Subsurface reservoir modeling to explore, develop, and optimize production of lithium-rich brine resources.
LithiumBank, an SLB customer in Western Canada, needed detailed aquifer modeling for a required lithium resource estimation and preliminary economic assessment of a major lithium extraction project. The area is historically known for having the highest lithium-in-brine concentrations in Alberta, so the study would help to better understand lithium-inferred estimations over the area of interest.
The data stack consisted of 2D and 3D seismic data, well logs, production information, and input from regional basin modeling. The study involved reservoir characterization, facies modeling, porosity and permeability modeling, inversion, interpretation of seismic data, and identification of lithium brine in place from concentration data. Petrel™ subsurface software and subsurface modeling interpretation services enabled LithiumBank to measure the quantity of lithium in place within the area of interest. Petrophysics harmonized the log data and computed porosity logs at each well location through different log types. Petrel software performed inversion on 3D and 2D data while using well logs to calibrate and guide the inversion process. The software also integrated all the porosity logs within the area of interest with 3D seismic and 2D inversion volumes, and a porosity cube was created over the target zone to estimate total brine volumes and resources in place.
The cross section below shows porosity distribution over target brines within the Leduc and Swan Hills formation intervals.
This project aided in lithium exploration and development activities for LithiumBank using multiphysics and multipetrophysical applications—uncovering the largest known lithium brine resource in North America, including the highest resource grades in Western Canadian brines. The findings and interpretations of the detailed 3D static model will provide a regulatory resource estimate as per NI 43-101 and enable additional technical work pertaining to planning well networks, field assets, economic, and engineering studies. This combination of approaches was the first of its kind for a lithium brine resource study and will be used for dynamic modeling and brine field development modeling in the project's next phases.
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