SGP -TR-223
Geothermal Modelling: Industry Standard Practices
Rony P. Nugraha1,2*, John O’Sullivan2, Michael J. O’Sullivan2, and Fathan H. Abdurachman3
1Geoenergi Solusi Indonesia (Geoenergis), Cibis Nine 11th Floor, Jakarta, Indonesia
2Department of Engineering Science, The University of Auckland, Private Bag 90210, Auckland, New Zealand
3PT. Pertamina Geothermal Energy, Jakarta, 10340, Indonesia
*[email protected]; [email protected]
Keywords: reservoir, modelling, geothermal, conceptual model, numerical model, natural state, history matching, future scenario, boundary condition, AUTOUGH2, Leapfrog, Waiwera
ABSTRACT
Geothermal reservoir modelling is essential for the successful development of geothermal fields. It assists geothermal companies in decision making and planning for the development of a geothermal resource. However, the approach used for generating a geothermal reservoir model depends on the company, university, or consultant involved as there is no standardised approach. Each entity has a different approach in making geothermal models based on their chosen or given reservoir tools and work-flow. This variability in approach is further exacerbated by the limited functionality of some reservoir tools. Commonly, simplification to the boundary conditions in the model are made in the hopes of simplifying the model setup process. These simplifications include the use of a constant pressure and temperature water table at the top of the model, constant pressure and temperature at the bottom boundary, and the inclusion of only a small part of the reservoir within the area covered by the model grid. These simplifications may cause the model’s future scenario simulation results to be unreliable and not useful for decision making. Furthermore, the numerical model used for the reservoir simulation process often does not align with the conceptual model of the field. All this, in turn, will prevent the use of the simulation results from the numerical model in refining the conceptual model.
This paper aims to help the geothermal community to develop guidelines for conducting geothermal reservoir modelling. The final output is expected to be a comprehensive Term of Reference (TOR) to ensure the final reservoir model is based on the accepted standard practices and provides value for the developer. Modern technologies in geothermal modelling will be presented in this paper. We will describe the numerical model development work-flow developed by the Geothermal Institute at the University of Auckland, and utilised by several developers worldwide. Moreover, we will elaborate on the key requirements that must be met at each stage of making a robust reservoir model. The aim is to eliminate unnecessary approximations and to ensure the model is based on correct reservoir physics. These stages include (1) making a 3D digital conceptual model which will be the basis of the numerical model, (2) selection of the grid for the model based on the advantages and disadvantages of each type of grid, (3) selecting appropriate data to be used for the natural state model calibration process and completing the calibration, (4) history matching with production data, and (5) setting up and running future scenarios.
1. INTRODUCTION
Geothermal reservoir modelling is the process of creating a representative model of a geothermal field to study the critical chemical and physical processes that govern the behaviour of the geothermal system. The overall process entails gathering and analy sing field data, developing a conceptual model of the geothermal system, developing a numerical model for natural state simulation and calibrating it against the existing data, developing a production model using the calibrated natural state model and validating it against production history data (if the field is already in production), and simulating future scenarios. Geothermal models are primarily used to forecast for how long the geothermal system can be economically exploited, to predict how often and when make-up wells should be scheduled, for choosing the best reinjection method, and for predicting how the environment can influence production (O’Sullivan & O’Sullivan, 2021).
According to O’Sullivan et al. (2001), since the 1980s, computer modelling to enhance the planning and management of geothermal field development has been a common procedure. In that same study, O’Sullivan et al. (2001) further detailed the result of an assessment of the worldwide use of geothermal reservoir simulation from 1990 to 2001. This assessment indicated that the TOUGH2 simulator (Pruess et al., 1999; Finsterle et al., 2014) is the most frequently used software for geothermal simulation.
Despite its critical roles in geothermal energy development, the methods used in generating a geothermal reservoir model depends on the individual company, university, or consultant. There is no standardised approach. Each modelling team has a different approach in making geothermal models based on their chosen or given reservoir tools and work-flow. This variability in approach is further exacerbated by the limited functionality of some reservoir tools. Commonly, simplification to the boundary conditions in the model are made in the hopes of simplifying the model setup process. These simplifications include the use of a constant pressure and temperature water table at the top of the model, constant pressure and temperature at the bottom boundary, and the inclusion of only a small part of the reservoir within the area covered by the model grid. As a result of this simplification process the model may fail to fully represent the complexity and heterogeneity of the geothermal system. These simplifications may cause the future scenario simulation results from the model to be unreliable and not useful for decision-making. Furthermore, the numerical model used for the reservoir simulation process often does not
align with the conceptual model of the field. All this, in turn, will prevent the use of the simulation results from the numerical model in refining the conceptual model.
This paper aims to provide a guideline for conducting a geothermal reservoir modelling project by summarising the best practice for each stage of geothermal model development, based on a work-flow developed by the Geothermal Institute, at the University of Auckland.
Several geothermal developers have also adopted this work-flow and it has become their standard practice in developing a geothermal model. We will also describe the numerical model development work using modern technologies in geothermal modelling, including the utilisation of Leapfrog software for 3D digital conceptual model development and 3D visualisation, the application of PyTOUGH (Croucher, 2011) scripts for numerical model setup and two-way integration between the Leapfrog model and the TOUGH2 model, and the use of AUTOUGH2 (Yeh et al., 2012) and Waiwera (Croucher et al., 2020) as the geothermal reservoir simulators. The main requirements that must be met at each stage of making a robust reservoir model will also be elaborated. This approach eliminates unnecessary approximations used by some modellers in the past and make the model more based on reservoir physics.
2. MODERN TOOLS IN GEOTHERMAL MODELLING
The necessity for larger, more sophisticated, and better geothermal models resulted in geothermal software developers creating TOUGH2, which has proven to be a robust 3D modelling tool. Over the years TOUGH2 has been updated with new options and additional equations of state (EOS). Burnell et al. (2012) highlighted that future geothermal modelling software should be based on the modelling approach used in the geothermal industry at that time. A decade since their study, modelling tools for geothermal have massively advanced - a direct consequence of the rapid change of computer technology. The following sections will discuss advanced methods for geothermal modelling that have been widely embraced by geothermal developers.
2.1 Leapfrog Geothermal Software
Leapfrog geothermal is commercial 3D subsurface modelling software that was specifically developed for the geothermal sector by the New Zealand-based software developer ARANZ Geo Limited (now Seequent Limited) in collaboration with New Zealand geothermal experts. Leapfrog Geothermal uses a dynamic strategy for meeting geological modelling challenges. It features an intuitive user interface that enables users to easily integrate geothermal data sets such as geology, structure, temperature, hydrothermal alteration, feed zones, geophysical data, and TOUGH2 numerical models. Leapfrog’s integrated environment enables direct visualisation, comparison, and modelling of field-wide, multidisciplinary data, which is commonly utilised for geothermal projects worldwide. (Alcaraz et al., 2015;
O’Sullivan et al., 2017).
Figure 1: The main interface for Leapfrog Geothermal
Leapfrog can be used as the central platform for geothermal modelling work. Leapfrog has the capacity to store all 3G (geology , geophysics, and geochemistry) survey data, along with GIS and well data, then further compile them into an integrated and very useful 3D geological model. Additionally, once a 3D geological model has been developed, Leapfrog can generate a TOUGH2 numerical model.
The software enables the automatic assignment of rock types to the TOUGH2 model, bypassing the time-consuming job of manually assigning geology to the reservoir model. Additionally, Leapfrog allows the 3D geological model to be easily updated throughout the duration of the project. New data from field surveys or drilling activities can be easily incorporated into the 3D geological model and used to augment earlier data. Leapfrog will then adapt the 3D digital conceptual model to be compatible with the data and automatically update the reservoir model configuration (Nugraha and O’Sullivan, 2018).
2.2 AUTOUGH2
AUTOUGH2 is an improved version of the TOUGH2 simulator, created to enhance the capability of TOUGH2 at the University of Auckland. Yeh et al. (2012) claimed that AUTOUGH2 aimed to enhance the ease of use, efficiency, and capabilities of TOUGH2.
Significant improvements to AUTOUGH2 include the following: (a) Over 36 Equations Of State (EOS) modules are combined with ot her AUTOUGH2 subroutines to create a single executable, allowing users to select the desired EOS; (b) modification of TOUGH2’s output format, enabling proper printing of simulation results for each EOS; (c) improvement of the original thermodynamics subroutines, resulting in a 5-10 fold speed-up in the calculation of secondary thermodynamic variables; and (d) application of the IAPWS-97 formulation in AUTOUGH2, allowing the handling of supercritical conditions for EOS1, EOS3, and EOS4 (e) restructuring of the AUTOUGH2 code for dynamic memory allocation, allowing for a larger models and more efficient memory usage; and (f) the addition of new generator types to AUTOUGH2, allowing for the simulation of more complex real-world geothermal production, injection, and power generation scenarios. Additionally, AUTOUGH2 includes several modifications to enhance convergence behaviour (O’Sullivan et al., 2013).
Furthermore, Yeh et al. (2013) established a novel open-source graphical tool called TIM to assist in the building and calibration of AUTOUGH2 reservoir models. It is capable of displaying model parameters and simulation results. TIM is primarily concerned with making easily accessible two-dimensional layer and two-dimensional slice charts that incorporate colour, text, and flow arrows. This enables a more quantitative examination of the local behaviour of models, necessary for manual model calibration.
Figure 2: (A) New generator types featured in AUTOUGH2 (Yeh et al., 2012) and (B) TIM’s main interface for numerical modelling
2.3 Waiwera
Waiwera is a new geothermal simulator created by the University of Auckland in partnership with GNS Science. They constructed Waiwera using a fully parallelised, object-oriented Fortran 2003 codebase, based on their experience developing and using the TOUGH2 and AUTOUGH2 codes. This application was the first open-source simulator that provided the necessary capability and performance for very large, complicated geothermal reservoir models (Croucher et al., 2020). Additionally, O’Sullivan et al. (2019) demonstrated that Waiwera is capable of running and calibrating massive geothermal reservoir models consisting of millions of blocks (> 106 blocks). They also use an approach with Waiwera allowing reservoir models to be defined by a set of grid-independent data files as a precursor to generating models of different resolution. This not only simplifies the design and management of multi-million block models but also offers a foundation for constructing many models with varying resolutions of the same system.
Croucher et al. (2020) also noted that Waiwera includes numerous advancements targeted at enhancing the phase transition behaviour and convergence of steady-state models, including
new update on the multiphase gravity equation
primary variable interpolation during phase transitions
primary variable non-dimensionalisation
extra non-linear solver convergence criteria
Table 1 gives examples of how Waiwera’s parallel capability combined with its improved convergence result in much faster simulations allowing for better, faster model calibration and the use of more advanced modelling techniques (O’Sullivan et al. 2021).
Table 1: Computational time comparison between AUTOUGH2 and Waiwera with various processors (CPU) used (O’Sullivan et al. 2021).
2.4 PyTOUGH Scripts
PyTOUGH is a Python scripting library that enables the automated execution of TOUGH2 simulations. The PyTOUGH library was built to help the user to handle all aspects of a TOUGH2 simulation, including rectangular, radial, and MINC grid generation, model grid optimisation, model setup, rock types population, block naming, grid layer refinement, grid block refinement, post -processing and analysis, and much more. This methodology overcomes the constraints of both manual and graphical methods, enabling sophisticated simulations incorporating suites of many models runs with varied parameters (such as permeabilities, heat inputs, and grid resolution), among others (Croucher, 2011; Wellmann, 2012; Croucher, 2015). Additionally, O’Sullivan et al. (2013) observed that using PyTOUGH scripts to automate nested grids, calculate and apply boundary conditions, and prepare well data can be critical for ensuring simulation accuracy and efficiency.
Figure 3: Example of model grid refinement using PyTOUGH script (O’Sullivan et al., 2013) 3. GEOTHERMAL MODEL DEVELOPMENT
The authors will outline the processes involved in developing a geothermal model using best practices defined by the Geothermal Institute, the University of Auckland. These approaches have been followed by geothermal developers worldwide and have become industry standards.
3.1 Developing 3D Conceptual Model
A conceptual model should include topography, stratigraphy, lithology, temperature distribution (isotherms), geological structure, hydrothermal alteration, heat source, fluid phases (liquid, two-phase, steam), chemistry, thermal manifestations, hydrological system, recharge area, and fluid flows within the system (upflows, outflows, inflow/recharge). It must accurately represent as much of the actual
Desktop (Linux)
High Performance Cluster (NeSI - Linux) Simulator
Number of Cores
AUTOUGH2 1
Waiwera 1
Waiwera 4
Waiwera 2
Waiwera 5
Waiwera 40
Synthetic
Natural
State 1257 s (1562) 1420 s (709) 454 s (705) 572 s (701) 280 s (701) 78 s (682) Production
History 32.4 s (56) 78 s (76) 36 s (76) 30 s (76) 19 s (76) 15 s (76)
San Jacinto
Natural
State >10000
(>10000) 6216 s (625) 2101 s (647) 2461 s (575) 1131 s (587) 332 s (615) Production
History 25668 s (708) 5066 s (278) 1713 s (318) 2545 s (306) 1211 s (315) 430 s (293)
system as possible. Additionally, creating a 3D digital conceptual model enables us to investigate each component individually, and one of the best software for creating a 3D digital conceptual model of a geothermal system is Leapfrog Geothermal. In general, it is good practice to ensure that the 3D digital conceptual model encompasses an area much larger than the resistivity boundary , to incorporate all the characteristics above (O’Sullivan et al., 2017; O’Sullivan, 2020).
Figure 4: Required components in developing a 3D digital conceptual model (modified from O’Sullivan et al., 2017) The process of developing a conceptual model begins immediately after completing a 3G exploration survey. It will continue throughout the duration of the project as new data are added from activities such as exploration, delineation, development drilling, well testing, and reservoir monitoring. When no well data are available during the first exploration phase, the conceptual model created can be refined using natural state simulation results. This is possible because, currently, making a numerical model is relatively simple and easily integrated with the conceptual model being developed.
3.2 Developing Numerical Model
A numerical model should be made in conjunction with the conceptual model of the geothermal system in order for the simulation to be representative of the actual state of the system. Using the 3D digital conceptual model, we can automatically construct the TOUGH2 model grid and its input file in Leapfrog. Thus, geological units are automatically assigned to the numerical model, conforming to the digital geological model. Additionally, it is good practice to align the TOUGH2 model with the important structures in the geothermal system. Leapfrog functionality enables us to rotate the numerical model grid to achieve this. The TOUGH2 model’s grid size should be sufficiently large to accommodate closed side boundary conditions. In general, the model’s grid should extend at least 4–5 kilometres outside the resistivity boundary of the geothermal system. For most models open side boundaries should be avoided as they may result in highly erroneous simulation forecasts.
Figure 5: Example of a numerical model in Leapfrog (Nugraha et al., 2018) 3.2.1 Selecting model grid types
Rectangular and Voronoi meshes are the most often utilised for the grid in TOUGH2 models. Croucher and O’Sullivan (2013) state that Voronoi grids are generated using a given set of block centres (and a border), with the faces defined by the perpendicular bisectors of the lines connecting pairs of blocks. As a result, all connections in a Voronoi grid are orthogonal. Voronoi grids are excellent for representing directional faults with a 90° dipping angle. However, their representation of dipping faults is problematic and manually calibrating a model using a Voronoi grid is less common and can be laborious. Additionally, Voronoi grid generators can generate blocks with extremely small faces, potentially resulting in numerical instability. By contrast, a rectangular grid also allows for orthogonal connections, and they have a relatively straightforward and simple design. As a result, manual calibration is easy to carry out, especially in the intital stages of model development. Issues may arise when higher resolution blocks are required in certain areas of the model to simulate the physical processes more accurately. Local refinement can be used to avoid more comput ationally expensive stretched rectangular grids.
Care must be taken with local refinment because at the margin of the area of interest, the transition zone, or zone between the refined and unrefined blocks, might result in non-orthogonal connections. Croucher and O’Sullivan (2013) advocate using either the Voronoi grid or the optimal triangular scheme for local refinement to minimise these issues, as illustrated in Figure 6.
Figure 6: Test results of Voronoi and optimised triangular refinement resulting in zero pressure error for both (modified from Croucher and O’Sullivan, 2013)
3.2.2 Modelling to surface
In some previous modelling studies, the top ground surface of the geothermal system has not been included in the numerical model.
Instead these models prescribe a constant P,T water table at the top of the model. This conventional assumption makes the model unable to simulate the near-surface physical behaviour. Thus the model is unable to predict the effect of reservoir production on the near-surface environment. Based on the experiences of the authors in modelling geothermal developments in New Zealand and Indonesia, unplanned environmental effects, such as the disappearance of hot springs, the emergence of new areas of steaming ground areas and land subsidence often occur. Furthermore, many of these effects have occurred in places that are sacred to the indigenous people. These unfortunate incidences have proven to cause much resistance from the local villagers to new geothermal projects. Therefore, the use of the water table as the top of the model should not be applied anymore. Changes in the shallow sub-surface pressure and temperature conditions can also affect geothermal production, particularly from shallow steam caps or if shallow cold groundwater incursion occurs.
The current best practice is to extend the geothermal model up to the ground surface following the topography of the area. This approach allows either water, steam or air to move up or down through the grid blocks, mimicking the near-surface physical behaviour and accurately representing the interaction of the deep geothermal system, the shallow subsurface and the surface features. O’Sullivan et al.
(2015), Ratouis et al. (2015), and O’Sullivan et al. (2016) have demonstrated the application of this approach in their studies investigating the extensive extraction effect on important geothermal surface features and to support environmental monitoring of the protected Rotorua geothermal system. Figure 7 below shows an example of modelling up to the surface with simulation results of the Orakeikorako geothermal model that match with the measured steaming ground area for the field.
Figure 7: Example of a model that accurately simulates the near-surface physical processes of the thermal feature by including the topography of the geothermal area (O’Sullivan et al., 2016)
3.2.3 Setting boundary condition
Another important aspect of model development is setting up the boundary conditions of the model. The types of boundary conditions used are different for sides, top, and base. The following descriptions of the best practice in setting up boundary conditions are summarised from Ratouis et al. (2015), O’Sullivan et al. (2016), O’Sullivan & O’Sullivan (2016a), Nugraha et al. (2018), and Nugraha and O’Sullivan (2020):
Top Boundary: As previously discussed in the previous sub-section, the current best practice is using the surface topography data as the top of the model boundary. If a part of a geothermal system is submerged under a water body such as a lake or an ocean, the bathymetry data of that particular area is included in the model. At the top of the model, dry atmospheric conditions are assigned to the dry-land surface, with a pressure of 1 bar and a mean temperature equal to the field’s ambient temperature. The environment beneath the water
body vary according to the hydrostatic pressure, and the temperature fluctuates based on the temperature data collected. The model uses the air-water equation of state to simulate mixtures of water and air and incorporate the vadose zone, effectively describing the system up to the surface. The CO2-water equation of state should be used in fields where CO2 is the principal gas. To simulate the surface hydrological system, a Python script is used to apply the yearly rainfall rate and an appropriate infiltration rate (say ~10%). The rock properties of the top layers in the model are then modified using a Python script with attributes corresponding to unconsolidated soil conditions with a large permeability at the ground surface. These model configurations will simulate precipitation infiltrating at the ground surface and migrating to lower elevations following the terrain included in the model.
Side Boundary: For most models the side boundaries are assumed to be no-flow boundaries, meaning that no heat or mass enters or leaves the system. The side boundaries should be positioned far enough away from the geothermal system to ensure no boundary condition effects, say, approximately 4–5 kilometres from the system’s resistivity boundary. If the model requires an open side boundary for the production history simulation, either recharge boundary conditions can be added allowing heat or mass to enter of leave the system, or the model can be expanded in size to allow for natural recharge.
Base Boundary: The model’s base boundary should be sufficiently deep to permit convective heat transfer in the system. The model’s bottom layer should be at least two kilometres away from the deepest predicted production zones. Additionally, a heat flux of 65-80 mW/m2 is allocated to the bottom layer as the background heat flow for the sytem. The hot upflow is then represented by the injection of hot water into the relevant faults or fractures.
Some geothermal models have used a ‘hot-plate’ approach, which assumes a constant high temperature and pressure at the base of the model. Using a hot plate boundary can provide an infinite source of hot, high pressure water (O’Sullivan & O’Sullivan, 2016b). For future scenario simulations, a spurious quasi-steady-state may be established where deep recharge from the hot plate matches the defined net production rate (namely, the difference between production and injection), assuming lateral recharge and recharge from above is negligible. Whenever this happens, the model predicts that the project’s required steam flow will persist indefinitely . This issue can be somewhat prevented or at least delayed by deepening the model and separating the hot plate from the production zone. Because of this issue, it is preferable to utilise a mass flux boundary condition rather than a hot plate boundary condition.
3.3 Linking Conceptual Model And Numerical Model
As Burnell et al. (2012) remarked, the initiative to connect the geological model to the TOUGH2 model through Leapfrog became a trend in New Zealand at that time. It remains a key requirement for geothermal developers to have the numerical geothermal model clearly linked to a 3D digital conceptual model while conducting geothermal modelling projects. O’Sullivan et al. (2017) developed a fully integrated modelling work-flow that has been utilised widely in the industry for generating geothermal numerical models.
Figure 8: Integrated geothermal modelling workflow developed by Geothermal Institute at the University of Auckland (O’Sullivan et al., 2017)
Figure 8 illustrates the work-flow by which Leapfrog Geothermal connects the multidisciplinary data to form the digital conceptual model.
Leapfrog integrates with PyTOUGH scripts to provide bi-directional integration with TOUGH2 models. As O’Sullivan et al. (2017) noted, utilising this work-flow to accomplish a tight coupling in both the conceptual and reservoir models adds substantial benefit to the geothermal modelling process in some aspects:
It guarantees that the conceptual and reservoir models have coherent and well-documented structures.
Both models can be visualised in a meaningful, consistent 3D environment.
It simplifies the process of updating the conceptual model with new data and can apply the necessary changes to the reservoir model.
It substantially decreases the time needed to establish a reservoir model, allowing it to be developed more quickly and by less advanced reservoir modellers.
It enables specialists from various disciplines to collaborate and interact using a centralised repository of field data and reservoir modelling results.
4. MODEL CALIBRATION
When conducting calibration, the goal is to achieve a good match between the model simulation results and the measured field data.
Calibration can be performed manually or automatically via inverse modelling. Automatic calibration cannot replace manual calibration but is typically designed to improve the manual calibration results. Thus, automatic calibration will support manual calibration, increasing the model’s robustness and reliability. Two software packages for automatic calibration are widely used: iTOUGH2 (Finsterle, 2004) and PEST (Doherty, 2016). The following sub-sections will detail the data typically used for model calibration at each modelling stage.
4.1 Calibrating Natural State Model
When calibrating a natural state model, the rock permeabilities, the location of the upflow zone, and the mass rate and enthalpy of the hot geothermal upflow fluid are adjusted until a good match is achieved between the model and the data for downhole temperatures, as well as the positions and strength of surface features (e.g., hot springs, steaming ground, large shallow temperature gradients). Downhole pressure measurements can also be used for calibration but care must be taken to ensure the wellbore pressure measurements are correctly interpreted to reservoir conditions. The example in Figure 9 illustrates a calibrated natural state model that corresponds to the temperatures and locations of surface features.
Figure 9: Temperatures in the top blocks of the calibrated natural state model are satisfactorily matched with the location and temperature of surface features (Nugraha and O’Sullivan, 2020)
After each adjustment of model parameters, the natural model must be run until very large time steps are obtained, indicating a stable steady-state has been achieved (O’Sullivan & O’Sullivan, 2016a). By utilising Waiwera, these natural state simulation can be completed in a matter of minutes, significantly speeding up the calibration process which can take several days to weeks, depending on the complexity of the geothermal system. The data required for calibrating the natural state model are summarised in Table 2. If the model is developed during the early stages of exploration, before the well data are available, the calibration process can be conducted using available field data derived from 3G survey activities.
4.2 Calibrating Production Model
Once the production history information for the field is available, the next step is to calibrate the production model. According to O’Sullivan & O’Sullivan (2016a), this process entails adjusting porosities and local permeabilities to match observed changes in production enthalpies and downhole pressures throughout the specified period. The model uses as inputs the measured injection and production rates. The natural state model should be re-run after each parameter adjustment to ensure that the initial conditions for the production history simulation are consistent with the new parameter values. The data required for production history matching are also included in Table 2.
Table 2: Required data for model calibration derived from Ratouis et al. (2015), O’Sullivan et al. (2016), O’Sullivan &
O’Sullivan (2016a), Nugraha et al. (2018), and Nugraha and O’Sullivan (2020) Model Calibration Data
Data Category Collected Data Description
Geology
Thermal feature locations and types
(UTM coordinates; fumarole, hot spring, steaming ground, etc.)
Indicate upflow and outflow of the system for natural state simulation
Location permeable zones (Faults, week zone, fractures)
Identify the governing structures of the system for natural state simulation
Water table levels
Used as the references for calibrating the top of the saturated zone in the model in natural state simulation
Geophysics
Surface heat flow survey
(Shallow temperature distribution)
Used as the references for calibrating the shallow temperature distribution in the model in natural state simulation
Clay cap profile from MT
(Bottom of conductive layer prediction)
The simulation temperature in that area should achieve ~180-200OC (before having well data) in natural state simulation
Geochemistry
Thermal manifestations data
(Area, temperature, flow rate, gas flux, and chemical content)
Used as the references for the thermal manifestation blocks in the model for natural state simulation
Geothermometers
(Deep reservoir temperature prediction)
Used as the references for the reservoir temperature distribution in the model (before having well data) for natural state simulation
Well data
Well logging
(Downhole temperature and pressure profile after heating up process, feed zones)
Used as the main references for calibrating the temperature distribution in the model in natural state simulation
Well testing
(Productivity/injectivity index, permeability, well performance, dryness, enthalpy, mass flow)
Used as the references for production history matching and future scenarios simulation
Well sampling at the wellhead
(Fluid chemistry, pH, temperature, pressure)
Used as the references for production history matching and future scenarios simulation
Production/Injection history data Used as the references for production history matching
5. FUTURE SCENARIO SIMULATION
Future scenario simulation is the final stage of geothermal modelling. It aims to predict the future behaviour of the system by applying differnt production and injection scenarios to the calibrated model. Future scenario simulations can be conducted using the production model or the natural state model if no production has yet occured. The scenarios are set up so that they define expected options for future production performance, well numbers and locations, reinjection strategy , and make-up wells scheduling. Hence, the field utilization can be optimised and well maintained. Future scenarios also can be used for predicting the effect of production on the environment. These simulation scenarios utilise the well testing and well sampling data (Table 2) as the estimates for the future well properties. The simulations are typically run for 25-35 years, depending on the project (O’Sullivan & O’Sullivan, 2021). Furthermore, uncertainty quantification methods can now be applied to calculate the statistical distributions of forecasts from the calibrated model (Omagbon et al., 2021). Figure 10 gives examples of the model forecasts using the uncertainty quantification method.
Figure 10: Application of uncertainty quantification method in the forecasted future model parameters: (A) forecasted downhole temperature and (B) forecasted steam flow rates and their probabilities (Omagbon et al., 2021) 6. CONCLUSION
A set of up-to-date, proven best practices for geothermal modelling has been compiled from a variety of sources including the experience of the authors working with many modelling projects. These practices have been adopted and applied by geothermal developers worldwide. The authors hope that this summary can serve as a guideline for companies, universities, or consultants who wish to start geothermal modelling projects. By following these guidelines geothermal models can be developed which meet industry standards and provide significant value for supporting the development of the geothermasl project as a whole.
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