My primary research activities focus on developing Bayesian inversion strategies for extracting information from seismic survey data. In particular, I investigate the use of seismic attenuation in these inversion strategies because instrinsic attenuation is theoretically linked more strongly to the hydraulic properties of a formation (i.e., porosity, permeability, saturation) than velocity. The non-linearity and multimodality of the rock physics equations that model attenuation warrant stochastic inversion techniques, and a Bayesian framework allows a convenient expression of prior geologic knowledge, as well as the predictions, in a probabilistic way. These methods can achieve a better characterization of reservoirs in the exploration phase.
I am also interested in solving problems in the oil and gas industry using statistics, BIG data analytics, and machine learning. A large focus is on well production data, where my group is using time series statistics, geostatistics, and pattern recognition techniques to improve forecasting and operational decision support.
- Participating Member, National Risk Assessment Partnership 2018-2020
- Member, Organizing Committee – IAMG Conference 2019
- Guest Editor, Mathematical Geosciences, IAMG 2019 Special Issue
- Scholarship Chair, SPE Pittsburgh Section 2014-2019
- Faculty Advisor, Penn State SPE Student Chapter 2014-2018
- Joon, S, Dawuda, I, Morgan, E, and Srinivasan, S (2022). Rock Physics-Based Data Assimilation of Integrated Continuous Active-Source Seismic and Pressure Monitoring Data during GCS. SPE Journal SPE-209585-PA (in press; posted 28 February 2022). doi:10.2118/209585-PA
- Udegbe, E, Morgan, EC, and Srinivasan, S (2019). Big Data Analytics for Seismic Fracture Identification, Using Amplitude-Based Statistics.Computational Geosciences: 11-15. doi:10.1007/s10596-019-09890-z
- Xi, Z, and Morgan, EC (2019). Combining Decline Curve Analysis and Geostatistics to Forecast Gas Production in the Marcellus Shale. SPE Reservoir Evaluation & Engineering -Formation Evaluation. doi:10.2118/197055-PA
- Udegbe, E, Morgan, EC, and Srinivasan, S (2019). Big Data Analytics for Production Data Classification using Feature Detection: Application to Restimulation Candidate Selection. SPE Reservoir Evaluation & Engineering -Formation Evaluation. doi:10.2118/187328-PA
- Morgan, EC, Vanneste, M, Lecomte, I, Baise, LG, Longva, O, and McAdoo, BG (2012). Estimation of free gas saturation from seismic reflection surveys by the genetic algorithm inversion of a P-wave attenuation model. Geophysics, 77(4): R175-R187. doi:10.1190/geo2011-0291.1
- Thompson, EM, Baise, LG, Kayen, RE, Morgan, EC, and Kaklamanos, J (2011). Integrated multiscale site response mapping. Bulletin of the Seismological Society of America, 101(3): 1081-1100. doi:10.1785/0120100211
- Morgan, EC, Lackner, M, Vogel, RM, and Baise, LG (2011). Probability distributions for offshore wind speeds. Energy Conversion and Management, 52(1): 15-26. doi:10.1016/j.enconman.2010.06.015
- Morgan, EC, McAdoo, BG, and Baise, LG (2008). Quantifying geomorphology associated with large subduction zone earthquakes. Basin Research, 20(4): 531-542. doi:10.1111/j.1365-2117.2008.00368.x
- Hoffmann, G, Silver, E, Day, S, Morgan, EC, Driscoll, NW, and Orange, D (2008). Sediment waves in the Bismarck volcanic arc, Papua New Guinea. Special Paper Geological Society of America, 436: 91-126. doi:10.1130/2008.2436(05)