Shale fundamentals: Experimental and modeling insights. (November 2022)

• Record Type: Journal Article
• Authors: Mehana, Mohamed; Santos, Javier E.; Neil, Chelsea; Carey, James William; Guthrie, George; Hyman, Jeffery; Kang, Qinjun; Karra, Satish; Sweeney, Mathew; Xu, Hongwu; Viswanathan, Hari
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 11192
• Page End: 11205
• Publication Date: 2022-11
• Subjects: Shale fundamentals -- Hydraulic fracturing -- Machine learning -- Molecular simulation -- Numerical modeling; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.08.229
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. We will discuss the main processes involving the matrix, hydraulic fractures, enhanced oil recovery, and carbon dioxide sequestration. In addition, we present science-informed workflows and platforms to optimize pressure-drawdown at a site, enable real-time reservoir management, accelerate numerical modeling and quantify uncertainty. We summarize our insights on pressure-drawdown optimization to maximize recovery while considering the lifetime of the well. In addition, we demonstrate our work on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays and basins.