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Improved reservoir characterization of thin beds by advanced deep learning approach

Targeting reservoirs below seismic resolution presents a major challenge in reservoir characterization. High-resolution seismic data is critical for imaging the thin gas-bearing Khadro sand facies in several fields within the Lower Indus Basin (LIB). To truly characterize thin beds below tuning thickness, we showcase an optimally developed deep learning technique that can save up to 75% turn-around time while significantly reducing cost. Our workflow generates high-frequency acoustic impedance synthetics by utilizing a deep neural network (DNN) at the reservoir level vis-a-vis validating the results with existing geological facies. Simultaneously, we introduce continuous wavelet transform (CWT); wherein the three components (real, imaginary, and magnitude) are interrelated to obtain a resultant high-frequency seismic volume. A strong agreement is established at available wells to achieve a higher resolution seismic by injecting higher frequencies, which is then populated throughout the 3D cube. An excellent correlation is met with key seismic attributes extracted across the field for original and CWT-based synthetic seismic. The augmented seismic volume with enhanced frequency range substantiates the dominant frequency (Fd) and resolves thin beds, which is also validated with the help of wedge modeling of both acquired and high-frequency datasets. As a geologically valid solution, our approach effectively resolves an initially 54 m bed to ∼25 m. This deep-learning methodology is ideally suited to regions where the acquired seismic has limited resolution and lacks advanced reservoir characterization.

Ketersediaan
195551.136Perpustakaan 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, 15.238 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.23, September 2024
Subjek
Deep learning
Reservoir characterization
Seismic data
Continuous wavelet transform
Deep neural network
Geological facies
High-frequency
Limited resolution
Seismic attributes
Thin beds
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Lampiran Berkas
  • Improved reservoir characterization of thin beds by advanced deep learning approach
    Targeting reservoirs below seismic resolution presents a major challenge in reservoir characterization. High-resolution seismic data is critical for imaging the thin gas-bearing Khadro sand facies in several fields within the Lower Indus Basin (LIB). To truly characterize thin beds below tuning thickness, we showcase an optimally developed deep learning technique that can save up to 75% turn-around time while significantly reducing cost. Our workflow generates high-frequency acoustic impedance synthetics by utilizing a deep neural network (DNN) at the reservoir level vis-a-vis validating the results with existing geological facies. Simultaneously, we introduce continuous wavelet transform (CWT); wherein the three components (real, imaginary, and magnitude) are interrelated to obtain a resultant high-frequency seismic volume. A strong agreement is established at available wells to achieve a higher resolution seismic by injecting higher frequencies, which is then populated throughout the 3D cube. An excellent correlation is met with key seismic attributes extracted across the field for original and CWT-based synthetic seismic. The augmented seismic volume with enhanced frequency range substantiates the dominant frequency (Fd) and resolves thin beds, which is also validated with the help of wedge modeling of both acquired and high-frequency datasets. As a geologically valid solution, our approach effectively resolves an initially 54 m bed to ∼25 m. This deep-learning methodology is ideally suited to regions where the acquired seismic has limited resolution and lacks advanced reservoir characterization.
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