LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Technical Report · Thu Jul 07 04:00:00 EDT 2022

DOI:https://doi.org/10.2172/1880447· OSTI ID:1880447

Viswanathan, Hari S. ^[1]; Carey, James William ^[1]; Xu, Hongwu ^[1]; Karra, Satish ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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 fully and partially supported by the fundamental shale portfolio 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. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies 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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: 89233218CNA000001
• OSTI ID: 1880447
• Report Number(s): LA-UR-22-28005
• Country of Publication: United States
• Language: English

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