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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

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Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations.

Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios.

Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code).

Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields.

Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs:
1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible
2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON
3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations
4. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible
5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells.
6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated.
7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process

Citation Formats

TY - DATA AB - Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible 5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells. 6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated. 7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process AU - Beckers, Koenraad F. A2 - Duplyakin, Dmitry A3 - Martin, Michael J. A4 - Johnston, Henry E. A5 - Siler, Drew L. DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1842479 KW - geothermal KW - energy KW - machine learning KW - ML KW - subsurface KW - characterization KW - Brady Hot Springs KW - BHS KW - prediction KW - reservoir modeling KW - time series KW - PCA KW - principal component analysis KW - reservoir management KW - reservoir KW - dual-porosity KW - stimulation KW - injection test KW - pde KW - temperature KW - flow KW - pressure KW - simulation KW - single-fracture KW - doublet KW - heat map KW - TensorFlow KW - CNN KW - LSTM KW - MLP KW - hydrothermal KW - Open Source Reservoir KW - OSR KW - Nevada LA - English DA - 2021/10/20 PY - 2021 PB - National Renewable Energy Laboratory T1 - Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results UR - https://doi.org/10.15121/1842479 ER -
Export Citation to RIS
Beckers, Koenraad F., et al. Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results. National Renewable Energy Laboratory, 20 October, 2021, GDR. https://doi.org/10.15121/1842479.
Beckers, K., Duplyakin, D., Martin, M., Johnston, H., & Siler, D. (2021). Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results. [Data set]. GDR. National Renewable Energy Laboratory. https://doi.org/10.15121/1842479
Beckers, Koenraad F., Dmitry Duplyakin, Michael J. Martin, Henry E. Johnston, and Drew L. Siler. Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results. National Renewable Energy Laboratory, October, 20, 2021. Distributed by GDR. https://doi.org/10.15121/1842479
@misc{OEDI_Dataset_7462, title = {Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results}, author = {Beckers, Koenraad F. and Duplyakin, Dmitry and Martin, Michael J. and Johnston, Henry E. and Siler, Drew L.}, abstractNote = {Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations.

Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs.

Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios.

Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code).

Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields.

Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs:
1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible
2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON
3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations
4. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible
5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells.
6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated.
7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process}, url = {https://gdr.openei.org/submissions/1346}, year = {2021}, howpublished = {GDR, National Renewable Energy Laboratory, https://doi.org/10.15121/1842479}, note = {Accessed: 2025-04-25}, doi = {10.15121/1842479} }
https://dx.doi.org/10.15121/1842479

Details

Data from Oct 20, 2021

Last updated Mar 24, 2022

Submitted Nov 8, 2021

Organization

National Renewable Energy Laboratory

Contact

Koenraad Beckers

Authors

Koenraad F. Beckers

National Renewable Energy Laboratory

Dmitry Duplyakin

National Renewable Energy Laboratory

Michael J. Martin

National Renewable Energy Laboratory

Henry E. Johnston

National Renewable Energy Laboratory

Drew L. Siler

United States Geological Syrvey

Research Areas

DOE Project Details

Project Name Insightful Subsurface Characterizations and Predictions

Project Lead Mike Weathers

Project Number 35517

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