Discrete 3D fracture network extraction and characterization from 3D seismic data — A case study at Teapot Dome

Abstract

Three-dimensional discrete fracture networks (DFNs) extracted from the seismic data of the Tensleep Formation at Teapot Dome successfully matched 1D fracture data from multiple boreholes within the area. The extraction process used four seismic attributes, i.e., variance, chaos, curvature, and spectral edge, and their multiple realizations to define seismic discontinuities that could potentially represent fractures within the Tensleep Formation. All of the potential fracture attributes were further enhanced using a fracture-tracking attribute for better extraction and analysis of seismic discontinuity surfaces and their network properties. A state-of-the-art discontinuity surface extraction and characterization workflow uniformly extracted and interactively characterized the seismic discontinuity surfaces and networks that correlate with borehole fracture data. Among the attributes, a fracture-tracking attribute cube created out of the high-resolution spectral-edge attribute provided the best match with the borehole fracture data from the Tensleep Formation. Therefore, the extracted discontinuity planes were classified as fractures and then characterized. The extracted fracture population also matched earlier published records of faults and fractures at Teapot Dome. Unlike the conventional method, which uses 1D borehole fracture data as primary input and 3D seismic data as a guide volume during DFN modeling, I used 3D seismic attributes as the primary data and the 1D borehole fracture data only for quality control. I also evaluated the power of converting seismic fracture attribute volumes into discrete surfaces and networks for effective correlation with 1D fracture logs from boreholes.

Effective Permeability in Fractured Reservoirs: Discrete Fracture Matrix Simulations and Percolation‐Based Effective‐Medium Approximation

“…To further generate a polydisperse fracture network that matched the experimental data, we set κ = 0.1 and β = 45 ° . Although seismic data were used to extract and characterize fracture networks (Thachaparambil, 2015), capturing all properties of matrix‐fracture systems in subsurface is extremely difficult. Therefore, our aim was not generating fracture networks observed exactly in nature but statistically representative ones.…”

Interpretation of fractured zones using seismic attributes — Case study from Teapot Dome, Wyoming, USA

“…Comparison of fracture strike and dip of image log data (orange) with maximum azimuth and dip of GLCM-based attributes energy (blue), entropy (green), homogeneity (rose), contrast (turquoise), variance (violet), and dissimilarity (yellow). The last column shows the results of Thachaparambil (2015), where spectral edge attribute calculations were converted into discrete objects called seismic discontinuity planes (SDPs) to compare their azimuth and dip with image log data.…”

“…The effectiveness of an attribute in DFN modeling can be judged by comparison with image log data (Thachaparambil, 2015). The GLCM-based attribute calculations were used to generate fracture strikes and dips that were plotted as rose diagrams to compare with diagrams based on image logs (Figure 11).…”

“…Previous fracture studies of the Tensleep Formation include analog field studies at Alcova Anticline (Gilbertson, 2006) and Fremont Canyon (Zhang, 2005;Wilson et al, 2013bWilson et al, , 2015. Seismic attribute analyses have been carried out by Ouenes et al (2010), Gao (2013), Wilson et al (2012Wilson et al ( , 2013aWilson et al ( , 2013bWilson et al ( , 2015, Di andGao (2014), andThachaparambil (2015). A study of open fractures seen on five image logs through the Tensleep Formation was done by Schwartz (2006).…”

Seismic attributes from the complex Teager-Kaiser energy

“…In this study, we focus on the Tensleep formation which is one of the reservoir intervals and consists of naturally fractured sandstones. Several studies of fracturing in Tensleep formation using seismic attributes have been already published (Gao et al, 2011;Thachaparambil, 2015;Schneider et al, 2016). Here, we just want to show that the TK energy and the TKV attribute help the interpretation of this formation.…”