Abstract Artificial intelligence and machine learning (ML) are being applied to many oil and gas (OG most notably net thickness and porosity. Optimized well design parameter settings vary relative to their placement across the study area and subsequent productivity ranking region. Overall, the ML-based framework discussed in this paper attempts to analyze shale controlling factors concurrently, to deliver a systematic evaluation result for production potential that accounts for and quantifies controlling features associated with geologic properties and well design attributes.

中文翻译：

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用于评估页岩气生产潜力的机器学习信息集成框架：Marcellus 页岩案例研究

摘要 人工智能和机器学习 (ML) 正在应用于许多石油和天然气（OG 最显着的净厚度和孔隙度。优化的井设计参数设置相对于它们在整个研究区域和随后的生产力排名区域中的位置而有所不同。总体而言，ML本文讨论的基于框架的框架试图同时分析页岩控制因素，为生产潜力提供系统的评估结果，该结果说明和量化与地质属性和井设计属性相关的控制特征。