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Lithuania is located on the East of Baltic sedimentary basin and has a geothermal anomaly situated in the southwestern region of the country. There are two primary geothermal complexes within the anomaly, composed of Cambrian and Devonian aquifers. The Cambrian formation is composed of sandstones that have a reservoir temperature reaching up to 96 °C (depth > 2000 m). The Devonian aquifer is composed of unconsolidated sands of Parnu–Kemeri and has a reservoir temperature of up to 46 °C (depth > 1000 m). Historically, both formations have been investigated for geothermal energy production. In this article, we present a detailed literature review of the geothermal work carried out on both formations, including past, present, and some possible future studies. The study presented in this paper highlights the key findings of previous research work, summarizes the research gaps, and then elaborates on the possible applications of emerging technologies to bridge the research gaps and improve our understanding of geothermal complexes in Lithuania. Although it is not the main aim of this article, this article also touches upon the important need to develop 2D/3D numerical models, to quantify uncertainties, in the evaluation of the geothermal potential in Lithuania for commercial development. This study also highlights possibilities of extending geothermal development to depleted hydrocarbon reservoirs through repurposing the high-water-production wells. Moreover, from the literature review, it can be concluded that the Lithuanian geothermal aquifers are hyper-saline in nature and temperature changes lead to the deposition of salts both upstream and downstream of the reservoir. Therefore, there is a need for developing multiphysics thermo-mechanical–chemical (THMC) models for evaluation of reservoir behavior. The literature also describes the potential use and development of the THMC model as a part of future work that must be carried out. [ABSTRACT FROM AUTHOR]

Lithuania has a geothermal anomaly situated in the southwestern region of the country. This anomaly is comprised of two primary geothermal complexes located in western Lithuania. The first complex is characterized by the Pärnu–Kemeri Devonian sandstone aquifers, which exhibit exceptionally good flow properties. However, the reservoir temperatures in this complex only reach up to 45 °C. The second complex encompasses Cambrian sandstone reservoirs. Although these Cambrian sandstone reservoirs exhibit high temperatures, with the highest reservoir temperatures reaching up to 96 °C, these Cambrian sandstone reservoirs have less favorable petrophysical properties. This study focuses on the high temperature Cambrian Geothermal sandstone reservoirs. The study aims to conduct a geological screening of the existing and depleted hydrocarbon reservoirs with high water production rates. After initial data gathering, numerical modeling is employed with the help of mechanistic box models to evaluate the geothermal potential of the selected sites for commercial development. Ultimately, the study identifies the top five screened sites, which could be developed further for techno-economical modelling. [ABSTRACT FROM AUTHOR]

In recent years machine learning algorithms have been gaining momentum in resolving subsurface flow issues related to hydrocarbon flows, Carbon capture utilization and storage, hydrogen storage, geothermal flows, and enhanced oil recovery. This paper presents and attempts to solve subsurface flow problem using neural upscaling method. The neural upscaling method, described in the present work, is a machine learning approach to calculate effective properties in each grid block for subsurface flow modeling. This method is intended to be more accurate than traditional analytical upscaling methods (which are only accurate for layered or homogeneous media) and numerical upscaling methods (which are more accurate for heterogeneous media but involve higher computational cost and are dependent on boundary conditions). The neural upscaling method is based on learning from a large number of geological realizations, which allows it to account for uncertainty in geology. It is also computationally fast and accurate. The method is demonstrated through a series of 2D test cases, and its accuracy is compared to that of analytical and numerical upscaling methods. [ABSTRACT FROM AUTHOR]