Influence of process-based, stochastic and deterministic methods for representing heterogeneity in fluvial geothermal systems

Major, Márton; Daniilidis, Alexandros; Hansen, Thomas Mejer; Khait, Mark; Voskov, Denis

doi:10.1016/j.geothermics.2023.102651

Cited by 10 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more precise characterization of this aquitard was subsequently accomplished by employing a geostatistical inversion approach to calibrate the extensometer data (Zhuang et al, 2019). Recent studies have demonstrated the potential utility of tracer measurements to improve the characterization of subsurface heterogeneity in permeability (e.g., Lee et al, 2017;Jiang et al, 2021;Major et al, 2023). The primary factor lies in the high sensitivity of tracer transport to permeability (Babaei and Nick, 2019;Banafsheh et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

ZHUANG,

LÜ,

YAN

et al. 2024

Acta Geologica Sinica (Eng)

View full text Add to dashboard Cite

In groundwater hydrology, aquitard heterogeneity is often less concerned compared to aquifers, despite its significant impact on groundwater hydraulics and groundwater resources evaluation. A semi‐analytical solution was derived for pumping induced well hydraulics and groundwater budget with the consideration of vertical heterogeneity in aquitard hydraulic conductivity (K) and specific storage (Ss). The novelty of the new solution is its partitioning of the aquitard into multiple homogeneous sub‐layers to consider various forms of vertically heterogeneous K or Ss. Two scenarios of analytical investigations were explored. One is the presence of aquitard interlayers with distinct K or Ss values, a common field‐scale occurrence. Another is an exponentially depth‐decaying aquitard Ss, a regional‐scale phenomenon supported by statistical analysis. Analytical investigations reveal that a low‐K interlayer can significantly increase aquifer drawdown and enhance aquifer/aquitard depletion. Meanwhile, a high‐Ss interlayer can noticeably reduce aquifer drawdown and increase aquitard depletion. Locations of low‐K or high‐Ss interlayers also significantly impact well hydraulics and groundwater budget. In the context of an exponentially depth‐decaying aquitard Ss, a larger decay exponent can enhance aquifer drawdown. When using current models with a vertically homogeneous aquitard, half the sum of the geometric and harmonic means of exponentially depth‐decaying aquitard Ss should be used to calculate aquitard depletion and unconfined aquifer leakage.

show abstract

Section: Discussionmentioning

confidence: 99%

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

ZHUANG,

LÜ,

YAN

et al. 2024

Acta Geologica Sinica (Eng)

View full text Add to dashboard Cite

show abstract

“…Various machine learning methods were used to address the uncertainty in geothermal energy production [52][53][54]. However, AI-centric approaches for sequential decision making under uncertainty have not been covered.…”

Section: Artificial Intelligence-centric Field Development Planningmentioning

confidence: 99%

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

View full text Add to dashboard Cite

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.

show abstract

Probabilistic assessment of the thermal performance of low-enthalpy geothermal system under impact of spatially correlated heterogeneity by using XGBoost algorithms

Liao,

Xie,

Zhao

et al. 2024

Energy

View full text Add to dashboard Cite

Influence of process-based, stochastic and deterministic methods for representing heterogeneity in fluvial geothermal systems

Cited by 10 publications

References 42 publications

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

Pumping‐induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Probabilistic assessment of the thermal performance of low-enthalpy geothermal system under impact of spatially correlated heterogeneity by using XGBoost algorithms

Contact Info

Product

Resources

About