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Enhancing reservoir porosity prediction from acoustic impedance and lithofacies using a weighted ensemble deep learning approach

Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consuming, expensive, and difficult to conduct. In addition, seismic inversion confronts problems of nonlinearity and has multiple solutions. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability, mapping directly from acoustic impedance and lithofacies data to porosity. To prove the point, in this paper, we trained an ensemble of DL models and then proposed a weight combination of every single trained model’s strength to improve the result. We evaluated the method's reliability using a number of metrics. Further, we compared it with traditional ones. The weighted ensemble resulted in a lower error than the simple ensemble and the single model. Its spatial distribution map showed the best connectivity with that of historical porosity. Finally, we tested our method's effectiveness using a dataset that was used in a previously published study. Our method improved the prediction of the latter.

Ketersediaan
141551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
10 hlm PDF, 7.361 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.16, December 2022
Subjek
Machine Learning
Deep neural networks
Reservoir rock properties
Ensemble learning methods
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Lampiran Berkas
  • Enhancing reservoir porosity prediction from acoustic impedance and lithofacies using a weighted ensemble deep learning approach
    Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consuming, expensive, and difficult to conduct. In addition, seismic inversion confronts problems of nonlinearity and has multiple solutions. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability, mapping directly from acoustic impedance and lithofacies data to porosity. To prove the point, in this paper, we trained an ensemble of DL models and then proposed a weight combination of every single trained model’s strength to improve the result. We evaluated the method's reliability using a number of metrics. Further, we compared it with traditional ones. The weighted ensemble resulted in a lower error than the simple ensemble and the single model. Its spatial distribution map showed the best connectivity with that of historical porosity. Finally, we tested our method's effectiveness using a dataset that was used in a previously published study. Our method improved the prediction of the latter.
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