Application of Unconventional Seismic Attributes and Unsupervised Machine Learning for the Identification of Fault and Fracture Network

• Published in: Applied sciences Vol. 10; no. 11; p. 3864
• Main Authors: Ashraf, Umar, Zhang, Hucai, Anees, Aqsa, Nasir Mangi, Hassan, Ali, Muhammad, Ullah, Zaheen, Zhang, Xiaonan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2020
• Subjects: ant-colony optimization; Basins; Carbonate reservoirs; Cores; Drilling; Fault detection; Fault lines; fracture density; fracture network; fracture proximity; Fractures; Fractures (Geology); Geological faults; Hydrocarbons; Identification; Learning algorithms; Machine learning; Mechanical properties; Neural networks; Oil and gas fields; Optimization; Optimization algorithms; Plate tectonics; Shale; Shale gas; Studies; thinned fault likelihood; Unsupervised learning; unsupervised neural network; Workflow; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10113864

The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW–SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.