A deep learning approach to the inversion of borehole resistivity measurements

Authors: Shahriari, M., Pardo, D., Picon, A., Galdran, A., Del Ser, J. and Torres-Verdín, C.

Journal: Computational Geosciences

Volume: 24

Issue: 3

Pages: 971-994

eISSN: 1573-1499

ISSN: 1420-0597

DOI: 10.1007/s10596-019-09859-y

Abstract:

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.

Source: Scopus

A deep learning approach to the inversion of borehole resistivity measurements

Authors: Shahriari, M., Pardo, D., Picon, A., Galdran, A., Del Ser, J. and Torres-Verdin, C.

Journal: COMPUTATIONAL GEOSCIENCES

Volume: 24

Issue: 3

Pages: 971-994

eISSN: 1573-1499

ISSN: 1420-0597

DOI: 10.1007/s10596-019-09859-y

Source: Web of Science (Lite)

A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements.

Authors: Shahriari, M., Pardo, D., Picón, A., Galdran, A., Ser, J.D. and Torres-Verdín, C.

Journal: CoRR

Volume: abs/1810.04522

Source: DBLP

A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements

Authors: Shahriari, M., Pardo, D., Picón, A., Galdrán, A., Ser, J.D. and Torres-Verdín, C.

Abstract:

We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it becomes essential to invert them in real time. In this work, we explore the possibility of using Deep Neural Network (DNN) to perform a rapid inversion of borehole resistivity measurements. Herein, we build a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically meaningful and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is built, we can perform the actual inversion of the field measurements in real time. We illustrate the performance of DNN of logging-while-drilling measurements acquired on high-angle wells via synthetic data.

Source: arXiv