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Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas

The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having 4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E.

Ketersediaan
#
Perpustakaan BIG (Eksternal Harddisk) 550
364
Tersedia
Informasi Detail
Judul Seri
Geosciences
No. Panggil
550
Penerbit
Switzerland : MPDI.,
Deskripsi Fisik
29 hlm PDF, 13.887 KB
Bahasa
Inggris
ISBN/ISSN
2076-3263
Klasifikasi
550
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
online resource
Edisi
Vol.15, Issue 1, January 2025
Subjek
Neural network
Shale
photoelectric factor
carbonates
machine-learning
multiattributes
3D seismic
neutron porosity
mixed lithology
gamma ray
Info Detail Spesifik
Geosciences
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

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  • Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas
    The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having 4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E.
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