Data-Driven Proxy Modeling of Water Front Propagation in Porous Media

Abstract

In the water flooding process, determining the location of the injected water front is as one of the most critical variables, which is the basis of many subsequent predictions. Despite the importance and use of this parameter in a vast range of flooding-related assessments, there are no alternative methods to traditional analytical modeling or time-consuming numerical 3D simulation for its determination. This study introduces a data-driven proxy modeling approach based on two powerful deep learning algorithms for real-time determination of the injected water front location on the grid scale. The developed proxy models have realized the possibility of modeling the location of the flow front by minimally using the data extracted from the numerical simulators and only relying on commonly available field data. The proposed proxy models successfully simulated the breakthrough time in production wells and water arrival time in certain reservoir grids in new blind scenarios.

KEYWORDS:

• Data-driven proxy modeling
• water flooding
• waterfront location
• deep learning
• fluid dynamics

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant 52274057, 52074340 and 51874335, the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008, the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN, the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002 , 111 Project under Grant B08028.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 51874335,52074340]; Natural Science Foundation of Shandong Province: [Grant Number JQ201808,ZR2022QD080].