Petrophysical challenges with mudrocks in the Permian Basin: New concepts and workflows
Author:
Affiliation:
1. The University of Texas at Austin
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Link
https://library.seg.org/doi/pdf/10.1190/image2022-3746761.1
Reference25 articles.
1. Al-Obaidi, M., and Z. Heidari, 2018, Automatic well-log-based fabric-oriented rock classification for optimizing landing spots and completion intervals in the Midland Basin: Presented at the 59th Annual Logging Symposium, SPWLA, SPWLA-2018-CCCC.
2. Automated detection of bad borehole sections and evaluation of log quality using Toeplitz Inverse Covariance-based Clustering
3. Shear wave slowness prediction integrating unsupervised multivariate time series clustering and ensemble class-based machine learning
4. Integrated detection and investigation of bad borehole section in the Wolfcamp Formation in the Midland Basin using machine learning, petrophysics, and core characterization
5. Developing Predictive Power in the Permian: Leveraging Advanced Petrophysics to Deliver Cash to the Business
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1. Generating 3D Lithology Probability Volumes Using Poststack Inversion, Probabilistic Neural Networks, and Bayesian Classification A Case Study from the Mixed Carbonate Siliciclastic Deposits of the Cisco Group of the Eastern Shelf of the Permian Basin, North - Central Texas;GEOPHYSICS;2023-11-22
2. Thin Bed Analysis Workflow for Low Resistivity Pay in Clastic Reservoirs in the Permian;Day 3 Wed, October 18, 2023;2023-10-09
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