SBIR Phase I: Data-Driven Module for Prediction of Materials Physical-Chemical Properties Using Machine Learning

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是开发一个新的网络应用程序，以便更快、更准确地估计各种学术和工业学科的工程师和科学家使用的材料的重要物理和化学性质。石油和天然气、采矿、核废料管理和环境咨询公司可以利用这一工具获取关键信息，这些信息对以下方面至关重要：评估不同水力压裂和提高采收率策略下的油气产量，最大限度地降低矿山酸性废水污染地下水的风险，评估核废料处置库中水泥基工程屏障系统的安全性，设计有效的污染场地处理和修复策略。它还可以为分析地热能利用的长期影响、评价农业和粮食生产工业土壤系统的养分循环和农药污染、量化二氧化碳地质封存的不确定性提供必要的参数。SBIR第一阶段项目建议开发一个基于云的应用程序，该应用程序将使用新颖的机器学习算法和深度学习图像处理技术，将大量数据转化为各行业在决策过程中使用的有价值的参数。基于人工神经网络模型，新应用程序的最小可行产品版本将针对石油和天然气市场。它将为早期采用者提供最先进的数据驱动模块，包括特殊核心分析（SCAL）服务提供商、勘探和生产公司以及化学产品供应商。定制应用程序将引入一种计算效率更高的SCAL和数字岩石技术领域的替代方案（即基于物理和成像范式的结合），并将加速岩石性质的预测过程。首先，用户将能够计算出储层岩石最关键的属性之一，即渗透率。将第一阶段开发的用于岩石渗透率估计的机器学习算法定制到其他行业，将有助于进入其他市场。建立在亚马逊上？它将通过降低硬件和软件的前期和维护成本，为客户提供灵活、计算可扩展、按需且具有成本效益的解决方案。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to develop a novel web application for faster and more accurate estimation of important physical and chemical properties of materials used by engineers and scientists from various academic and industrial disciplines. Oil and gas, mining, nuclear waste management, and environmental consulting companies can use this tool to acquire critical information which are esential to: estimate the oil and gas production rates under different hydraulic fracturing and enhanced oil recovery strategies, minimize the risk of groundwater contamination by acid mine drainage, asses the safety of cement-based engineered barrier systems in nuclear waste repositories, and design the efficient treatment and remediation strategies for contaminated sites. It can also provide necessary parameters for analyzing the long-term effects of geothermal energy usage, evaluating nutrient cycling and pesticide contamination in soil systems in agriculture and food production industry, and quantifying the uncertainties associated with carbon dioxide geological sequestration and storage. This SBIR Phase I project proposes to develop a cloud-based application which will use novel machine learning algorithms and deep learning image processing techniques to turn large volumes of data into valuable parameters used by various industries in their decision-making process. Built on artificial neural network models, minimum viable product version of the new application will target the oil and gas market. It will offer a state-of-the-art data-driven module for early adopters including special core analysis (SCAL) service providers, exploration and production companies, and chemical product suppliers. The customized application will introduce a more computationally-efficient alternative to SCAL and digital rock technology field (i.e. combined physics-based and imaging paradigm) and will accelerate the forecasting process of rock properties. Initially, users will be able to calculate one of the most critical properties of the reservoir rock, i.e. permeability. Customizing machine learning algorithms developed in Phase I for rock permeability estimation to other industries will facilitate entering into other markets. Built on Amazon?s AWS cloud service, it will offer a flexible, computationally-scalable, on-demand and cost-effective solution to the customers by decreasing the upfront and maintenance costs of hardware and software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.