Electrical imaging of hydraulic fracturing fluid using steel-cased wells and a deep-learning method

Li, Yinchu1; Yang, Dikun1,2

2021-08-01

10.1190/GEO2020-0178.1

GEOPHYSICS   影响因子和分区

0016-8033

1942-2156

Steel-cased wells used as long electrodes (LEs) in a surface-electric or electromagnetic survey can enhance anomalous sig-nals from deep hydraulic fracturing zones filled by injected fluid. Although recent research has been published on the algo-rithms designed for the simulation of the effect of casings, feasibility studies on resolving the small-scale fracturing fluid flow from surface data are lacking. We have carried out a de-tectability and recoverability study for a top-casing electric source configuration. Applying a fast 3D DC modeling code using the concept of the equivalent resistor network, the detect-ability study shows that favorable conditions for detecting the fluid flow direction include multiple electrically coupled wells and different electric conductivities above and below the shale layer. The observed behavior is then modeled through a circuit analog. For the purpose of fracturing fluid imaging, the model for recovery is simplified as a distribution of full fluid saturation on a 2D fracture plane. A deep-learning (DL) framework is adopted to solve the imaging problem, which is difficult for con-ventional regularized inversions. Our DL implementation uses a supervised deep fully convolutional network to learn the rela-tionship between data patterns on the surface and the model of fracturing fluid distribution, encoded in a large number of synthetic data-model pairs. Once trained, the neural network can make a real-time and pixel-wise prediction of the fluid dis-tribution. The robustness of our DL approach is tested in the presence of ambient noise and inaccuracies in casing conduc-tivity. A reasonably consistent prediction performance has been observed. Our numerical feasibility study results demonstrate that electric surveys using steel casings as LEs have great potentials in real-time monitoring of fracturing fluid flow.

SCI

英语

第一 ; 通讯

National Natural Science Founda-tion of China[41974087] ; Shenzhen Key Lab-oratory of Deep Offshore Oil and Gas Exploration Technology[ZDSYS20190902093007855]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:000685070000008

SOC EXPLORATION GEOPHYSICISTS

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Deep Offshore Oil & Gas Explorat, Shenzhen 518055, Peoples R China

Li, Yinchu,Yang, Dikun. Electrical imaging of hydraulic fracturing fluid using steel-cased wells and a deep-learning method[J]. GEOPHYSICS,2021,86(4).

Li, Yinchu,&Yang, Dikun.(2021).Electrical imaging of hydraulic fracturing fluid using steel-cased wells and a deep-learning method.GEOPHYSICS,86(4).

Li, Yinchu,et al."Electrical imaging of hydraulic fracturing fluid using steel-cased wells and a deep-learning method".GEOPHYSICS 86.4(2021).