New approaches for sedimentary basins thermal calibration: towards integration of source rocks Raman spectroscopy and carbonate thermometry
Creators: name: Vergara, Natalia Amanda
name: Corrado, Sveva
name: Gasparrini, Marta
name: Schito, Andrea
name: Michel, Pauline
Publisher: name: figshare
Year: 2023
Subjects: Geology not elsewhere classified
Organic geochemistry
Exploration geochemistry
Isotope geochemistry
Machine learning not elsewhere classified

description: A realistic reconstruction of the time-temperature history of sedimentary basins is critical to understand basin evolution and to predict hydrocarbon (HC) maturation and migration as well as to assess reservoir quality for geothermal and oil and gas exploration purposes. One of the classical approaches for the reconstruction of thermal evolution in sedimentary basins is thermal modelling calibrated against thermal constrains (e.g., vitrinite reflectance, FI microthermometry, % of illite in illite-smectite mixed layers, etc). Thermal calibration of numerical models requires a suitable distribution of paleo-temperature information along the basin and a good understanding on the application limits and reliability of each thermometric tool when applied to different materials (i.e. siliciclastics, carbonates, organic matter, etc.). The present doctoral research project aims to acquire a better understanding on the limitations, advantages and reliability of some conventional (i.e. FIM, vitrinite reflectance and Rock-Eval pyrolysis) and novel (i.e. ∆47 thermometry and Raman spectroscopy) thermometric methods applied to different materials, specifically to: (1) diagenetic carbonates precipitated within the Upper Triassic siliciclastic reservoirs and (2) Dispersed Organic Matter (DOM) from Lower Toarcian source rock intervals. The first part of this doctoral research focused on the reconstruction of the thermal and past fluid- flow histories of the Norian siliciclastic Chaunoy reservoirs by means of the joint application of FI and ∆47 thermometry. The aim of this study was to acquire a better understanding on the behavior of these two thermometers when applied to natural diagenetic carbonate samples which have been exposed to significant heating (&gt;100 °C) for a prolonged time span. This study included petrographic and geochemical (O-C isotopes) analyses to constrain and reconstruct the carbonate precipitation sequence and fluid geochemistry (δ18Ocarb and δ13C). In addition, the implementation of two different ∆47 solid-state reordering models allowed to asses the occurrence of ∆47 solid-state reordering and to back calculate original precipitation temperatures. Contrasting thermal datasets retrieved from the application of FI and ∆47 thermometry, as well as the different time-temperature evolution scenarios reconstructed from these datasets, underlined two main concerns: (1) the complexity of reconstructing thermal and fluid flow histories when relying independently in one thermometer; (2) the weight reequilibration (FI) and reordering (∆47) phenomena can have in the calculation of true precipitation temperatures, specially for calcites having experienced maximum burial temperatures &gt;100 °C. The second part of this doctoral research focused on the thermal characterization of DOM in the context of diagenetic conditions (T &lt; 200 °C, %Ro &lt; 1%) through conventional and new thermometric tools. The aim of this study was to test the implementation of Raman spectroscopy coupled with unsupervised learning to automatically recognize and classify organic facies on highly heterogeneous DOM at low diagenetic stages to facilitate the assessment of thermal maturity. Samples of OM dispersed in Lower Toarcian source rocks were analyzed with a classical workflow including Rock-Eval pyrolysis and vitrinite reflectance (%Ro). Pyrolysis pointed out kerogen types II, III and II-III with Tmax values ranging from 417 °C to 438 °C. %Ro measurements revealed maturities ranging from 0.45% to 0.65%, indicating immature to early stages of HC generation. The acquired dataset was used as the groundwork for testing three different clustering algorithms (k- means, GMM and DBSCAN) based on the analysis of 1365 Raman spectra principal components acquired from single organic particles. These clustering algorithms were evaluated in terms of (1) their ability to accurately predict the number of clusters, cluster shape and orientation and cluster density and; (2) their accuracy in making predictions. Results showed the best performances were obtained through the application of GMM clustering. GMM allowed to successfully determine cluster geometry and the optimal number of clusters by means of the integration of a Bayesian information criteria. In addition, GMM showed the best prediction performance among the three analyzed algorithms, identifying and classifying different organic fragments with an accuracy &gt; 70%. In particular, GMM identified the fraction corresponding to translucent phytoclasts (i.e. vitrinite) with an accuracy &gt;80%, improving the quality of correlations against known thermal parameters (Tmax and %Ro) by reducing the uncertainties on the calculation of Raman parameters. This work shows how the implementation of unsupervised learning techniques coupled to Raman spectroscopy can be a powerful tool for the characterization of heterogeneous DOM, aiding the assessment of thermal maturity. Calibration of thermal models usually relies on the use of conventional paleo-thermometers (e.g. pyrolysis and %Ro) to retrieve paleo-temperatures information. This thesis explores and contributes to develop two novel paleo-thermometers, highlighting their strengths and weaknesses through a critical integration with traditional techniques, in order to broaden the spectrum of possible calibration parameters. Thus, the robustness and reliability of thermal and fluid flow histories, in particular when traditional paleo-thermometers cannot be applied, could in future strongly benefit of alternative calibrating tools. In turn, well-constrained thermal models would provide a deeper understanding on basin evolution improving exploration predictions.