Image of Irida: A machine learning based code for the automated derivation of site-specific rock type logs and their properties using Kimeleon colourlith image logs

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Irida: A machine learning based code for the automated derivation of site-specific rock type logs and their properties using Kimeleon colourlith image logs

High resolution characterization of sub-surface geology is critical to improving the performance of reservoir models in fluid flow and reactive transport simulation studies in the fields of groundwater, CO2 geo-sequestration and oil and gas research. The modern improvements in wireline logging technology allow for the deduction of depth continuous records of individual rock properties at cm-scale resolution. However, to the best of the authors’ knowledge, no method exists to obtain high-resolution lithotype logs. Traditional methods for rock typing are based on core sample analysis and are therefore limited to discrete depth. The Kimeleon colourlith log output is based on the combination of wireline logs and is probably one of the few tools which creates continuous lithotype logs at high resolution primarily for the purpose of visualization. There is currently no automated method to transform the colourlith images into quantitative rock type logs which can be used as an input for reservoir modelling software. Additionally, colourlith logs are based solely on wireline information and do not incorporate local lithological information from core samples. This study addresses these issues by combining discrete core sample data with continuous colourlith logs. A code (Irida) has been developed to enhance the usability of the Kimeleon software by transforming colourlith logs into high resolution lithotype logs of rock types identified in core plugs. The code takes the colourlith image log as an input along with the names and properties of site-specific rock types identified and measured in discrete core samples. The code then uses k-means clustering, an unsupervised machine learning method, to compute a high-resolution log of rock types and their properties which are continuous with depth. The properties of interest include porosity, anisotropic absolute and relative permeabilities and capillary pressure curves. The code significantly reduces the efforts required for the preparation of lithotype logs.

Ketersediaan
143551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
12 hlm PDF, 8,391 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.16, December 2022
Subjek
Upscaling
k-means clustering
Rock type log
Colourlith log
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Irida: A machine learning based code for the automated derivation of site-specific rock type logs and their properties using Kimeleon colourlith image logs
    High resolution characterization of sub-surface geology is critical to improving the performance of reservoir models in fluid flow and reactive transport simulation studies in the fields of groundwater, CO2 geo-sequestration and oil and gas research. The modern improvements in wireline logging technology allow for the deduction of depth continuous records of individual rock properties at cm-scale resolution. However, to the best of the authors’ knowledge, no method exists to obtain high-resolution lithotype logs. Traditional methods for rock typing are based on core sample analysis and are therefore limited to discrete depth. The Kimeleon colourlith log output is based on the combination of wireline logs and is probably one of the few tools which creates continuous lithotype logs at high resolution primarily for the purpose of visualization. There is currently no automated method to transform the colourlith images into quantitative rock type logs which can be used as an input for reservoir modelling software. Additionally, colourlith logs are based solely on wireline information and do not incorporate local lithological information from core samples. This study addresses these issues by combining discrete core sample data with continuous colourlith logs. A code (Irida) has been developed to enhance the usability of the Kimeleon software by transforming colourlith logs into high resolution lithotype logs of rock types identified in core plugs. The code takes the colourlith image log as an input along with the names and properties of site-specific rock types identified and measured in discrete core samples. The code then uses k-means clustering, an unsupervised machine learning method, to compute a high-resolution log of rock types and their properties which are continuous with depth. The properties of interest include porosity, anisotropic absolute and relative permeabilities and capillary pressure curves. The code significantly reduces the efforts required for the preparation of lithotype logs.
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