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

Abstract

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.

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

“…These NNW-SSE faults formed due to the transtension amid the collision of the Eurasian and Indian plates [45]. The orientation of the fault within the SGF is NNW-SSE oriented, while most of the fractures are characterized in the SE-NW direction [27]. The blue rectangle highlights the zone of interest (ZOI) (modified from [35]).…”

“…(a) Regional geological map of Pakistan. The Sawan gas field (SGF) is located close to the Jacobabad-Khairpur High (JKH) north of the Lower Indus Basin (LIB) (highlighted by a blue square) [27]. (b) Location of the SGF showing the study wells on the imagery.…”

“…The proper and dependable estimation of low-impedance gas-bearing zones, estimation of reservoir parameters, and reservoir thickness calculation at sparse well locations in the Sawan gas field (SGF) is still a substantial task [26], and vast hydrocarbon potential remained unexplored [27]. In addition, numerous wells drilled thereabouts are declared dry or have been abandoned [28].…”

“…Various authors performed multiple post-stack inversion methods to calculate the reservoir parameters [33,34]. However, their studies were based only on attributes [27,28,31], inversion techniques [32][33][34], or well logging [29,35]. None of those studies have provided comprehensive research to incorporate all the necessary methods to present thorough research for classifying the reservoir thickness, quantitative and qualitative assessments of facies heterogeneity, and the factors controlling the reservoir heterogeneity of the Lower Goru sands (LGS).…”

“…Ref. [27] applied the machine learning tools to comprehend the faults and fractures of the LGF. Ref.…”