Automated Fault Detection and Extraction under Gas Chimneys Using Hybrid Discontinuity Attributes

Abstract

3D-seismic data have increasingly shifted seismic interpretation work from a horizons-based to a volume-based focus over the past decade. The size of the identification and mapping work has therefore become difficult and requires faster and better tools. Faults, for instance, are one of the most significant features of subsurface geology interpreted from seismic data. Detailed fault interpretation is very important in reservoir characterization and modeling. The conventional manual fault picking is a time-consuming and inefficient process. It becomes more challenging and error-prone when dealing with poor quality seismic data under gas chimneys. Several seismic attributes are available for faults and discontinuity detection and are applied with varying degrees of success. We present a hybrid workflow that combines a semblance-based fault likelihood attribute with a conventional ant-tracking attribute. This innovative workflow generates optimized discontinuity volumes for fault detection and automatic extraction. The data optimization and conditioning processes are applied to suppress random and coherent noise first, and then a combination of seismic attributes is generated and co-rendered to enhance the discontinuities. The result is the volume with razor sharp discontinuities which are tracked and extracted automatically. Contrary to several available fault tracking techniques that use local seismic continuity like coherency attributes, our hybrid method is based on directed semblance, which incorporates aspects of Dave Hale’s superior fault-oriented semblance algorithm. The methodology is applied on a complex faulted reservoir interval under gas chimneys in a Malaysian basin, yet the results were promising. Despite the poor data quality, the methodology led to detailed discontinuity information with several major and minor faults extracted automatically. This hybrid approach not only improved the fault tracking accuracy but also significantly reduced the fault interpretation time and associated uncertainty. It is equally helpful in detecting any seismic objects like fracture, chimneys, and stratigraphic features.

Delineating Plio-Pleistocene channels (gas reservoirs) via seismic attributes in Baltim Gas Field, Nile Delta, Egypt

“…Seismic attributes are characterized as the most informative tools to characterize seismic features, such as layer velocity, acoustic impedance inversion [4,24,41], and pore pressure prediction [37], after the emergence of intricate trace theory during the earlier phases of the 1970s, the initial application of seismic attribute analysis was directed towards the realm of oil exploration in the decades between the 1970s and 1980s [42], the term "attributes" pertains to measurements that are generated from seismic data, for instance, envelope amplitude (commonly referred to as "re ection strength"), velocity, instantaneous frequency, polarity, instantaneous phase, slope, slope azimuth, seismic interpretation methods have made it conceivable to calculate volumes of structural attributes and augment faults in seismic data due to automated fault extraction using a variety of seismic features [25].…”

Facies analysis and distribution of Late Palaeogene deep‐water massive sandstones in submarine‐fan lobes, NW Borneo

“…The geology of South‐East Asia is considered a vital avenue for hydrocarbon generation and petroleum production in the Tertiary sedimentary successions (Al‐Masgari et al, 2021; Imran et al, 2020, 2021; Usman, Siddiqui, Zhang, et al, 2020). The generation of large amounts of sediments from various local sources and their deposition in the deep‐marine environments is believed to be associated with the complex geology of Borneo in the Tertiary period (Ahmed, Siddiqui, Rahman, et al, 2021; Ahmed, Siddiqui, Ramasamy, et al, 2021; Hall et al, 2008; Ogawa & Back, 2022; Siddiqui et al, 2017; Usman, Zhang, Siddiqui, & Jamaluddin., 2020).…”

An Improved Seismic Fault Interpretation in a Structurally Complex Geologic Setting Using a Pretrained CNN Model and Seismic Attributes: An Example from the Browse Basin, Australia

“…Some traditional methods have been able to find faults by analyzing and calculating some attributes dependent on lateral discontinuities in 2D and 3D seismic images [10,11]. Many researchers have utilized different attributes for detecting faults such as the curvature [2,12], variance [13,14], semblance [4,15], coherency [10,[16][17][18], eigenstructure [17,19], fault likelihood [20], similarity [8,21,22], entropy [23], flexure [3,24,25], gradient magnitude [26], chaos [26,27] and derivatives [3,18,28,29]. In earlier research, Rijks et al presented how the azimuth and dip magnitude may reveal very tiny faults with movement substantially lower than that of a seismic wavelet [30].…”