Analysis of the Pipeline dataset, a high-resolution 3D seismic volume, in the Taranaki Basin, Western New Zealand, allows for the identification and characterization of several deepwater architectural elements within a channel complex. This study focuses on the delineation and characterization of architectural elements using seismic attributes and unsupervised machine learning techniques such as self-organized maps (SOM). These techniques provide a quick and detailed interpretation to better understand the geomorphology and distribution of the seismic facies within the channel complex. Seismic attributes such as sweetness, co-rendered with a sobel filter (coherence) allow for the delineation of the geometry of the channel complex and identification of possible sand-rich lithofacies. In addition, we explore the application of clustering analyses to discriminate seismic facies within each channel complex. Three predominant groups are obtained using this classification technique a) Sand-rich deposits b) Siltstone deposits c) Mud-rich deposits. The proper selection of input seismic attributes in SOM along with the use of display options such as horizon probes proves to be a quick workflow to classify and characterize facies within reservoirs.
Presentation Date: Wednesday, September 18, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:30 PM
Location: Poster Station 4
Presentation Type: Poster
