Basin-scale Geothermal Model Calibration: Experience from the Perth Basin, Australia

Wellmann, Florian; Reid, Lynn B.

doi:10.1016/j.egypro.2014.10.392

Cited by 12 publications

(11 citation statements)

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“…Both optimistic and pessimistic scenarios adopted for the 3D simulations are unlikely as they combine extreme values. Automatic calibration of model parameters used in several geothermal studies (Fuchs and Balling 2016;Hardwick et al 2014;Wellmann and Reid 2014) should be used in a next step to reduce the range of parameters considered in the optimistic and pessimistic scenarios, especially for the Precambrian basement, unattainable by direct characterization methods. However, this can only be possible with equilibrium temperature data to improve BHT correction.…”

Section: Discussionmentioning

confidence: 99%

“…Such models commonly need a large volume of data for temperature predictions to be representative. Examples can be found with numerous studies focused on sedimentary basins benefiting from large and/or high-quality temperature datasets (Calcagno et al 2014;Ebigbo et al 2016;Fuchs and Balling 2016;Hofmann et al 2014;Lenkey et al 2017;Sippel et al 2013;Wellmann and Reid 2014). Studies of these sedimentary basins showed that low-quality input data can make modeling results unreliable, leading to false conclusions (Fuchs and Balling 2016;Gray et al 2012).…”

Section: General Contextmentioning

confidence: 99%

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Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada

et al. 2020

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Anticosti Island is located in the Anticosti sedimentary basin, an Ordovician/Silurian carbonate platform. This platform is mainly composed of limestone and shale with some dolomite and sandstone and reaches up to 5 km depth in the southwest. It overlies a Precambrian basement of the Grenville Province made of magmatic and metamorphic rocks. Like most remote and off-grid regions in Canada, it relies heavily on fossil fuels for energy supplies. An assessment of deep geothermal resources was achieved in this area with the objective of diversifying energy resources to help develop renewable energy for villages deserved by micro-grid systems. Despite sparse and low-quality bottom-hole data (15 wells of 1111 m to 2762 m depth), a 3D temperature model was developed for this sedimentary basin and its underlying Precambrian basement up to 40 km (mantle depth). Quantifying confidence intervals for thermal parameters, namely bottom-hole temperature, thermal conductivity, heat generation rate and mantle heat flux, was paramount to obtain a reliable range of temperature predictions. A high variability of modeled temperature, up to 41% at the base of the sedimentary basin and 70% at mantle depth, remains when trying to constrain input parameters. The lack of equilibrium temperature measurements at depth affects the temperature predictions, both in the sedimentary basin and the Precambrian basement. It is an important issue to solve in further studies. Furthermore, knowledge of the thermal properties of the Precambrian basement of the Grenville Province and its geometry is poor. In addition, there is a wide confidence interval on thermal conductivity of specific lithologies in the Anticosti sedimentary basin. It has a significant impact on temperature predictions at depth and should be improved for studies focusing on electricity production. Despite a wide confidence interval on temperature predictions, geothermal electricity generation from reservoirs at 120 °C or more appears difficult in the current technical and economic context. Electricity generation at a low temperature with an inlet of 70 °C could be achieved at a reservoir depth of 2-4 km, but with a net efficiency of 10-11% (considering a flow rate of 40 l s −1 and a cooling temperature of 5 °C). Direct use of geothermal heat from the deepest part of the sedimentary basin seems to be the most realistic option, provided that sufficiently permeable horizons can be found.

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Section: Discussionmentioning

confidence: 99%

Section: General Contextmentioning

confidence: 99%

Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada

et al. 2020

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“…Perth Basin consists of several faults that make it complicated from a modeling point of view. Simulation and modeling of Perth Basin have been investigated in several works (Wellmann and Reid 2014, Olierook et al 2015, Niederau et al 2017, De la Varga et al 2019. Similar to the previous case study, first, the original mesh based on the real data is generated by Gempy software (Fig.…”

Section: Model Of Perth Basin Australia (Geothermal Resource)mentioning

confidence: 99%

Subdivide and Conquer: Adapting Non-Manifold Subdivision Surfaces Method to Represent and Approximate Complex Geological and Reservoir Structures

Moulaeifard¹,

Wellmann²,

Varga³

et al. 2021

Preprint

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Computer graphics have gradually developed practical techniques to address models with the complex topology, in particular, by parametric surface-based modeling approach. Also, geologists have used this approach because it provides significant gains over grid-based modeling (e.g., implicit modeling) by using grid-free surfaces. However, since this approach originates from computer graphics, not all the capacities and limitations of this approach have been considered and investigated in geological modeling.With this aim in mind, this paper investigates surface-based geological modeling through both geological and computer graphics approaches. NURBS (Non-Uniform Rational B-Splines) and subdivision surfaces, as two main parametric surface-based modeling methods, are investigated, and the strengths and weaknesses of both are compared. Although NURBS surfaces have been used in geological modeling, subdivision surfaces as a standard method in the animation and gaming industries, have received little attention in geological modeling. Subdivision surfaces support arbitrary topologies and watertight modeling, which are quite useful for complex geological modeling.Investigating subdivision schemes with semi-sharp creases is an important part of this paper. Semi-sharp creases show the resistance of a mesh structure to the subdivision procedure, which provides a unique method for complex geological and reservoir modeling. Moreover, non-manifold topologies, as a challenging concept in complex geological and reservoir modeling, are explored, and the subdivision surfaces compatible with non-manifold topology are declared.Finally, the approximation of complex geological structures by the non-manifold subdivision surface method is investigated with two different case studies. The approximated mesh is a simplified and less complex version of the original mesh while the important details of the original mesh are preserved. It not only significantly reduces the cost of modeling and simulation (by reducing the number of vertices to less Preprint-Moulaeifard et al. (Submitted to Mathematical Geoscince) 3 than 5% of the number of vertices of the original mesh) but also, has features such as being watertight, smooth, topologically identical to the main original mesh and controllable with few control points. Keywords Surface-based modeling. Subdivision surfaces. Non-manifold topology. Approximation of geological structures. Grid free. NURBS.

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“…Additionally, an automated model calibration is executed to provide an objective and reproducible way to compensate for the errors of both the physical and geological model. Sensitivity analyses for basin-scale models have been performed before in Noack et al (2012) and also been combined with automated model calibrations (Wellmann and Reid 2014). Also, Fuchs and Balling (2016) consider model calibrations but in their case without sensitivity analyses.…”

Section: Introductionmentioning

confidence: 99%

Crustal-scale thermal models: revisiting the influence of deep boundary conditions

et al. 2022

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The societal importance of geothermal energy is significantly increasing because of its low carbon-dioxide footprint. However, geothermal exploration is also subject to high risks. For a better assessment of these risks, extensive parameter studies are required that improve the understanding of the subsurface. This yields computationally demanding analyses. Often, this is compensated by constructing models with a small vertical extent. This paper demonstrates that this leads to entirely boundary-dominated and hence uninformative models. It demonstrates the indispensable requirement to construct models with a large vertical extent to obtain informative models with respect to the model parameters. For this quantitative investigation, global sensitivity studies are essential since they also consider parameter correlations. To compensate for the computationally demanding nature of the analyses, a physics-based machine learning approach is employed, namely the reduced basis method, instead of reducing the physical dimensionality of the model. The reduced basis method yields a significant cost reduction while preserving the physics and a high accuracy, thus providing a more efficient alternative to considering, for instance, a small vertical extent. The reduction of the mathematical instead of physical space leads to less restrictive models and, hence, maintains the model prediction capabilities. The combination of methods is used for a detailed investigation of the influence of model boundary settings in typical regional-scale geothermal simulations and highlights potential problems.

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Basin-scale Geothermal Model Calibration: Experience from the Perth Basin, Australia

Cited by 12 publications

References 11 publications

Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada

Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada

Subdivide and Conquer: Adapting Non-Manifold Subdivision Surfaces Method to Represent and Approximate Complex Geological and Reservoir Structures

Crustal-scale thermal models: revisiting the influence of deep boundary conditions

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