Optimizing geothermal production in fractured rock reservoirs under uncertainty

Patterson, J. R.; Cardiff, Michael; Feigl, K. L.

doi:10.1016/j.geothermics.2020.101906

Cited by 21 publications

(7 citation statements)

References 17 publications

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“…The lifetime and sustainability of a geothermal reservoir are critical for its return on financial investment. As drilling and subsurface engineering account for 30% to 50% of the total investment costs 23 a simple doublet system is often chosen besides known benefits of multi-well layouts 24 , 25 . We show that through the consideration of heterogeneous heat transfer the installation of additional wells might be economically efficient, as we calculated an extended lifetime of 10 years compared to conventional estimations (Fig.…”

Section: Discussionmentioning

confidence: 99%

Velocity-dependent heat transfer controls temperature in fracture networks

Heinze

Pastore

2023

Nat Commun

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Heat transfer between a fluid and the surrounding rock in the subsurface is a crucial process not only, but most obviously, in geothermal systems. Heat transfer is described by Newton’s law of cooling, relating the heat transferred to a coefficient, the specific surface area, and the temperature difference between rock and fluid. However, parameterizing the heat transfer coefficient in fracture networks poses a major challenge. Here we show that within a fracture network the heat transfer coefficient is strongly heterogeneous but that laboratory single fracture experiments can provide a reasonable estimate in dependence of flow rate. We investigate the distribution of the heat transfer coefficient experimentally as well as numerically and analyze the heat transfer at individual fractures. Our results improve the prediction of temperatures in engineered and natural geothermal systems and allow sustainable management and design of reservoirs considering the role of individual fractures.

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Section: Discussionmentioning

confidence: 99%

Velocity-dependent heat transfer controls temperature in fracture networks

Heinze

Pastore

2023

Nat Commun

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“…Patterson et al. (2020) use analytical solutions to represent the production response of an enhanced geothermal system (EGS) to optimize the economics of production operation. While the physics‐based proxy models mentioned above can provide physically consistent predictions, their underlying assumptions limits the application to specific and simple problems.…”

Section: Introductionmentioning

confidence: 99%

“…Sigurdardottir et al (2015) apply a lumped parameter model to maximize the project NPV by adjusting the pump use through a mixed-integer programming algorithm (continuous production rate and discrete pump options as decision variables). Patterson et al (2020) use analytical solutions to represent the production response of an enhanced geothermal system (EGS) to optimize the economics of production operation. While the physics-based proxy models mentioned above can provide physically consistent predictions, their underlying assumptions limits the application to specific and simple problems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

Qin

Jiang

Faulder³

et al. 2023

Water Resources Research

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Improving the long‐term energy production performance of geothermal reservoirs can be accomplished by optimizing field development and management plans. Reliable prediction models, however, are needed to evaluate and optimize the performance of the underlying reservoirs under various operation and development strategies. In traditional frameworks, physics‐based simulation models are used to predict the energy production performance of geothermal reservoirs. However, detailed simulation models are not trivial to construct, require a reliable description of the reservoir conditions and properties, and entail high computational complexity. Data‐driven predictive models can offer an efficient alternative for use in optimization workflows. This paper presents an optimization framework for net power generation in geothermal reservoirs using a variant of the recurrent neural network (RNN) as a data‐driven predictive model. The RNN architecture is developed and trained to replace the simulation model for computationally efficient prediction of the objective function and its gradients with respect to the well control variables. The net power generation performance of the field is optimized by automatically adjusting the mass flow rate of production and injection wells over 12 years, using a gradient‐based local search algorithm. Two field‐scale examples are presented to investigate the performance of the developed data‐driven prediction and optimization framework. The prediction and optimization results from the RNN model are evaluated through comparison with the results obtained by using a numerical simulation model of a real geothermal reservoir.

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“…Fractured rocks are ubiquitous in the environment, and they play a major role in a wide range of geoscience issues, such as groundwater flow and contaminant transport (e.g., Medici et al., 2019; Neuman, 2005), hydraulic fracturing (e.g., Osiptsov, 2017; Peshcherenko et al., 2022), storage of CO 2 and nuclear waste (e.g., Bodvarsson et al., 1999; Ren et al., 2017; Wang & Hudson, 2015) and geothermal production (e.g., Murphy et al., 1981; Patterson et al., 2020). Geophysical methods offer a variety of tools to obtain information on subsurface structure and physical properties of fractured rocks.…”

Section: Introductionmentioning

confidence: 99%

A unified model for the permeability, electrical conductivity and streaming potential coupling coefficient in variably saturated fractured media

Thanh

Nghĩa

van

et al. 2022

Geophysical Prospecting

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We present a new unified model for the permeability, electrical conductivity, and streaming potential coupling coefficient in variably saturated fractured media. For those, we conceptualize the fractured medium as a partially saturated bundle of parallel capillary slits with varying sizes. We assume that the fracture size distribution of the corresponding medium follows a fractal scaling law, which allows us establish a pressure head-saturation relationship based on the Laplace equation. We first describe the flow rate, the conduction current, and the electrokinetic streaming current within a single fracture. Then, we upscale these properties at the scale of an equivalent fractured media partially saturated in order to obtain the relative permeability, the electrical conductivity and the streaming potential coupling coefficient. The newly proposed model explicitly depends on pore water chemistry, interface properties, microstructural parameters of fractured media, and water saturation. Model predictions are in good agreement with both experimental and simulated data and with another model from the literature. The results of this work constitute a useful framework to estimate hydraulic properties and monitor water flow in fractured media.

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Optimizing geothermal production in fractured rock reservoirs under uncertainty

Cited by 21 publications

References 17 publications

Velocity-dependent heat transfer controls temperature in fracture networks

Velocity-dependent heat transfer controls temperature in fracture networks

Efficient Optimization of Energy Recovery From Geothermal Reservoirs With Recurrent Neural Network Predictive Models

A unified model for the permeability, electrical conductivity and streaming potential coupling coefficient in variably saturated fractured media

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