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A deep learning approach to the inversion of borehole resistivity measurements

Computational geosciences, 2020-06, Vol.24 (3), p.971-994 [Peer Reviewed Journal]

Springer Nature Switzerland AG 2020 ;Springer Nature Switzerland AG 2020. ;ISSN: 1420-0597 ;EISSN: 1573-1499 ;DOI: 10.1007/s10596-019-09859-y

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Title:
A deep learning approach to the inversion of borehole resistivity measurements
Author: Shahriari, M. ; Pardo, D. ; Picon, A. ; Galdran, A. ; Del Ser, J. ; Torres-Verdín, C.
Subjects: Artificial neural networks ; Boreholes ; Deep learning ; Drilling ; Earth and Environmental Science ; Earth Sciences ; Electrical properties ; Electrical resistivity ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Inverse problems ; Inversion ; Logging ; Mathematical Modeling and Industrial Mathematics ; Neural networks ; Original Paper ; Real time ; Silicon ; Soil Science & Conservation
Is Part Of: Computational geosciences, 2020-06, Vol.24 (3), p.971-994
Description: Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are often used to estimate layer-by-layer electrical properties from measurements. When used for well geosteering purposes, it becomes essential to invert the measurements into layer-by-layer values of electrical resistivity in real time. We explore the possibility of using deep neural networks (DNNs) to perform rapid inversion of borehole resistivity measurements. Accordingly, we construct a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically reliable and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is constructed, we can invert borehole measurements in real time. We illustrate the performance of the DNN for inverting logging-while-drilling (LWD) measurements acquired in high-angle wells via synthetic examples. Numerical results are promising, although further work is needed to achieve the accuracy and reliability required by petrophysicists and drillers.
Publisher: Cham: Springer International Publishing
Language: English
Identifier: ISSN: 1420-0597
EISSN: 1573-1499
DOI: 10.1007/s10596-019-09859-y
Source: ProQuest Central

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A deep learning approach to the inversion of borehole resistivity measurements

Springer Nature Switzerland AG 2020 ;Springer Nature Switzerland AG 2020. ;ISSN: 1420-0597 ;EISSN: 1573-1499 ;DOI: 10.1007/s10596-019-09859-y

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