度地提高自然资源产量的解决方案。”"当前的解决方案不令人满意。"除了为该专利申请获得的背景信息外，NewsRx记者还获得了本发明人对该专利申请的概要信息：“在一方面，一种用于水力压裂优化的装置，其中该装置包括至少一处理器，以及通信地连接到该至少一处理器的存储器，所述存储器包含指令，所述指令配置所述至少处理器以从至少一感测设备接收储层数据，生成生产训练数据，所述生产训练数据包括作为输入的多个储层数据，所述输入与作为输出的多个最优生产参数相关联，使用所述生产训练数据训练水力压裂优化机器学习模型，确定作为Frackin G优化机器学习模型函数的最优生产参数，并生成作为最优生产参数函数的最优生产计划。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A patent application by the inventor Cook, David (Lakeway, TX, US), filed on February 8, 2024, was made available onli ne on June 6, 2024, according to news reporting originating from Washington, D.C ., by NewsRx correspondents. This patent application has not been assigned to a company or institution. The following quote was obtained by the news editors from the background informa tion supplied by the inventors: "There are significant differences in natural re source production after large-scale fracking of horizontal wells due to a plural ity of reservoir properties. Fracking optimization is very important for low per meability reservoir stimulation and development in natural resource extraction. A solution for optimizing fracking process for maximizing natural resource produ ction is needed. Existing solutions are not satisfactory." In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventor's summary information for this pat ent application: "In an aspect, an apparatus for fracking optimization, wherein the apparatus includes at least a processor, and a memory communicatively connec ted to the at least a processor, the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing de vice, generate a production training data include a plurality of reservoir datum s as input correlated to a plurality of optimal production parameters as output, train a fracking optimization machine-learning model using the production train ing data, determine an optimal production parameter as a function of the frackin g optimization machine-learning model, and generate an optimal production plan a s a function of the optimal production parameter.
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