G.O.THERM PROJECT. An integrated approach to probe the deep thermal structure of Ireland. Ben Mather, Thomas Farrell, Javier Fullea

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G.O.THERM PROJECT An integrated approach to probe the deep thermal structure of Ireland Ben Mather, Thomas Farrell, Javier Fullea IRC Research for Policy & Society 2016 call Co-sponsored by the GSI and SEAI

Motivation Greenhouse gas emissions 80-95% lower than 1990 levels by 2050. Direct applications of geothermal energy resources can help achieve this ambitious commitment. G.O.THERM is funded through social sciences to produce research that can be disseminated publicly and to policy makers. Assess the probability of viable geothermal locations across Ireland. Heat flow density Predicted temperature at 2.5 km (GSI) Requires multiple datasets that are sensitive to temperature 4 1 2 INSET 3 5 Build on outputs from TELLUS and IRETHERM projects Collect/reassess data to help constrain regions where coverage is sparse. New thermal conductivity measurements Update heat flow predictions using new methodologies 7 8 6 9 10 TEMP ( C) 250 100 60 40 20

Project outline Produce not just a single model of thermal structure, but also quantify its uncertainty. Curie depth Non-unique: many configurations of subsurface rheology may fit our observations. Geological model Different datasets add unique constraints on the thermal regime of the lithosphere. Assimilating multiple datasets reduces the uncertainty of subsurface temperature. Integrated thermal model Di Posterior ~ likelihood x prior n io at P (m d ) P (d m) P (m) Heat flow observations in em ss Bayes theorem:

Project outline Produce not just a single model of thermal structure, but also quantify its uncertainty. Curie depth Non-unique: many configurations of subsurface rheology may fit our observations Geological model Different datasets add unique constraints on the thermal regime of the crust. Assimilating multiple datasets reduces the uncertainty of subsurface temperature. Integrated thermal model Di Posterior ~ likelihood x prior n io at P (m d ) P (d m) P (m) Heat flow observations in em ss Bayes theorem:

Existing heat flow data Surface heat flow data is our primary constraint on subsurface temperature Originally collected in the 1970s and 1980s Mixture of methods to calculate heat flow Some of the original data is missing We reassess these data with a probabilistic methodology to recalculate surface heat flow Conductivity sample #1 Depth k1 Conductivity sample #2 k2 k3 k4 Conductivity sample #3 Temperature Temperature R4 R2 R3 R1 Qs average heat flow Thermal Resistance Conductivity sample #4 Bullard plot Temperature geotherm

Mather et al. 2018 (JGR in review) Palaeoclimate signal The temperature measured in a borehole is usually out of equilibrium with the rest of the crust. Surface temperature history Palaeoclimate is the biggest unaccounted factor in current heat flow data Last glacial retreat ~17 ka where surface temperature was 18 C less than today Novel probabilistic method that applies a correction, while also quantifying uncertainty Corrected Bullard Plot Temperature adjustment

Updated heat flow map Mather et al. 2018 (JGR in review) ~15 mw m-2 increase in surface heat flow (+6 C/km) Parts of Ireland start to look more geothermally viable... Goodman report Contour lines indicate increase in heat flow with a palaeoclimate correction Interpolated using inverse-distance weighting

Updated heat flow map Mather et al. 2018 (JGR in review) ~15 mw m-2 increase in surface heat flow (+6 C/km) Parts of Ireland start to look more geothermally viable... = measurements Goodman report Contour lines indicate increase in heat flow with a palaeoclimate correction Interpolated using inverse-distance weighting

The thermal conductivity conundrum Part of this project was to obtain new thermal conductivity measurements to Increase the coverage across Ireland (e.g. Dalradian Metamorphics) Provide new heat flow estimates GEOSERV found these were much lower than anticipated from historic measurements in mostly limestone (~2.1 W/mK vs. 2.8 W/mK). Significant implications for geothermal potential if top layer has much lower conductivity than previously anticipated. Temperatures extrapolated to 5 km are 10 C higher if thermal conductivity is 25% of current estimates. A change of 0.51km depth to geothermal resources. Scope for a new project to reassess thermal conductivity across Ireland. Temperature at 5km with present conductivity values Temperature at 5km with conductivity reduced by 25%

Project outline Produce not just a single model of thermal structure, but also quantify its uncertainty. Curie depth Non-unique: many configurations of subsurface rheology may fit our observations. Geological model Different datasets add unique constraints on the thermal regime of the crust. Assimilating multiple datasets reduces the uncertainty of subsurface temperature. Integrated thermal model Di Posterior ~ likelihood x prior n io at P (m d ) P (d m) P (m) Heat flow observations in em ss Bayes theorem:

TELLUS about Curie depth The temperature at which magnetic minerals lose their permanent magnetic properties is the Curie temperature Dominant magnetic mineral is magnetite, which has a Curie point of 580 C. TELLUS We can analyse the magnetic anomaly to estimate the depth to the 580 C isotherm i.e. Curie depth. Total field anomaly EMAG2 Magnetic anomaly

PyCurious Python tool for computing the Curie depth from the magnetic anomaly Efficient parallel code Few dependencies Open source software that can be applied to many regions worldwide In Ireland, the depth to 580 C is shallow ~ 15 km https://github.com/brmather/pycurious

Project outline Produce not just a single model of thermal structure, but also quantify its uncertainty. Curie depth Non-unique: many configurations of subsurface rheology may fit our observations. Geological model Different datasets add unique constraints on the thermal regime of the crust. Assimilating multiple datasets reduces the uncertainty of subsurface temperature. Integrated thermal model Di Posterior ~ likelihood x prior n io at P (m d ) P (d m) P (m) Heat flow observations in em ss Bayes theorem:

Geological model of the lithosphere Map thermal conductivity and heat production to various regions of the lithosphere. New model of the lithosphere integrates: Surface geology (GSI map) Vp/Vs ratios Seismic refraction profiles Regional P-wave tomography Model is not perfect, but it can be iteratively improved by the geologists of tomorrow. Distributed in the hope it will be useful IRELAND 1.0 A wide range of potential applications, not limited to geothermal studies: Industy: hydrocarbon potential, mineralisation Research: structural interpretation, electrical conductivity, seismic tomography. IRELAND 1.0 Layer thicknesses

Integrated thermal model (in progress) Assimilates corrected heat flow, Curie depth, and geological prior information. Significant heat refraction in Northern Ireland. High temperatures proximal to the Leinster Granite. Further constrain thermal structure with seismic velocity and gravity anomaly. 3D thermal model Depth to 120C & uncertainty Mather & Fullea 2018 (in prep)

Conclusions Bayesian inversion produces a model with the optimal trade-off between each dataset. Heat flow observations tell us about the integrated heat flux with depth. Curie depth offers a 580 C isotherm constraint. Seismic velocity helps to constrain subsurface rheology (in progress) Gravity anomaly constrains density (in progress) Our thermal model of Ireland (and uncertainty) will be disseminated Temperature maps at various depths online 3D interactive model visualised in Paraview Achievements Reassessment of historic heat flow data IRELAND 1.0 geological model PyCurious open software to find Curie depth Preliminary 3D thermal model of Ireland

Thank you