143 A Showcase of Student Research in Geoinformatics and Data Science (Posters)

Students are invited to present original research posters relevant to geoinformatics and data science, including geospatial analysis, machine learning, big data, modeling, and visualization. Posters will be judged for divisional awards.

Monday, 20 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

Determining Potential Sources of Lithium Enrichment in Smackover Formation Brines in the Ark-La-Tex Region

Paper number: 143-5
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

Abstract

Lithium (Li) is a critical element that plays a vital role in rechargeable lithium batteries for electric vehicles, energy storage systems, and electronic devices. However, little is known about the causation of lithium enrichment in brines. The Late Jurassic Smackover Limestone Formation of the Arkansas-Louisiana-Texas (Ark-La-Tex) region, USA, contains lithium-concentrated brine within the Reynold oolite (upper Smackover) and middle members of the formation, ranging from ~ 1 to 477 milligrams per liter (mg/L). There are several leading hypotheses exist for lithium enrichment in Smackover brines: (1) Extensive evaporation of Jurassic seawater concentrated lithium in residual brines after evaporite precipitation, with subsequent movement into the Smackover; (2) Lithium-rich fluids originating from deeper crystalline basement rocks migrated upward along structural pathways; (3) Diagenetic alteration via dolomitization fluids; (4) Lithium-rich fluids coming up from the Eagle Mills Formation through faults; or (5) Degradation of organic-rich intervals facilitated lithium mobilization.

This research compiles oilfield brine and geological data from the Smackover and surrounding geologic formations, including the Louann Salt, Haynesville Formation, and Cotton Valley Group, with a focus on formation thickness, salt diapir locations, brine chemistry, fault distribution, and igneous intrusions. A random forest machine-learning model is used to understand the connection between lithium enrichment, geological structures, and rock properties. Previous studies have applied machine learning to this play, but only within a limited area and focused solely on predicting lithium concentrations in brine. This research expands the scope to a broader region and incorporates geospatial correlations. Here, we present our preliminary maps, which highlight lithium-rich areas and help us understand the possible sources and distribution of lithium in Smackover Formation brines.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413984

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