Simulation of Reservoir Charge Over Geologic Time to Predict Present-Day Spatial Distributions of Fluid Composition
Westside Houston
Speaker:
Seminar Date: Nov 14 2024
Registration Opens: Oct 11 2024 - Nov 15 2024
Time: 11:30 AM - 01:00 PM (US CDT)
Admission/Registration Link: None
Donation Link: None
Meeting/Webinar Link: None
Contact: QinShan “Shan” Yang (VP Westside, SPWLA Houston Chapter)
Corresponding: vpwestside@spwla-houston.org
Fees: FREENOTES:
Speaker : Tarek S. Mohamed (SLB)
Date : Thursday, Nov 14th , 2024
Time : 11:30 am – 1:00 pm (US CDT)
Venue : SLB, 6350 West Sam Houston Parkway North, Houston, TX 77041
Admission : This activity will include a boxed lunch.
The seminar is sponsored by SLB so there is no charge for registration.
However, you still need to register using the applicable links below
Parking Info : Guest parking is available free of charge. Upon arrival, please proceed to the front desk to check in
Please register by Nov 13th, 2024 @ 11am to reserve lunch using the above provided link.
Contact : QinShan (Shan) Yang (SPWLA Houston VP Westside)
Corresponding : vpwestside@spwla-houston.org
ABSTRACT:
Reservoir fluids often exhibit compositional complexity vertically and laterally in reservoirs. These complexities include viscous oil and tar distributions, and gas-oil ratios and can also include more subtle fluid variations such as varying biomarker ratios and isotopic ratios. Recent advances have led to resolving of many mixing dynamic processes of reservoir charge fluids over geologic time. The objective is to simulate reservoir charge over geologic time to (a) constrain key attributes of the reservoir which comprise the geologic model and (b) to improve the prediction of fluid properties across tectonic features. The analysis of 80 reservoirs within the context of reservoir fluid geodynamics has allowed identification of mass transport and mixing dynamics of different charge fluids over geologic time. Reservoir simulation can be used to predict resulting compositional distributions; these predictions depend on (1) reservoir attributes, both known and uncertain, (2) the properties and locations of charge fluids, such as density and viscosity, and (3) the time since charge. The comparison of predicted and measured fluid distributions allows history matching of reservoir charge. Fluid mechanics principles are shown to validate simulation results building confidence in their predictions. Forward modeling with reservoir simulation shows that even simple 2D simulations can illuminate key reservoir attributes that impact fluid compositional distributions such as connectivity and baffling especially over different areal sections of the reservoir. Reservoir case studies are used to validate the charge and mixing dynamics that are employed in modeling. Reservoir simulation shows that a substantial range of the extent of mixing is found dependent on reservoir and fluid properties, thereby providing a very sensitive test of these reservoir parameters. Simulation of reservoir charge for history matching is a very new concept, yet it relies on standard reservoir simulation (over geologic time) for comparison between predicted vs measured fluid compositional distributions of present day to test the reservoir and geologic models. This approach has shown that several presumptions about mixing of charge fluids were not general and inhibited the new workflow. Removing such conceptual limitations has been crucial to developing novel workflows.
BIOGRAPHY:
Tarek S. Mohamed is an Interpretation development engineer at SLB. He co-leads the new direction of modeling fluid dynamic mixing processes and history-matching reservoir charge over geologic time to predict fluid spatial compositional distributions in untapped regions and to test geologic models. He is the co-author of over 16 technical papers accepted by 7 organizations including SPWLA, SPE, SEG, AAPG, and ACS. He completed projects with collaborators from academia, the energy industry, and Los Alamos National Laboratory. He holds a PhD in petroleum engineering from the University of Texas at Austin, an MS in petroleum engineering and a graduate certificate in data science and analytics from the University of Oklahoma, and a BS in petroleum engineering from Suez University.