Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand

• Published in: Geomechanics and geophysics for geo-energy and geo-resources. Vol. 9; no. 1; pp. 1 - 27
• Main Authors: Ismail, Amir, Radwan, Ahmed A., Leila, Mahmoud, Abdelmaksoud, Ahmed, Ali, Moamen
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2023; Springer Nature B.V; Springer
• Subjects: Automatic fault extraction; Computation; Detection; Energy; Engineering; Environmental Science and Engineering; Fault detection; Fault lines; Faults; Filters; Fluid filters; Foundations; Geoengineering; Geological faults; Geometrical attributes; Geophysics/Geodesy; Geostatistics; Geotechnical Engineering & Applied Earth Sciences; High resolution; Hydraulics; Locating; Machine learning; Neural networks; Seismic data; Similarity; Structural models; Thinned fault likelihood; Three dimensional models; Unsupervised learning; Unsupervised neural network; Voxel connectivity filter; Well logs; Workflow; Voxel connectivity filter; Unsupervised neural network; Geometrical attributes; Automatic fault extraction; Thinned fault likelihood
• ISSN: 2363-8419; 2363-8427
• DOI: 10.1007/s40948-023-00646-9

Unsupervised machine learning using an unsupervised vector quantization neural network (UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as opposed to traditional methodologies is currently used effectively in the 3D seismic structural interpretation for high-resolution detection of fault patterns, fracture network zones, and small-scale faults (SSFs). This technology has a crucial role in locating prospective well sites and building a 3D structural model while saving time and cost. The innovation of the current workflow involves combining geostatistical and structural filtering, optimal geometrical seismic attributes, UVQ-NN for automatic major faults, fracture network zones, and SSFs volumes extraction due to the unavailability of well logs and cores. To sharpen the fault edges and discontinuities, a steered volume was first extracted. Structural filters were then applied to the 3D volume, first with a dip-steered median filter (DSMF), followed by a dip-steered diffusion filter (DSDF), and finally, both DSMF and DSDF were combined to generate the fault enhancement filter (FEF). After that, optimal geometrical attributes were computed and extracted, such as similarity, FEF on similarity, maximum curvature, polar dip, fracture density, and thinned fault likelihood (TFL) attributes. Finally, selected attributes were inserted as the input layer to the UVQ-NN to generate segmentation and matching volumes. On the other hand, the TFL was used with the voxel connectivity filter (VCF) for 3D automatic fault patches extraction. The results from the UVQ-NN and VCF identified the locations, orientations, and extensions of the main faults, SSFs, and fracture networks. The implemented approach is innovative and can be employed in the future for the identification, extraction, and classification of geological faults and fracture networks in any region of the world. Article highlights Using an unsupervised vector quantization neural network (UVQ-NN) integrated with meta-geometrical attributes used effectively in the 3D-seismic structural interpretation for high-resolution detection of fault patterns, fracture network zones, and small-scale faults (SSFs). Seismic conditioning includes geostatistical and geometrical filters help to improve and polish the seismic data's structural features. The role of voxel connectivity filter and unsupervised machine learning combined with geometrical attributes in hydrocarbon exploration.