3D Borehole to Surface Electromagnetic Inversion Based on Upscaling network
DOI:
https://doi.org/10.54097/z3vh0w14Keywords:
Borehole-to-surface electromagnetic, Upscaling, Deep residual network, Three-dimensional inversionAbstract
Borehole-to-surface electromagnetic method, due to its transmitter source being located in the borehole, can more directly excite electromagnetic responses from deep formations compared to surface electromagnetic methods, effectively improving the detection resolution of deep targets. However, for steel-cased wells, the casing possesses extremely high electrical conductivity, and the casing effect cannot be ignored in the data processing of borehole-to-surface electromagnetic surveys. Direct discrete modeling of slender hollow steel casings is both difficult and unfavorable for subsequent inversion calculations. Therefore, this paper proposes a three-dimensional borehole-to-surface electromagnetic inversion method based on an upscaling network. First, through unsupervised deep learning, the method establishes a nonlinear mapping between the electromagnetic response of fine-grid models containing casings and equivalent coarse-grid models, training an upscaling network from fine-scale grids to coarse-scale grid electrical structures. Second, the upscaled coarse-grid information is used as the initial model to perform three-dimensional borehole-to-surface electromagnetic inversion. Finally, the accuracy and feasibility of the proposed method are verified through three-dimensional borehole-to-surface electromagnetic inversion examples, and the inversion capability for deep targets is analyzed through synthetic data models, examining sensitivity regions, single-transmitter wells, and dual-transmitter wells.
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[1] Cao Hui, Mao Lifeng, Wang Xuben, et al. Physical simulation experiment on borehole-to-surface electromagnetic responses[J]. Oil Geophysical Prospecting, 2013, 48(6): 995-998.
[2] .Li Tingting, Wang Jia, Zhu Kaiguang. Research on the exploration depth of the borehole-surface electromagnetic field[J]. Progress in Geophysics, 2013, 28(1): 373-379.
[3] Tsourlos P, Ogilvy R, Papazachos C, et al. Measurement and inversion schemes for single borehole-to-surface electrical resistivity tomography surveys[J]. Journal of Geophysics and Engineering, 2011, 8(4): 487. DOI: https://doi.org/10.1088/1742-2132/8/4/001
[4] .QIN Ce, FU Wenliang, WANG Xuben, et al.Three-dimensional anisotropic forward modeling algorithm for borehole-to-surface electromagnetic method using non-conforming meshes and high-order finite element[J]. Oil Geophysical Prospecting, 2024, 59(6): 1410-1419.
[5] Colombo D, McNeice G W. Quantifying surface-to-reservoir electromagnetics for waterflood monitoring in a Saudi Arabian carbonate reservoir[J]. Geophysics, 2013, 78(6): E281-E297. DOI: https://doi.org/10.1190/geo2012-0206.1
[6] Bai Z, Tan M J, Zhang F L. Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources[J]. Applied Geophysics, 2016, 13(3): 437-448. DOI: https://doi.org/10.1007/s11770-016-0575-8
[7] Cao Hui, Wang Xuben, He Zhanxiang, et al. Calculation of borehole-to-surface electromagnetic responses on horizontal stratified earth medium[J]. Oil Geophysical Prospecting, 2012, 47(2): 338-343.
[8] Colombo D, McNeice G, Cuevas N, et al. "Surface to borehole electromagnetics for 3D waterflood monitoring: Results from first field deployment." SPE Annual Technical Conference and Exhibition?. SPE, 2018. DOI: https://doi.org/10.2118/191544-MS
[9] Hoversten G M, Schwarzbach C, Belliveau P, et al. "Borehole to surface electromagnetic monitoring of hydraulic fractures." 79th EAGE Conference and Exhibition 2017. Vol. 2017. No. 1. European Association of Geoscientists & Engineers, 2017. DOI: https://doi.org/10.3997/2214-4609.201700853
[10] Streich R. Controlled-source electromagnetic approaches for hydrocarbon exploration and monitoring on land[J]. Surveys in geophysics, 2016, 37(1): 47-80. DOI: https://doi.org/10.1007/s10712-015-9336-0
[11] Puzyrev V, Vilamajo E, Queralt P, et al. Three-dimensional modeling of the casing effect in onshore controlled-source electromagnetic surveys[J]. Surveys in Geophysics, 2017, 38: 527-545. DOI: https://doi.org/10.1007/s10712-016-9397-8
[12] Patzer C, Tietze K, Ritter O. Steel-cased wells in 3-D controlled source EM modelling[J]. Geophysical Journal International, 2017, 209(2): 813-826. DOI: https://doi.org/10.1093/gji/ggx049
[13] Heagy L J, Oldenburg D W. Modeling electromagnetics on cylindrical meshes with applications to steel-cased wells[J]. Computers & Geosciences, 2019, 125: 115-130. DOI: https://doi.org/10.1016/j.cageo.2018.11.010
[14] Cuevas N H. Insights on electromagnetic scattering by steel casings in surface-to-borehole and borehole-to-surface methods[J]. The Leading Edge, 2022, 41(2): 93-99. DOI: https://doi.org/10.1190/tle41020093.1
[15] Marsala A F, Al-Buali M, Ali Z, et al. "First pilot of borehole to surface electromagnetic in saudi arabia-a new technology to enhance reservoir mapping & monitoring." 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. European Association of Geoscientists & Engineers, 2011.
[16] Tietze K, Patzer C, Ritter O, et al. "3D inversion of controlled-source electromagnetic data in the presence of steel-cased wells." 78th EAGE Conference and Exhibition 2016. Vol. 2016. No. 1. European Association of Geoscientists & Engineers, 2016. DOI: https://doi.org/10.3997/2214-4609.201600557
[17] Um E S, Commer M, Newman G A, et al. Finite element modelling of transient electromagnetic fields near steel-cased wells[J]. Geophysical Journal International, 2015, 202(2): 901-913. DOI: https://doi.org/10.1093/gji/ggv193
[18] Commer M, Hoversten G M, Um E S. Transient-electromagnetic finite-difference time-domain earth modeling over steel infrastructure[J]. Geophysics, 2015, 80(2): E147-E162. DOI: https://doi.org/10.1190/geo2014-0324.1
[19] Qin C, Li H, Li H, et al. High-order finite-element forward-modeling algorithm for the 3D borehole-to-surface electromagnetic method using octree meshes[J]. Geophysics, 2024, 89(4): F61-F75. DOI: https://doi.org/10.1190/geo2023-0365.1
[20] Castillo-Reyes O, Modesto D, Queralt P, et al. 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms[J]. Computers & Geosciences, 2022, 160: 105030. DOI: https://doi.org/10.1016/j.cageo.2021.105030
[21] Weiss C J. Finite-element analysis for model parameters distributed on a hierarchy of geometric simplices[J]. Geophysics, 2017, 82(4): E155-E167. DOI: https://doi.org/10.1190/geo2017-0058.1
[22] Schwarzbach, Christoph, and Eldad Haber. "Improved upscaling of steel-cased wells through inversion." SEG International Exposition and Annual Meeting. SEG, 2018. DOI: https://doi.org/10.1190/segam2018-2998601.1
[23] Um E S, Kim J, Wilt M. 3D borehole-to-surface and surface electromagnetic modeling and inversion in the presence of steel infrastructure[J]. Geophysics, 2020, 85(5): E139-E152. DOI: https://doi.org/10.1190/geo2019-0034.1
[24] Liu B, Guo Q, Li S, et al. Deep learning inversion of electrical resistivity data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5715-5728. DOI: https://doi.org/10.1109/TGRS.2020.2969040
[25] Puzyrev V. Deep learning electromagnetic inversion with convolutional neural networks[J]. Geophysical Journal International, 2019, 218(2): 817-832. DOI: https://doi.org/10.1093/gji/ggz204
[26] Liao Q, Lei G, Zhang D, et al. Analytical solution for upscaling hydraulic conductivity in anisotropic heterogeneous formations[J]. Advances in Water Resources, 2019, 128: 97-116. DOI: https://doi.org/10.1016/j.advwatres.2019.04.011
[27] Li H, Durlofsky L J. Ensemble level upscaling for compositional flow simulation[J]. Computational Geosciences, 2016, 20(3): 525-540. DOI: https://doi.org/10.1007/s10596-015-9503-x
[28] Wang N, Liao Q, Chang H, et al. Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network[J]. Computational Geosciences, 2023, 27(6): 913-938. DOI: https://doi.org/10.1007/s10596-023-10233-2
[29] Wang Z, Battiato I. A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems[J]. Advances in Water Resources, 2024, 183: 104588. DOI: https://doi.org/10.1016/j.advwatres.2023.104588
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