Geothermal Operations Research Group

The Geothermal Operations Research Group is a research body set up under the umbrella of the Iceland School of Energy. Since 2008, efforts to coordinate research activities by its members have been made. Main focus of the research is on the operational side of the geothermal sector here in Iceland and around the globe. The research group aims to bridge the gap between industry and academic research in the field by combining the strength of both sectors. On many projects researchers from Reykjavik University and the University of Iceland work closely with experts from the Iceland geothermal industry, such as Landsvirkjun, HS Orka or Orkuveita Reykjavikur.

Current Members

Below you can find an overview of current members of the Geothermal Operations Research Group as of November 2016.

Academic researchers and industry professionals

María Sigríður Guðjónsdóttir

  • Assistant Professor at Reykjavik University
  • Contact: msg@ru.is

Juliet Newson

Halldór Pálsson

Ágúst Valfells

  • Professor at Reykjavik University
  • Contact: av@ru.is

Hlynur Stefánsson

  • Associate Professor at Reykjavik University
  • Contact: hlynur@ru.is

Silja Rán Sigurðardóttir

Egill Júlíusson

  • Senior Geothermal Reservoir Engineer at Landsvirkjun
  • Mendeley

Birgir Hrafnkelsson

  • Associate Professor for Statistics at University of Iceland
  • HI-Website

PhD & Master students

Cari Covell

  • PhD student at Reykjavik University
  • Cari is studying the usage of machine learning and data fusion in geothermal reservoir characterization. Cari will be presenting her research at the GEORG Workshop, November 24-25 and at the 42nd Stanford Geothermal Workshop, February 13-15, 2017.

Jorge Humberto Ormeño Leon

  • ISE Master student

Keith Smithson

  • ISE Master student
  • Keith is developing a method to quantify the reliability of geophysical exploration data. He will be presenting his research at the GEORG Workshop, November 24-25, 2016 and at the 42nd Stanford Geothermal Workshop, February 13-15, 2017.

Dagur Helgason

  • ISE Master student
  • Dagur's thesis is focused around optimization of the well placement process in geothermal fields. To this end he is developing a Python algorithm that use PyTOUGH and TOUGH2 to simulate well locations and establish the optimum in a fast and inexpensive process. He will be presenting his research at the GEORG Workshop, November 24-25 and at the 42nd Stanford Geothermal Workshop, February 13-15, 2017.

James Cately

  • ISE Master student

Riley Newman

  • ISE Master student

Zhen Qin

  • ISE Master student

Miao Yu

  • ISE Master student

 

Publications

Coordination of research efforts has led to several publications, primarily PhD and Master's thesis projects, being produced underneath the Geothermal Operations Research Group's umbrella. A selected few are listed below.

Optimization for sustainable utilization of low temperature geothermal systems (2013)

PhD Research by Silja Rán Sigríðardóttir

Abstract: Low temperature geothermal resources provide hot water that is commonly used for space heating and various applications. A geothermal resource is considered to be a renewable energy source that can be utilized by current and future generations if sustainability considerations are respected. The goal of this work is to determine if utilization of a geothermal reservoir can be optimized under sustainable operation. One way to carry out this kind of optimization is to connect reservoir and operational optimization models directly. The underlying reservoir model used here is a lumped parameter model (LPM). A LPM model can be used to simulate pressure (drawdown) changes in a low temperature reservoir with respect to harvesting levels. One scenario is to maximize the present value of profit where important parameters include production rate, water level (drawdown) and production capacity from which the profit can be calculated. Optimization over a time period subject to underlying developing constraints is often referred to as dynamic optimization. This problem is essentially a mixed integer non-linear dynamic optimization problem, often referred to as mixed integer dynamic optimization (MIDO). Three solution methods for the optimization problem are discussed, tested and compared. The parameters of the LPM are obtained by non-linear least square estimation where the LPM is essentially a simplified approach to characterize a spatially distributed reservoir. Data from four different geothermal fields are calibrated to the LPM, validated with split validation and the best calibrations chosen for the optimization application. Profit is first maximized assuming long-term production based on demand from historical data. Different performance indices for the geothermal utilization are then optimized and various scenarios are considered and compared under annually increased demand.

Complexity Analysis of Lumped Parameter models: Development of Complexity Reduction Algorithm (2016)

Dual-degree Master's thesis by Yuxi Li

Abstract: Lumped parameter models have been shown to be a useful tool for geothermal reservoir analysis and production planning. Tank models are a common form of lumped parameter models, incorporating tanks of given capacitance partially filled with fluid. Between the tanks are connections with given conductance, that allow fluid to flow between connected tanks with different fluid levels. In this thesis, we analyze how tank models of varying complexity compare in terms of accuracy and utility. An algorithm called Complexity Reduction Algorithm (CRA) is developed that can automatically find those models that are most likely to be the best by choosing a certain path through the model space. Since in general, it is reasonable to expect that a complex model is able to give an accurate fitting result and the optimum model indicated by CRA only has a medium complexity, a switch-back method is developed to decrease the training error of the complex lumped parameter models available in multi-production scenarios. Combined with this method, CRA is able to decrease the training error of complex model further.

Also, in some cases, there are large number of production wells that are producing hot water, which will leads to a situation that large amount of parameters needed to be estimated, since the number of parameters grows quadratically in terms of the number of tanks. K-means Clustering algorithm is shown to be suitable for finding initial production tank configuration under such situations.

Real data from the Laugarnes geothermal field and Reykir geothermal area in Iceland is shown in the thesis. The results show that the newly developed algorithm can provide insights into model selection for lumped parameter models. The accuracy of both history-matched and predicted drawdown for lumped parameter models of varying complexity and the results by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as an indicator for model selection have been shown.

Numerical modeling of the Hágöngur Geothermal Reservoir in Central Iceland (2016)

Master's thesis by Ximena Muguruza

Abstract: The Hágöngur geothermal area in Central Iceland is among the larger geothermal areas of the country. Surface exploration studies including geology and geothermal manifestations mapping, Transient Electromagnetic (TEM) resistivity surveys, Magnetotelluric (MT) soundings and geothermometry assessments have been conducted in the area, along with the drilling of one deep exploration well (HG-01) near the center of the resistivity anomaly. The present study integrates all physical, geological and geochemical data available by developing conceptual and numerical models of the system, in order to provide insight into the reservoir's behavior for future utilization of the geothermal fluid.

Resistivity, lithology and stratigraphy models of the reservoir were created using the RockWorks16 software based on MT resistivity measurements, lithology data of the HG-01 borehole and geological and topographical information of the area. Two different versions of the conceptual model were built based on these data, plotting stratigraphy units, cap rock, faults, heat source, temperature gradients and flow pattern. Further numerical models were developed matching up stratigraphic elevation information, rock properties and downhole temperature and pressure values using the TOUGH2 simulator via the graphical user interface PetraSim. PetraSim was also employed to define and calibrate cell-specific data such as heat sources and initial conditions to create two versions of the natural state model of the reservoir.

Hydraulic Well Stimulation in Low-Temperature Geothermal Areas for Direct Use (2016)

Master's thesis by Cari Covell

Abstract: Direct use of hot water through renewable energy resources is globally in demand. Thermal energy stored in fractures and pores within geothermal reservoirs contains natural fluids. At times, extracting natural fluids, or hot water in low-temperature areas, can be a challenge. Hydraulic stimulation is one technique to overcome this challenge. Research about hydraulic stimulation methods was done based on theory, fluid treatment, and well testing; in order to see unique trends for low-temperature geothermal applications. Furthermore, a literature review of all hydraulic stimulation applications was conducted to understand reasons for success or failure.
In order to predict the effects of hydraulic stimulation before an actual operation, a case study was performed on well HF-1 in Hoffell, Iceland. First, a preliminary production flow model was performed using updated data at the completion of testing in 2014. After evaluating the need for stimulation, a fracture model using MFrac was done in two scenarios with an open-hole packer; injection below the packer and injection above the packer.
The packer was placed in a conservative interval of 1070-1110 m depth to isolate the main fracture at 1093 m depth. Injection below the packer failed, therefore results from injection above the packer were only suitable moving forward. Subsequently, MProd software was used to find an improvement ratio after simulating stimulation above the packer. The improvement ratio of 1.096 was then applied to the original production data of well HF-1 and a LPM was performed yet again. Reservoir properties of S, T, II, and PI were calculated and compared to original production data. Results indicated the lumpfit model to be very optimistic and improvement of only 4 l/s flow over a 10 year well lifetime was observed. Therefore, the well is not a good candidate for stimulation. However, improvement was seen which proves the potential for this methodology to be implemented in other low-temperature geothermal areas.

The Role of Uncertainty for Lumped Parameter Modeling and Opimization of Low Temperature Geothermal Resources (2015)

Master's thesis by Sven Scholtysik

Abstract: With a growing population and ongoing industrialization, energy demand is rising on a global scale. Satisfying this demand in a sustainable way, while minimizing mankind's impact on climate change, is a significant challenge for our and future generations. One of the options that is apt to make a difference, is the use of low emission energy sources for heating purposes. One of these sources is Earth's ubiquitous geothermal potential. Understanding this potential and its limitations is of utmost importance.

Despite having a large impact on optimization and management of geothermal resources, the influence of uncertainty has not been studied extensively. Consequently, this thesis discusses the role of uncertainty in detail.
Using net present value maximization as the objective function, the sources of uncertainty are identified by creating a customized net present value model, which splits costs and benefits into different variables. These variables are analyzed for their tendency towards uncertainty. One of the influencing variables is the reservoir's physical reaction to production, as it allows forecasting of realistic exploitation values.
In order to test how much information is needed to produce good forecasting results, an initial lumped parameter model fit is obtained to identify the complexity of the best fit for four low temperature reservoirs in Iceland. In order to simulate decreasing uncertainty, the operational data is cut into smaller portions. By gradually extending the data range and iterative fitting, a development of the coefficient of determination is analyzed, finding that after ten seasonal cycles of data input, the model fit reaches a significant level of certainty. This time horizon can act as a stabilizing factor in the economic optimization and improve the accuracy of economic forecasting. Furthermore, the best fits do not show differences in model complexity for different levels of uncertainty.



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