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Characterize. Predict. Perform: The Future of Reservoir Engineering

Monday, 4 May
Room 604
Technical Session
Reservoir engineering is entering a transformative era defined by the triad of characterization, prediction, and performance optimization. Advances in subsurface imaging, data analytics, and machine learning are enabling unprecedented levels of reservoir characterization, integrating geological, and petrophysical data into unified models. This session will address a broad spectrum of advanced reservoir engineering topics, including methodologies for identifying formation water in water-based mud wells, the use of generative AI in facies modeling, low-salinity waterflooding in carbonate reservoirs, reservoir property modeling in ultra-thin interbedded sand–shale sequences, and high-resolution reservoir characterization illustrated through case studies from Myanmar and Ecuador.
Chairperson(s)
Elive Menyoli, Geophysics Advisor - GOA Asset Optimization - Hess Corporation
Mariela Araujo Fresky, Open Innovation - Principal Researcher - Shell
Sponsoring Society:
  • American Institute of Chemical Engineers (AIChE)
  • 1030-1048 36753
    An Innovative Solution For Establishing Standard Of Identifying Formation Water During Formation Testing In Water Based Mud Wells For Low Permeability Formation
    G. Rao, SLB
  • 1050-1108 36887
    Generative Ai For Reservoir Characterization: Parameterization And Facies Modeling With Gans And Vaes
    M. Valiyev, University of Southern California
  • 1110-1128 36941
    Case Study: First Formation Testing & Sampling While Drilling (ftwd) Run In Ecuador Providing Reservoir Insight In Complex Well Profile.
    M. Guerrero, Halliburton
  • 1130-1148 36741
    Advanced Formation Saturation Evaluation with Complex Gravel Pack Completion for Reservoir Surveillance: A Case Study from Southeast Asia
    F. Ab Sukor, A. Azman, Weatherford
  • 1150-1208 37223
    Enhancing Oil Recovery In Carbonate Reservoirs Through Low Salinity Waterflooding: Laboratory Insights, Smart Brine Optimization, And Field-scale Validation
    V.R. Srivastava, A. Thapliyal, Oil & Natural Gas Corp. Ltd.
  • 1210-1228 37178
    Residual Trapping And Solubility Trapping In Geological Carbon Storage
    V. Kumar, Texas A & M University; S. Misra, O. Talabi, G. Ren, Texas A&M University; U. Odi, A. Silver, Aramco Americas; C. Temizel, Saudi Aramco
  • Alternate 36793
    In-Situ Biot's Effective Stress-Strain Modeling for Formation Damage Diagnostics with Quadratic Gradient Induced-Turbulence
    F. Fernandes, Petrobras and Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
  • Alternate 36854
    A Predictive Correlation For Temperature-dependent Porosity Recalculation Under Reservoir Conditions
    E. Abdullah, A. Yousef, King Fahd University of Petroleum & Minerals; R. Alodily, NESR; O. Alade, King Fahd University of Petroleum & Minerals

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