1. CONTROL ID: 2612726
TITLE: Unsupervised Seismic Facies Classifications of the Lower Strawn Fm. Bend Arch-Fort Worth Basin, TX
AUTHORS (FIRST NAME, LAST NAME): David Prado-Barros
1
, Roger Slatt
1
, Kurt J. Marfurt
1
INSTITUTIONS (ALL):
1. School of Geology & Geophysics, University of Oklahoma, Norman, OK, United States.
ABSTRACT BODY:
Abstract Body: The Bend Arch- Fort Worth Basin is a tectonically active area that exhibits a complex sediment
distribution along north-central Texas. While Pennsylvanian plays have been one of the main exploratory targets, the
depositional history and 3D seismic expression of the Desmoinesian time Lower Strawn Formation described by Gun
(1979) and Pranter (1989) as a turbidite / submarine fan complex is only partially documented. In this paper, we
subject a post-stack 3D seismic volume conditioned to structural oriented filtering and time-frequency domain based
spectral balancing from which we extract a set of attributes and P-impedance inversion. Appropriate attributes are
then used for unsupervised facies classification with the aim of geometrically differentiate the sand bodies and
delineate the architectural elements present along the target unit. After data conditioning, we compute a suite of
candidate attributes, including P-impedance inversion. The next “exploratory data analysis” step involved comparing
candidate attributes to areas where well control showed the desired sand to be present or absent. Our analysis
showed that several of the eight texture attributes that measure the lateral homogeneity, entropy, and other properties
of the reflector exhibited value. To simplify the classification, we reduced these eight measures into one represented
by the first principal component. The resulting five attributes used in classification were P-impedance, precondition
seismic amplitude, peak spectral frequency (from the time-frequency analysis), the first principal component of the
eight texture attributes, and the relative stratal location. We evaluated three different classification techniques with the
goal of compare a diverse set of results. K-means clustering analysis, Self-organizing Maps (SOM) and Principal
Component Analysis (PCA). The results show that some classification techniques can highlight architectural elements
stronger than other. Although K-means was able to highlight possible channels, point bars, scroll bars and crevasse
splays features, SOM and PCA were able to discriminate more subtle facies changes along a specific area.