SBIR Phase I: An Intelligent Decision Support System Software for Unconventional Oil and Gas Field Development Design

这个小型企业创新研究（SBIR）项目的更广泛的影响/商业潜力是用更准确的混合模型取代非常规油气田开发设计中现有耗时的数值工具和不准确的统计和分析方法，这些模型利用了物理特性以及先进的机器学习方法。初步测试表明，这种人工智能软件可以显着提高预测准确性，降低每桶生产石油和天然气的成本。该研究的结果使石油和天然气生产商能够通过探索所有可能的井和完井设计并基于多个标准找到最佳设计来提高其非常规油气藏（也称为页岩油气藏）的最终石油采收率。STTR第一阶段项目提出开发智能决策支持系统（IDSS）以优化非常规油气田开发设计。这是第一次在石油和天然气工业中为此目的提出一个全面的IDSS。该软件背后的技术在三个层面上改进了油田开发设计流程。首先，使用混合方法（基于物理的油藏工程，先进的机器学习和深度学习）的鲁棒预测模型可以为每个潜在的设计进行准确快速的产量预测。然后，在优化层面，考虑到当地地下地质的不确定性，该模型找到最佳的油田开发设计。最终，在最高级别，认知单元帮助决策者根据他们的目标找到最终设计。该奖项反映了NSF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 replace existing time-consuming numerical tools and inaccurate statistical and analytical methods in unconventional oil and gas field development design with more accurate hybrid models that take advantage of physical properties as well as advanced machine learning methods. The initial tests suggest that such AI software can significantly increase the prediction accuracy and reduce the cost per barrel of produced oil and gas. The results of this research enable oil and gas producers to increase their ultimate oil recovery from their unconventional reservoirs (also known as shale reservoir) by exploring all possible well and completion designs and finding the most optimal one based on multiple criteria.This STTR Phase I project proposes to develop an intelligent decision support system (IDSS) to optimize unconventional oil and gas field development designs. It is for the first time that a comprehensive IDSS is being proposed in the oil and gas industry for this purpose. The technology behind this software improves the field development design process at three levels. First, a robust predictive model using a hybrid approach (physics-based reservoir engineering, advanced machine learning, and deep learning) makes an accurate and fast production forecast for every potential design. Then, at the optimization level, considering the local subsurface geological uncertainty, the model finds the most optimum field development designs. Eventually, at the highest level, the cognitive unit helps decision makers to find their final design based on their objectives.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.