An integrated, mesh-independent geothermal modelling framework

O’Sullivan, John; Popineau, Joris; Gravatt, Michael; Renaud, Théo; Riffault, Jeremy; Croucher, Adrian; Yeh, Angus; O’Sullivan, Michael

doi:10.1016/j.envsoft.2023.105666

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerical simulation is a powerful instrument that allows for a robust 3-D characterization of subsurface permeability, porosity, heat, and mass input parameters. This study followed the modeling framework established by O'Sullivan et al (2023).…”

Section: Numerical Model Designmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing the Geothermal Lithium Resource at the Salton Sea

Dobson,

Araya,

Brounce

et al. 2023

View full text Add to dashboard Cite

The energy transition towards a more sustainable and renewable future is a pivotal global endeavor. Central to this shift for the United States is the critical role of domestically sourced lithium, a key mineral in the production of high-performance batteries essential for electric vehicles and renewable energy storage systems. This has driven the United States to invest heavily in a domestic supply chain for batterygrade lithium to enhance energy security, reduce supply chain vulnerabilities, and foster economic growth by tapping into local resources. A notable example is the Biden Administration's "American Battery Materials Initiative," which was included in the $2.8-billion Bipartisan Infrastructure Law (The White House, 2022).The "Salton Sea Known Geothermal Resource Area" in Imperial County, California has been identified as a potential domestic U.S. resource of lithium due to the brine-hosted lithium in the deep subsurface geothermal reservoir. An analysis funded by the U.S. Department of Energy provides an overview of opportunities and challenges associated with developing the lithium resource in the Salton Sea geothermal reservoir, as well as potential environmental and societal impacts to the county and surrounding region.The geologic history of the region suggests that lithium in the subsurface brines could have come from multiple sources, including water and sediments from the Colorado River, which have been periodically deposited over the past several million years; rocks from the mountain ranges surrounding the Imperial Valley; and lithium-bearing volcanic rocks and igneous intrusions from past geologic events. Further, several processes may have concentrated lithium in the brine over time, including evaporative concentration of lithium-bearing water that flowed into the basin and leaching of lithium from the sediments and rocks by the circulating geothermal brines.Geothermal brine production at the Salton Sea Geothermal Field, the area with existing geothermal power plants, has averaged just over 120 million metric tons per year since 2004. Using an approximate lithium brine concentration of 198 parts per million (ppm), the amount of dissolved lithium contained in these produced brines is estimated to be 127,000 metric tons of lithium carbonate equivalent (LCE) per year. The total dissolved lithium content in the well-characterized portion of the Salton Sea Geothermal Reservoir is estimated at 4.1 million metric tons of LCE, and the estimated total resource increases to 18 million metric tons of LCE if assumptions for porosity and total reservoir size are increased to reflect the probable resource extent.Analysts measured lithium concentrations in the reservoir rocks, which were shown to vary with depth and mineralogy. These data were used to help refine conceptual and computer models of the reservoir; specifically, two complementary computer models of the reservoir were developed. Analysts used the first model to simulate the approximate 30-year history of geothermal power production in the area u...

show abstract

Section: Numerical Model Designmentioning

confidence: 99%

“…The production model was set up using our standardized framework for including production and reinjection wells (O'Sullivan et al, 2023). This approach adds wells as time-dependent source and sink terms in the model blocks corresponding to the feed zones of the production and reinjection wells.…”

Section: Production Modelmentioning

confidence: 99%