Gildin, Eduardo
individual record
Professor
Positions:
- Faculty Affiliate, Energy Institute, Texas A&M Engineering Experiment Station (TEES), Division of Research
- Professor, Petroleum Engineering, College of Engineering
UN Sustainable Development Goals:
UN Sustainable Development Goals:
overview
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
- Ph.D. in Aerospace Engineering, The University of Texas at Austin - (Austin, Texas, United States) 2006
- M.S. in Mechanical Engineering, University of Sao Paulo - (São Paulo, Brazil) 1998
- B.S. in Mechanical Engineering, Centro Universitario da FEI - (São Bernardo do Campo, Brazil) 1995
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.
- 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.
- Zalavadia, H., & Gildin, E. (2021). Two-Step Predict and Correct Non-Intrusive Parametric Model Order Reduction for Changing Well Locations Using a Machine Learning Framework. Energies. 14(6), 1765-1765.
- Zalavadia, H., & Gildin, E. (2020). Non-Intrusive Parametric Model Order Reduction with Error Correction Modeling for Changing Well Locations Using a Machine Learning Framework. Day 1 Mon, July 27, 2020. abs/2001.05061,
Chapters1
- 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.
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.
- 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.
- 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.
- 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.
in the news
- Texas A&M researchers create advanced drilling advisory system that opens door to autonomous drilling World Oil January 9, 2020
researcher on
Principal Investigator3
awards and honors
recent teaching activities
- ENGR684 Professional Internshp Instructor
- ICPE689 Sptp: Data Sci Intel Oil Fd Op Instructor
- ICPE689 Sptp:da Sci For Oil Fld Oper Instructor
- ITDE684 Professional Internship Instructor
- ITDE684 Professional Internship: In-ab Instructor
chaired theses and dissertations
- Agharzayeva, Zinyat (2018-08). Application of Machine Learning and Data Analytics Methods to Detect Interference Effects from Offset Wells. (Master's Thesis)
- Al Jawad, Murtada Saleh H (2018-08). Development of a Fully Integrated Acid Fracture Model. (Doctoral Dissertation)
- Balogun, Oluwafemi Opeyemi (2017-12). New History Matching Methodology for Two Phase Reservoir Using Expectation-Maximization (EM) Algorithm. (Doctoral Dissertation)
- Tarakanov, Alexander (2017-12). Impact of PVT Properties of the Fluid on the LBM Scheme Within the Scale Integration for Shale Reservoirs. (Doctoral Dissertation)
- Suranetinai, Chaiyaporn (2017-08). Integrated Field Optimization on UNISIM-I-D Benchmark Case. (Master's Thesis)
Email
egildin@tamu.edu
First Name
Eduardo
Last Name
Gildin
mailing address
Texas A&M University; Petroleum Engineering; 3116 TAMU
College Station, TX 77843-3116
USA
UN Sustainable Development Goals:
UN Sustainable Development Goals: