3D inversion of electromagnetic logging-while-drilling data

David Marchant; Nigel Clegg; Luke Rawsthorne; Jari Kunnas

doi:10.1080/22020586.2019.12073097

2nd Australasian Exploration Geoscience Conference: Data to Discovery

ISSN: 2202-0586
E-ISSN:

oa 3D inversion of electromagnetic logging-while-drilling data
Authors David Marchant^a, Nigel Clegg^b, Luke Rawsthorne^c and Jari Kunnas^d
View Affiliations Hide Affiliations

^a Computational Geosciences Inc, . Vancouver, , BC, , Canada, [email protected] ^b Halliburton Great Yarmouth, , UK, [email protected] ^c Aker BP, Stavanger, Norway, [email protected] ^d Halliburton, Stavanger, , Norway, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2019, Issue 2nd Australasian Exploration Geoscience Conference: Data to Discovery, Dec 2019, p. 1 - 5
DOI: https://doi.org/10.1080/22020586.2019.12073097
- Published online: 01 Dec 2019

Abstract

Summary

Electromagnetic logging while drilling is commonly used to infer information about the electrical properties around the wellbore and to aid in geosteering. Data from modern tools, which combine multiple transmitter and receiver orientations and offsets, can be difficult to manually interpret in all but the simplest of environments. Inversion is required to optimally extract and use the information from this data. Although low dimensional inversions can provide useful information in certain environments, full, 3D solutions are required to extract the maximum possible amount of information from the data.

In this work, we present the first fully 3D inversion of electromagnetic logging-while-drilling data. Moreover, we demonstrate that using semi-structured meshing and mesh decoupling, along with advanced data integration techniques, enables the inversions to be performed in real time.

Article metrics loading...

/content/journals/10.1080/22020586.2019.12073097

2019-12-01

2026-01-18

From This Site

/content/journals/10.1080/22020586.2019.12073097

dcterms_title,dcterms_subject,pub_keyword

-contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

Abubakar, A., Habashy, T.M., Druskin, V.L., Knizhnerman, L., and Alumbaugh, D., 2008, 2.5D forward and inverse modeling for interpreting low-frequency electromagnetic measurements: Geophysics, 75 (4), F165-F177.
Bakr, S., Pardo, D., and Torres-Verdin, C., 2017, Fast inversion of logging-while-drilling resistivity measurements acquired in multiple wells: Geophysics, 82, E111-E120.
Clegg, N., Djefel, B., Kunnas, J., and Kuld, I., 2018, Identification and projected interception of oblique faults up to 85 ft away using ultra-deep azimuthal resistivity and geosignals: A case history from the Norwegian Continental Shelf. Presented at Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, 12-15 November. SPE-192854-MS. https://doi.org/10.2118/192854-MS.
Dupuis, C. and Denichou, J.M., 2015, Automatic inversion of deep-directional-resistivity measurements for well placement and reservoir description: The Leading Edge, 34 (5), 504-512.
Fohring, J. and Haber, E., 2016, Adaptive a-optimal experimental design for linear dynamical systems: SIAM/ASA Journal of Uncertainty Quantification, 4 (X), 1138-1159.
Haber, E. and Schwazbach, C., 2014, Parallel inversion of large-scale airborne time-domain electromagnetic data with multiple OcTree meshes: Inverse Problems, 30 (5).
Sviridov, M.V., Mosin, A., Antonov, Y., Nikitenko, M., Martakov, S., and Rabinovich, M., 2014, New software for processing LWD extra deep resistivity and azimuthal resistivity data: SPE Reservoir Evaluation and Engineering, 17 (2). SPE-160257-PA. https://doi.org/10.2118/160257-PA.
Thiel, M. and Omeragic, D., 2018, Azimuthal imaging using deep-directional resistivity measurements reveals 3D reservoir structure: Presented at SPWLA 59th Annual Logging Symposium, London, UK, 2-6 June. SPWLA-2018-S..
Wu, H.-H., Golla, C., Parker, T., et al., 2018, A new ultra-deep azimuthal electromagnetic LWD sensor for reservoir insight. Presented at the SPWLA 59th Annual Logging Symposium, London, UK, 2-6 June. SPWLA-2018-X.

/content/journals/10.1080/22020586.2019.12073097

Article Type: Research Article

Keyword(s): borehole geophysics; electromagnetic; inversion; three-dimensional

Most Cited This Month Most Cited RSS feed

- Inversion of data from electrical imaging surveys in water-covered areas
  
  Authors M. H. Loke and J. W. Lane
- Inversion of Magnetic Data from Remanent and Induced Sources
  
  Authors Robert G. Elllis, Barrry de Wet and Ian N. Macleod
- A comparison of smooth and blocky inversion methods in 2-D electrical imaging surveys
  
  Authors M. H. Loke, Ian Acworth and Torleif Dahlin
- Designing matched bandpass and azimuthal filters for the separation of potential-field anomalies by source region and source type
  
  Authors Jeffrey D. Phillips
- Bad Colour Maps Hide Big Features and Create False Anomalies
  
  Authors Peter Kovesi
- Practical 3D EM inversion – the P223F software suite
  
  Authors Art Raiche, Fred Sugeng and Glenn Wilson
- Estimating Noise Levels in AEM Data
  
  Authors Andy Green and Richard Lane
- Target delineation using Full Tensor Gravity Gradiometry data
  
  Authors Colm A. Murphy and James Brewster
- Transdimensional Monte Carlo Inversion of AEM Data
  
  Authors Ross C Brodie and Malcolm Sambridge
- The geotech VTEM time domain helicopter em system
  
  Authors Ken Witherly, Richard Irvine and Edward Morrison
More Less

oa 3D inversion of electromagnetic logging-while-drilling data

Abstract

Most Read This Month

Most Cited This Month Most Cited RSS feed