Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field, Scotian Basin

Abstract

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field, Scotian Basin. An integrated study of instantaneous frequency, P-impedance, volume of clay and neutron-porosity attributes, and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area. Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region. P-impedance was estimated using model-based seismic inversion. Petrophysical properties such as the neutron porosity (NPHI) and volume of clay (VCL) were estimated using the multilayer perceptron neural network with high accuracy. Comparatively, a combination of low instantaneous frequency (15–30 Hz), moderate to high impedance (7000–9500 gm/cc∗m/s), low neutron porosity (27%–40%) and low volume of clay (40%–60%), suggests fair-to-good sandstone development in the Dawson Canyon Formation. After calibration with the well-log data, it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons. The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition (Dawson Canyon Formation) in the Penobscot field, Scotian Basin. Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs. The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.