Seismic attribute analysis of the Upper Morrow Sandstone and the Arbuckle Group from 3D-3C seismic data at Cutter Field, southwest Kansas

Arbuckle Group and Upper Morrow Sandstone reservoirs have pronounced economic and environmental importance to the state of Kansas because of their history of oil production and potential for CO2 storage. Characterizing and delineating these reservoirs with seismic methods is challenging for a number of geophysical reasons. This study investigates the accuracy with which analysis of post-stack 3D-3C seismic data can delineate Upper Morrow Sandstone reservoirs and predict Arbuckle Group rock properties at Cutter Field in southwest Kansas. P-P and P-SV seismic responses of the Upper Morrow Sandstone and Arbuckle Group are modeled using Zoeppritz’ equations and P-impedance inversion is performed. Seismic attributes are extracted at well locations and compared to models. The Upper Morrow Sandstone is below resolution of both the P-P and P-SV data. No significant correlation is evident between amplitudes or inverted P-impedance and Upper Morrow Sandstone thickness. Instantaneous frequency values of 43 ± 2 Hz are observed at well locations where Upper Morrow Sandstone thickness is greater than 5 m whereas values of 45 ± 6 Hz are observed at well locations where thickness is less than 5 m. The difference in the rms instantaneous frequency values is statistically significant at the 90% confidence interval. Well log data from the Arbuckle Group shows an approximate neutron porosity range of 3-13% and an inverse correlation between neutron porosity and P-impedance, significant at the 99.9% confidence interval with a standard error of regression of 2% porosity. Model-based P-impedance inversion and results and flow unit interpretation from well log data suggest that porosity and flow units within the Arbuckle Group can be approximated by a three-layer model. Investigators can draw upon the results of this study to guide seismic acquisition and interpretation practices in geologic settings analogous to Cutter Field

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks

The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3% recall error rate on an example docking task. Our open-source code is available at https://github.com/ryienh/graph-dock.Comment: Published as workshop paper at NeurIPS 2022 (AI for Science

1963: Abilene Christian College Bible Lectures - Full Text

THE CHRISTIAN AND MORALITY” Being the Abilene Christian College Annual Bible Lectures 1963 Price: $3.95 Published by ABILENE CHRISTIAN COLLEGE STUDENTS EXCHANGE ACC Station Abilene, Texa