Reservoir Simulation Research Articles

Simulation of Flood-Control Reservoirs: Comparing Fully 2D and 0D–1D Models

Flood-control reservoirs are often used as a structural measure to mitigate fluvial floods, and numerical models are a fundamental tool for assessing their effectiveness. This work aims to analyze the suitability of fully 2D shallow-water models to simulate these systems by adopting internal boundary conditions to describe hydraulic structures (i.e., dams) and by using a parallelized code to reduce the computational burden. The 2D results are also compared with the more established approach of coupling a 1D model for the river and a 0D model for the reservoir. Two test cases, including an in-stream reservoir and an off-stream basin, both located in Italy, are considered. Results show that the fully 2D model can effectively handle the simulation of a complex flood-control system. Moreover, compared with the 0D–1D model, it captures the velocity field and the filling/emptying process of the reservoir more realistically, especially for off-stream reservoirs. Conversely, when the basin is characterized by very limited flood dynamics, the two approaches provide similar results (maximum levels in the reservoir differ by less than 10 cm, and peak discharges by about 5%). Thanks to parallelization and the inclusion of internal boundary conditions, fully 2D models can be applied not only for local hydrodynamic analyses but also for river-scale studies, including flood-control reservoirs, with reasonable computational effort (i.e., ratios of physical to computational times on the order of 30–100).

Numerical simulation of shale gas reservoir based on ILU-GMRES method

ABSTRACT The numerical simulation of shale gas reservoirs requires transforming mathematical models into large linear equation systems, and solving large linear equation systems requires a lot of time. A novel ILU-GMRES method for solving large linear equations is presented, which treats the sub matrix discretized at nodes as the basic elements of the coefficient matrix. By combining the incomplete LU decomposition method (ILU) with the generalized minimum residue method (GMRES), the discretized large linear equations are iteratively solved. A comparison was made between the new method and other methods for calculating the actual shale gas reservoir model. The results reveal that the new method has a faster calculation speed in solving linear equation systems. The calculation time of the novel ILU-GMRES method is 1029s, the calculation time of the generalized minimum residual method (GMRES) method is 2709s, the calculation time of the Gauss–Seidel method (GS) is 3786s, and the calculation time of the successive over relaxation method (SOR) method is 3386s, the calculation time of the preprocessing conjugate gradient method (PCG) is 3115s. The calculation speed of the novel method is almost 2.6 times faster than the GMRES method, the novel method is more effective in the numerical simulation of shale gas.

Simulation-Based Optimization Workflow of CO2-EOR for Hydraulic Fractured Wells in Wolfcamp A Formation

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study proposes an optimization workflow for the CO2 huff-n-puff process for multistage-fractured horizontal wells in the Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics–reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. A fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a comprehensive and systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

Integration of Geological Model and Numerical Simulation Technique to Characterize the Remaining Oil of Fractured Biogenic Limestone Reservoirs

AbstractFine geological modeling leads to accurate reservoirs numerical simulations. Fractured biogenic limestone has abundant storage spaces and flow paths to accumulate oil and gas. The complexity and diversity of fractured biogenic limestone also lead to challenges in accurately characterizing its pore volume and remaining oil. This investigation aimed to enhance the understanding of fractured biotite reservoir properties via geological modeling. Numerical simulations were used to characterize the remaining oil during the late stage of field development. Considering the differences in porosity and permeability between fractures and matrix, a facies-controlled stochastic modeling technique was used to establish a dual-porosity and dual-permeability (DPDP) model for numerical simulation. Core information, logging data, and multiple seismic attributes were combined to guide low-level sequence fault interpretation for tectonic refinement. Based on classified seismic inversion, sedimentary phases were reconstructed. A discrete fracture network (DFN) model was obtained based on fracture occurrences and density models. The optimized discrete adjoint (ODA) algorithm was utilized to calibrate model parameters. The findings revealed that dense tectonic fractures develop in thick biogenic limestone areas. Combined with advanced reservoir simulation technology, these findings suggest that areas of thicker biogenic limestone were consistent with areas of higher fracture matrix conductivity multipliers. The remaining oil distribution patterns were investigated, and to deploy new wells was guided. Therefore, it is essential to better understand the tectonic characteristics of fractured biogenic limestone reservoirs and their remaining oil distribution patterns by integrating multiple sources of information and mastering advanced reservoir simulation technology for oilfield development.

Importance of Clay Swelling on the Efficacy of Cyclic Steam Stimulation in the East Moldabek Formation in Kazakhstan

Both steam and hot water flooding of high-viscosity oils in the presence of swelling clays are difficult methods for producing oil efficiently because of potential formation permeability reduction. This paper pertains to heavy oil recovery from the East Moldabek formation where the oil API gravity is about 22 and is inundated with swelling clays. To achieve this, we used the IntersectTM reservoir simulator to compare oil recovery economics using both hot water and steam injection as a function of steam cycle duration, temperature, and steam dryness. We also studied clay swelling in the East Moldabek formation where clay poses a significant challenge due to its impact on permeability reduction. In this research, we developed an equation based on experimental data to establish a relationship between water mineralization and permeability in the East Moldabek formation. The equation provides valuable insight on how to mitigate clay swelling which is crucial for enhancing oil recovery efficiency—especially in sandstone reservoirs. Our modeling studies provide the recovery efficiencies for salinities of the hot water EOR versus cyclic steam EOR methods in a formation containing swelling clays. Specifically, the reduction in formation permeability as a function of the distilled water fraction is the controlling parameter in hot water or steam flooding—when the formation water mixture becomes less saline, oil recovery decreases. Our research shows that clay swelling can significantly impact cyclic steam stimulation outcomes, potentially reducing its effectiveness, while hot water flooding may offer a more cost-effective and operationally feasible solution in formations where clay swelling is a concern. Economic analysis reveals the potential for achieving an optimal favorable condition for hot water injection. Therefore, this paper provides a guideline on how to conduct thermal oil recovery for heavy oils in fields with high clay content such as the East Moldabek deposit.

Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models

Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. The present study focuses on CO2 storage in the Smeaheia saline aquifer. Two SRMs were created using Deep Neural Networks (DNNs) to predict CO2 saturation and pressure over all grid blocks for 50 years. 18 million samples and 31 features, including reservoir static and dynamic properties, build the input data. Models comprise 3–5 hidden layers with 128–512 units apiece. SRMs showed a runtime improvement of 300 times and an accuracy of 99% compared to the 3D numerical simulator. The genetic algorithm was then employed to determine the optimal rate and duration of CO2 injection, which maximizes the volume of injected CO2 while ensuring storage operations’ safety through constraints. The optimization continued for the reproduction of 100 generations, each containing 100 individuals, without any hyperparameter tuning. Finally, the optimization results confirm the significant potential of Smeaheia for storing 170 Mt CO2.

Modeling unobserved geothermal structures using a physics-informed neural network with transfer learning of prior knowledge

Deep learning has gained attention as a potentially powerful technique for modeling natural-state geothermal systems; however, its physical validity and prediction inaccuracy at extrapolation ranges are limiting. This study proposes the use of transfer learning in physics-informed neural networks to leverage prior expert knowledge at the target site and satisfy conservation laws for predicting natural-state quantities such as temperature, pressure, and permeability. A neural network pre-trained with multiple numerical datasets of natural-state geothermal systems was generated using numerical reservoir simulations based on uncertainties of the permeabilities, sizes, and locations of geological units. Observed well logs were then used for tuning by transfer learning of the network. Two synthetic datasets were examined using the proposed framework. Our results demonstrate that the use of transfer learning significantly improves the prediction accuracy in extrapolation regions with no observed wells.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

A novel meshless method for numerical simulation of fractured-vuggy reservoirs

This paper proposes a novel meshless numerical simulation method for fractured-vuggy reservoirs. Initially, model discretization involves extracting fracture and vug feature nodes along the contours of the reservoir bodies and in alignment with fracture orientations, tailored to the characteristics of the fractured-vuggy oil deposit. Subsequently, a connection element model is established within the influence domain. Based on the seepage equations of the connection system, a computational method for the parameters of the connection elements (connection transmissibility and connection volume) is defined. In addition, a sequential method is employed to solve for the pressure and saturation, achieving rapid prediction of the reservoir's production dynamics. On this basis, an automatic history matching algorithm is utilized for real-time fitting of oil–water dynamic indicators, inverting the characteristic parameters of the connection element model, and quantitatively characterizing the injection–production connection elements. The research findings indicate that the method is capable of rapidly fitting and predicting the dynamics of oil reservoir production. Furthermore, conceptual model examples have substantiated that it achieves comparable computational efficiency and superior computational accuracy when compared to the traditional finite difference (volume) method under the same nodal conditions. Additionally, the node arrangement for fractures and vugs is comparatively flexible, and it can ensure the integrity of the flow paths with a limited number of nodes to enhance predictive accuracy. Therefore, this method can effectively meet the rapid prediction requirements for fractured-vuggy reservoirs and also provides a novel meshless computational approach for the numerical simulation of such reservoirs.

A parallel compositional reservoir simulator for large-scale CO2 geological storage modeling and assessment

Storing CO2 in deep aquifers and depleted gas reservoirs is an effective way to achieve carbon neutrality. However, the numerical simulation of CO2 storage in these formations is challenging due to the complexity of gases-brine systems. The number of gas species included in the gases-brine fluid models of existing simulators cannot meet the rapidly evolving CO2 sequestration scenarios. To address this intricate issue, we developed a three-dimensional fully implicit parallel CO2 geological storage simulator (PRSI-CGCS) on distributed-memory computers based on our in-house parallel platform. This simulator uses a compositional fluid model with a diverse range of gas species, including CO2, C1 ~ C3, N2, H2S, as well as newly added gases H2 and O2, which may be encountered in geological CO2 storages. Besides, we provide more suitable scaling factors for different gases in the stability analysis bypassing (SAB) method to accelerate the gases-brine phase equilibrium calculations. PRSI-CGCS does not incorporate energy conservation equations, and salt precipitation or dissolution is also not considered. Numerical experiments show that our simulator is scalable, robust and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster. Besides, after our modification on scaling factors, the SAB method can reduce the number of stability analyses by 61.39 % to 88.71 %, thereby reducing simulation time. Furthermore, case studies indicate that injecting O2 and H2 along with CO2 reduces the stability or capacity of CO2 storage and increases the pressure required for injection. However, this impact is not significant when the impurity content is less than 10 %.

Autonomous Inflow Control Valves Enable Better Water Control in Peru Oil Field

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218074, “Making Better Wells With Autonomous Inflow Control Valves for Water Control in the Flank of the Bretaña Norte Oil Field, Peru: A Case Study,” by Willy Garcia, SPE, J. Antonio Zegarra, SPE, and Edwar Bustamante, PetroTal Peru, et al. The paper has not been peer reviewed. _ Bretaña Norte is a heavy oil greenfield in a remote area of northeast Peru. The field development strategy was designed around horizontal wells and advanced completions with autonomous inflow control devices (AICDs). As a part of a technology evaluation program, autonomous inflow-control-valve (AICV) technology was selected for field trials because of its ability to autonomously shut off water based on fluid properties. A reservoir simulation study was conducted, and the expected performance of AICV was compared with other AICD technologies installed in the field, indicating potential benefits related to better reservoir management. Field Description Bretaña Norte is an onshore field in the Peruvian Amazon. The Vivian formation is the main reservoir of the Bretaña Norte field and consists entirely of fluvial deposits composed of massive, moderately sorted, fluvial sandstones with thicknesses ranging from 40 to 200 ft. The Vivian formation is subdivided into Vivian S1 and Vivian S2, but only the Vivian S2 formation has been exploited to date. Heavy oil is produced from the Vivian S2 formation (18–19 °API and 25 cp), which features reservoir permeability ranging from 1 to 4 darcies. An active bottom aquifer of greater than 400-ft thickness was encountered under the60-ft oil column of the reservoir, posing a difficult production challenge to the operator: how to handle high volumes of water production that must be reinjected into a different formation. The Bretaña Norte field began production in 2018 with a few initial deviated wells with electrical submersible pumps (ESPs). The first horizontal well completed with standalone screens (SAS) experienced early water breakthrough. Thus, the operator opted for advanced completions with screens and AICDs in 2019. Between 2019 and 2023, 11 wells were completed with different AICD technologies. Even though initial results were positive, such outcomes have been highly dependent on the structural position of the wells. In 2022, a trial well was completed with the AICV technology in the flank of the reservoir, significantly closer to the oil/water contact (OWC), to evaluate its performance under challenging conditions. AICV Technology The AICV is an AICD designed to modify its open-flow area according to the properties (viscosity and density) of the fluids flowing through it and to choke flow progressively as water cut or gas-volume fraction values increase. The AICV is fully reversible, meaning that, if changes in the fluids flowing through the valve occur, it will reopen or reclose accordingly. The AICV consists of two flow paths, the main flow path and the pilot flow path, with an approximate flow distribution of 97 and 3%, respectively. Fig. 1 presents a schematic of the AICV, including a heat map showing the pressure distribution across the different flow paths.

Underpinnings of reservoir and techno-economic analysis for Himalayan and Son-Narmada-Tapti geothermal sites of India

The high capital cost and risks associated with the extraction process of geothermal energy are key factors in the project economics. This paper presents a comprehensive technoeconomic study based on reservoir heat and flow simulations along with the levelized cost of energy (LCOE) for two geothermal sites (Himalayan and Son-Narmada-Tapti i.e. SONATA) in India.Dual porosity reservoir simulation models were constructed and were used to obtain the cumulative and the dynamic thermal energy potentials of these sites. The simulation studies suggest that both sites are equally potent for geothermal energy extraction. Comparatively, the proven cumulative energy potentials for the Himalayan and SONATA sites are found to be 4.92–5.10 and 3.62–3.7 TW h, respectively, for the 30 years of production at a rate of 92 kg/s. This difference can be attributed mainly to higher temperatures and deeper sites at the Himalayan Province. Sensitivity analyses were conducted to assess the impact of various parameters, including porosity, permeability, fracture spacing, and thermal conductivity. The results suggest that porosity and permeability are the key enablers of efficient energy production. The economic feasibility study suggests that the LCOE costs are high for these sites ($148/MWh for the Himalayan and $137/MWh for the SONATA). These two sites have CO2 mitigation potential in the range of 3.12 and 11.36 Megatons from a single doublet well configuration considering the CO2 emissions from conventional coal-fired thermal power plants. Overall, SONATA can be the better prospect considering the logistics and operational challenges along with reservoir heat potential.

On the Significance of Hydrate Formation/Dissociation during CO2 Injection in Depleted Gas Reservoirs

Summary The study aims to quantitatively assess the risk of hydrate formation within the porous formation and its consequences on injectivity during storage of CO2 in depleted gas reservoirs considering low temperatures caused by the Joule-Thomson (JT) effect and hydrate kinetics. Hydrates formed during CO2 storage operation can occupy porous spaces in the reservoir rock, reducing the rock’s permeability and thus becoming a hindrance to the storage project. The aim was to understand which mechanisms can mitigate or prevent the formation of hydrates. The key mechanisms we studied included water dry-out, heat exchange with surrounding rock formation, and capillary pressure. A semicompositional thermal reservoir simulator is used to model the fluid and heat flow of CO2 through a reservoir initially composed of brine and methane. The simulator can model the formation and dissociation of both methane and CO2 hydrates using kinetic reactions. This approach has the advantage of computing the amount of hydrate deposited and estimating its effects on the porosity and permeability alteration. Sensitivity analyses are also carried out to investigate the impact of different parameters and mechanisms on the deposition of hydrates and the injectivity of CO2. Simulation results for a simplified model were verified with results from the literature. The key results of this work are as follows: (1) The JT effect strongly depends on the reservoir permeability and initial pressure and could lead to the formation of hydrates within the porous media even when the injected CO2 temperature was higher than the hydrate equilibrium temperature; (2) the heat gain from underburden and overburden rock formations could prevent hydrates formed at late time; (3) permeability reduction increased the formation of hydrates due to an increased JT cooling; and (4) water dry-out near the wellbore did not prevent hydrate formation. Finally, the role of capillary pressure was quite complex, as it reduced the formation of hydrates in certain cases and increased in other cases. Simulating this process with heat flow and hydrate reactions was also shown to present severe numerical issues. It was critical to select convergence criteria and linear system tolerances to avoid large material balance and numerical errors.

Numerical simulation of carbon dioxide flooding in fractured reservoirs using generic projection-based embedded discrete fracture model

This paper, for the first time, integrates the generic projection-based embedded discrete fracture model (pEDFM) with the commercial reservoir simulator ECLIPSE for carbon dioxide (CO2) flooding in fractured reservoirs. The integrated method first obtains inter-grid connections and corresponding transmissibilities within the reservoir model based on the generic pEDFM. It then constructs an equivalent CO2 flooding ECLIPSE model to the original pEDFM reservoir model, thereby calculating the global equations of the compositional flow model to obtain distributions of pressure, saturation, component concentrations, and well performance data. We implemented three numerical examples to verify that the proposed method can achieve significantly higher computational accuracy compared to the widely used embedded discrete fracture model in both high and low permeability fracture scenarios, while also avoiding the difficulties associated with generating matching grids for complex fracture networks. Furthermore, the proposed integrated method uses the solver within ECLIPSE to solve the global equations, thus avoiding the high cost of developing a robust nonlinear solver for complex compositional model of CO2 flooding. This demonstrates the practicality of the method and its significant potential for subsequent application to various reservoir models.

Data-driven guided physics-informed segmented neural network for liquid–vapor flash calculation

Liquid–vapor phase equilibrium is ubiquitous in industrial and engineering field, which involves the flash calculation. The conventional flash calculation is solved with the numerical simulator, accompanying with large computational efforts. In this paper, we propose a data-driven guided physics-informed segmented neural network (DDG-PISNN) for the liquid–vapor pressure–temperature flash calculation. The training of DDG-PISNN is divided into two stages. First, a classifier for determining the stability of the system and a guiding network are built using data-driven methods. Subsequently, various control equations are employed to construct loss functions based on the results of classifier. In this way, DDG-PISNN fully leverages the advantages of data-driven approaches and physical equations. The accuracy and robustness of DDG-PISNN are calibrated by experiments under different conditions, and the performance is compared with that of DDG-PINN. In addition, a surrogate model for flash calculation is constructed based on DDG-PISNN. The accuracy of the surrogate model is also validated against a numerical case, and the computational efficiency is more than 800 times. Then, the surrogate model is embedded into the reservoir simulation technique to perform the flash calculation and form a surrogate-based compositional model. The surrogate-based model is employed to simulate the process of CO2 displacing crude oil. The results are in good agreement with the results of numerical solution.

Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China

During CO2-EOR implementation, CH4 in the reproduced CO2-rich mixture are included in the recycle injection gas. However, improper injection gas composition and operation parameters can reduce the flooding performance.In this study, a machine-learning assisted framework combining Convolutional Neural Networks-Gate Recurrent Unit (CNN-GRU) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to obtain the optimal parameters for simultaneous maximum oil production, carbon storage efficiency, and net present value. A case study from the CO2-EOR project in the H59 block of Jilin oilfield, China, was carried out. Base cases were established to evaluate the effects of reinjection CH4 on flooding performance.Results show that the built CNN-GRU model has high prediction accuracy and computational efficiency, and thus can be used as an alternative tool to the reservoir simulator. The proposed framework can find the optimum parameters to improve oil recovery, carbon storage and net present value. The co-injection of CH4 and CO2 improves oil production, and carbon storage performance, but reduces the net present value. This work provides engineers with multiple strategies for decision-making to simultaneously promote flooding performance with CH4 being a co-injectant in CO2-EOR projects.

A Novel Hybrid Physics/Data-Driven Model for Fractured Reservoir Simulation

A Novel Hybrid Physics/Data-Driven Model for Fractured Reservoir Simulation

Techno-economic-environmental study of CO2 and aqueous formate solution injection for geologic carbon storage and enhanced oil recovery

As carbon capture, utilization, and storage (CCUS), carbon-dioxide enhanced oil recovery (CO2 EOR) has inherent shortcomings, such as inefficient oil recovery and carbon storage, and low storage security with mobile CO2. This paper presents a techno-economic-environmental analysis of using formate species, a product of CO2 electrochemical reduction, as an alternative carbon carrier for sequestration and EOR in a carbonate oil reservoir in the Gulf of Mexico Basin. CO2 injection, water-alternating-CO2 injection, and aqueous formate solution injection were compared using a compositional reservoir simulation model and an economic calculator. Formate solution injection yielded greater levels of oil recovery and net carbon storage, where the carbon-bearing species resided in the dense aqueous phase without having to rely on petrophysical trapping mechanisms (structural and capillary). The enhanced oil production, net carbon storage, and storage security can be promoted by providing formate-based CCUS with more incentives (e.g., greater tax credit) in comparison to CO2-based CCUS for EOR and the manufacture of chemicals and products. In establishing carbon storage incentive policies and regulations, policymakers should include alternative carbon carriers.

The Impact of Deformable Surrounding Rocks on Naturally-Fractured Reservoir Simulation

The Impact of Deformable Surrounding Rocks on Naturally-Fractured Reservoir Simulation

A geothermal doublet in a single well: application of the recirculation well concept to reuse wells to be abandoned

The depletion of oil and gas reservoirs leaves many deep and expensive wells with no option but to be abandoned. However, these wells could have a second chance to extend their life as a source of geothermal energy, especially if they are located next to urban or industrial areas in need of heating. This article shows a technical analysis of the well recirculation or geothermal doublet in a single well approach as a strategy to reuse wells that cross aquifers. Different aspects of this strategy are highlighted, including the technical conditions required for the application, the well completion, and the geothermal performance. The latest is assessed via numerical reservoir simulations using geological data from an existing well. The results show that vertical petrophysical heterogeneities play an important role in the performance and feasibility of this approach.