Application of GMDH to Predict Pore Pressure from Well Logs Data: A Case Study from Southeast Sichuan Basin, China

• Published in: Natural resources research (New York, N.Y.) Vol. 32; no. 4; pp. 1711 - 1731
• Main Authors: Mgimba, Melckzedeck M., Jiang, Shu, Nyakilla, Edwin E., Mwakipunda, Grant Charles
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2023; Springer Nature B.V
• Subjects: Artificial intelligence; Artificial neural networks; Back propagation; basins; Case studies; Chemistry and Earth Sciences; China; clay; Clay minerals; Computer Science; cost effectiveness; Data structures; Dehydration; Earth and Environmental Science; Earth Sciences; Fossil Fuels (incl. Carbon Capture); Gases; Geography; Group method of data handling; High temperature; industry; Lateral transfers; Learning algorithms; Lithology; Machine learning; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mineral Resources; Neural networks; Original Paper; Parameters; petroleum; Petroleum industry; Physics; Polynomials; Pore pressure; prediction; Pressure; Root-mean-square errors; Statistics for Engineering; Support vector machines; Sustainable Development; temperature; Thermal expansion; Velocity; Well logs; Sichuan Basin; China; Sichuan China; means clustering; GMDH; Well logs; Pore pressure; Machine learning

Pore pressure prediction is significant in the petroleum industry because, compared to direct measurement, it is cost-effective and it generates an extensive range of data. Mathematical correlations fail to predict pore pressure due to their failure to include lateral transfer in the reservoir, high...