I have research interests in reservoir modeling and optimization for the oil and gas industry using concepts from mathematical modeling using discretization of pde's (finite difference, finite element methods, and finite volumes), systems and control theory and model reduction of large scale dynamical systems. In particular, I am interested in closed-loop reservoir management.

education and training
selected publications
Academic Articles35
  • Coutinho, E., Dall’Aqua, M., & Gildin, E. (2021). Physics-Aware Deep-Learning-Based Proxy Reservoir Simulation Model Equipped With State and Well Output Prediction. Frontiers in Applied Mathematics and Statistics. 7, 651178.
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  • Khaled, M., Srinivasan, S., Toure, A., Chen, M., Kincaid, E., Lopaz, T., ... Moridis, G. (2021). DREAMS: Drilling and Extraction Automated System. CoRR. abs/2106.05874,
  • Brankovic, M., Gildin, E., Gibson, R. L., & Everett, M. E. (2021). A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm. Applied Sciences. 11(11), 4874-4874.
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  • Kheriji, W., Efendiev, Y., Manuel Calo, V., & Gildin, E. (2017). Model Reduction for Coupled Near-Well and Reservoir Models Using Multiple Space-Time Discretizations. Model Reduction of Parametrized Systems. 471-490. Springer International Publishing.
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Conference Papers25
  • Ramos Gurjao, K. G., Gildin, E., Gibson, R., & Everett, M. (2022). Estimation of Far-Field Fiber Optics Distributed Acoustic Sensing DAS Response Using Spatio-Temporal Machine Learning Schemes and Improvement of Hydraulic Fracture Geometric Characterization. Day 1 Tue, February 01, 2022, SPE Hydraulic Fracturing Technology Conference and Exhibition.
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  • Vishnumolakala, N., Murphy, D. M., Nguyen, T., Losoya, E. Z., Kesireddy, V. R., & Gildin, E. (2021). Predicting Dysfunction Vibration Events while Drilling Using LSTM Recurrent Neural Networks. Day 3 Thu, October 14, 2021, SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition.
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  • Ramos Gurjao, K. G., Gildin, E., Gibson, R., & Everett, M. (2021). Mechanistic Modeling of Distributed Strain Sensing DSS and Distributed Acoustic Sensing DAS to Assist Machine Learning Schemes Interpreting Unconventional Reservoir Datasets. Day 2 Wed, September 22, 2021, SPE Annual Technical Conference and Exhibition.
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  • Z. Losoya, E., Vishnumolakala, N., Noynaert, S. F., Medina-Cetina, Z., Bukkapatnam, S., & Gildin, E. (2021). Automatic Identification of Rock Formation Type While Drilling Using Machine Learning Based Data-Driven Models. Day 1 Tue, June 08, 2021, IADC/SPE Asia Pacific Drilling Technology Conference.
  • Pradhan, Y., Gildin, E., & Blasingame, T. A. (2021). Drawdown Management Strategies — Midland Basin Case Studies. Proceedings of the 9th Unconventional Resources Technology Conference, Unconventional Resources Technology Conference.
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chaired theses and dissertations
First Name
Last Name
mailing address
Texas A&M University; Petroleum Engineering; 3116 TAMU
College Station, TX 77843-3116