Artificial Intelligent Approaches in Petroleum Geosciences

Constantin Cranganu is a professor of geophysics and petroleum geology at Brooklyn College of the City University of New York. He obtained a Ph.D. degree (ABD) from the University of Bucharest, Romania (1993), in geophysics and another Ph.D. from the University of Oklahoma (1997) in geology. Before coming to Brooklyn College, he worked at “Al. I. Cuza” University of Iasi, Romania, and the School of Geology and Geophysics of University of Oklahoma. His main research covers various areas of petroleum geosciences: oil and gas generation, abnormal fluid pressures in sedimentary basins, gas hydrate exploitation, identification of gas-bearing layers using well logs, geostatistics, etc. Lately, Prof. Cranganu started using artificial intelligent approaches in his petroleum-related research. He published many books, peer-reviewed articles, book reviews, and essays. His paper, “Using gene expression programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin, Oklahoma”, (co-author Elena Bautu), published in Journal of Petroleum Science and Engineering in 2012 was nominated for ENI Awards 2012.In 2014, he was the author and the senior editor of “Artificial Intelligent Approaches in Petroleum Geosciences”, Springer, 1st edition.

• Preface to the 2nd edition.- Preface to the 1st Edition.- 1. Applications of Data-Driven Techniques in Reservoir Modeling and Management.- Part 1: Waterflooding.- Part 2: Water Alternating Gas Injection, CO2 Storage, and Property Estimations.- 2. Comparison of three machine learning approaches in determining Total Organic Carbon (TOC): A case study from Marcellus shale formation, New York state.- 3. Gated Recurrent Units for Lithofacies Classification based on Seismic Inversion.- 4. Application of Artificial Neural Networks in Geoscience and Petroleum Industry.- 5. On Support Vector Regression to Predict Poisson’s Ratio and Young’s Modulus of Reservoir Rock.- 6. Use of Active Learning Method to Determine the Presence and Estimate the Magnitude of Abnormally Pressured Fluid Zones: A Case Study from the Anadarko Basin, Oklahoma.- 7. Active Learning Method for Estimating Missing Logs in Hydrocarbon Reservoirs.- 8. Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir.- 9. Well Log Analysis by Global Optimization-based Interval Inversion Method.- 10. Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview.- Index.