Understanding the thermal evolution and fluid flow within the Earth’s subsurface is critical to advancing the global energy transition. As we shift from fossil fuels to more sustainable energy solutions, geothermal energy, carbon capture and storage (CCS), and hydrogen storage have emerged as key technologies. Subsurface thermal modelling provides insights into how heat and fluids migrate through geological formations, impacting the efficiency and long-term viability of these solutions. In particular, carbonate reservoirs — often formed through complex diagenetic processes — play a pivotal role in many energy transition strategies. Their unique porosity and reactivity to fluids influence how heat is stored, CO₂ is trapped, and hydrogen is preserved. By integrating advanced thermal and fluid flow models, we can better predict the behavior of these reservoirs under evolving temperature and pressure conditions, ensuring more reliable and efficient energy systems for a low-carbon future.

The lab has over 17 years of experience in using cutting edge numerical codes, stable isotope methods, and field to thin sections observations to unravel the thermal histories of sedimentary basins.

Clumped isotopes have revolutionized our understanding of the thermal evolution and diagenetic history of carbonate rocks. Unlike traditional geochemical proxies, clumped isotopes measure the natural “clumping” of heavy isotopes within carbonate minerals, providing a direct and independent estimate of the temperature at which the rock formed or was altered. This method offers unparalleled accuracy in reconstructing past thermal conditions, critical for refining subsurface thermal models. In carbonate reservoirs, where diagenetic processes can significantly alter porosity and permeability, clumped isotope analysis allows geoscientists to untangle the complex interplay between temperature, fluid flow, and mineral reactions over geological time. By revealing the thermal fingerprint of diagenetic events, clumped isotopes provide a powerful tool for improving predictions of reservoir behavior — a key factor in optimizing geothermal energy production, carbon storage, and hydrocarbon exploration in the energy transition.

For over 15 years, we ran a clumped isotopes lab at Imperial College that was specialised in applications to the subsurface and the energy transition. If you want to know more about what clumped isotopes are, and how they are measured, you can read this article on our blog. We also have a page on the setup in our former laboratory at Imperial College: as the group transitioned to DERI and focusing on artificial intelligence, we left the lab behind. But we still have a deep understanding of thermal models, and we integrate this into our numerical work.

A few examples of relevant work we have done include reconstructing the thermal history of the Lower Cretaceous Qishn Formation (John, 2015) in Oman, reconstructing the history of fluid flow along fractures in the Jebel Madar salt dome (Herlambang and John, 2023), and a comprehensive study of the clumped isotope signatures of paired dolomite-calcite in Oman and the UAE leading to better constraints on the burial history of the region (Adlan et al, 2023).

This work on thermal history was underpinned by fundamental research within our research group. First, we established the first calibration of clumped isotopes at reservoir temperatures (i.e. 20-200˚C, Kluge et al, 2015): this required building a specliased rig able to control pressure to avoid evaporating water beyond 90˚C. This initial work is still forming the basis of all high-temperature calibrations for clumped isotopes – for instance, we collaborated and contributed temperature-controlled precipitates in the latest multilab “universal” calibration (Anderson et al, 2021). The second important contribution to the field of clumped isotopes is a fully automated, commercially available instrument capable of automatic preparation of clumped isotopes (the IBEX, Davies et al 2021). You can read more about the IBEX on our clumped isotope method page.

Measurements alone are not enough to derive a thermal history that is meaningful to the energy transition. For that, we rely no numerical code. This includes our work on producing community software for clumped isotopes. To be able to keep up with the data coming from the IBEX, we developed a free software called ‘Easotope’  (see John and Bowen, 2016). Easotope is built as a client-server application in Java, and runs on most standard operating systems that support a Java Virtual Machine (JVM). Easotope is capable of reading the raw format of most mass spectrometer files, saves all data on a server in a central MySQL database, and performs all the necessary corrections based on standards and other parameters. Easotope is a great help for the field of clumped isotope geochemistry, as it makes the management of complex isotope corrections easier and more transparent. This in turn reduces the source of uncertainty in interpreting data. The success of Easotope is testified by the fact that it is not only used in our lab, but also in labs around the world such as the MIT, ETH Zurich, the University of Bergen, and many others.

We also use code to build our thermal and diagenetic models. This can be custom code in Python, or dedicated code. For instance, we developed a generic framework for simple forward modelling that allows a multirealisation approach in parrallel. What this means is that we can define a mathematical model that defines the behaviour of our proxies (such as clumped isotope temperature, stable isotope values, trace elements), and run multiple simulations with different boundary conditions to select the envelop of realisations that best captures our observations. To do this, we built a fast libary in Scala (a JVM language) that we call CoNuS (Concurrent Numerical Simulations). CoNuS effectively is a simple Domain Specific Language (DSL) for forward simulation that can be used in a Jupyter notebook with ease, even for researchers with minimum coding experience. We demonstrated the power of CoNuS in a publication that changed the way we interpret the history of the Basin-and-Range Orogeny in Texas and New Mexico (Robinson et al, 2022).

Here too, artificial intelligence plays an important role. At the moment, we are exploring whether and how AI could be leveraged to improve on the precision of the mass spectrometer measurements. A clumped isotope measurement takes up to 2 hours, and with higher precision the measurement time and sample volume could be droped. We are also exploring how deep learning can help reduce uncertainties in multidimensional models of the subsurface. Finally, diagenetic products can be interpreted in images using deep learning (see our computer vision for geoscience research theme).

It is important to stress that geochemical measurements and numerical simulations alone would not suffice. Observation of cross-cutting relationships between diagenetic phases, and of the relationship between diagenetic deposits, fractures, and the bedding of rocks is absollutely vital.

We have a lot of experience with field-scale diagenesis, for instance, to map fracture related dolomite bodies in the Permian rocks of Oman (Beckert et al, 2015), which we then coupled with hyperspectral imaging at outcrops to distinguish between early and late dolomite (Beckert et al, 2018). Other examples of “structural diagenesis” work where we mix field observations with other methods include understanding fluid-flow in the Puig-reig anticline in Spain (Cruset et al, 2016), work on fracture-related bodies in the Red Sea region of Egypte (Hirani et al, 2018), and understanding geothermal fields in Taiwan (Lu et al, 2018). All these studies and many others required observations at the field and thin section scales to be integrated with isotope measurements and numerical modelling.