Machine learning model predicting hydrothermal dolomitisation for future coupling of basin modelling and geochemical simulations

• Published in: Chemical geology Vol. 637; p. 121676
• Main Authors: Collard, N., Faney, T., Teboul, P.A., Bachaud, P., Cacas-Stentz, M.C., Gout, C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 20-10-2023; Elsevier
• Subjects: Carbon storage site screening; Dolomitisation; Geochemical modelling; Machine learning; Mathematics; Neural network; Reservoir distribution; Geochemical modelling; Neural network; Carbon storage site screening; Dolomitisation; Reservoir distribution; Machine learning
• ISSN: 0009-2541; 1872-6836
• DOI: 10.1016/j.chemgeo.2023.121676

In the energy transition context, basin modelling, initially dedicated to the understanding of oil and gas systems, can address several challenges that the sustainable energy sector faces, such as identifying large storage volumes for carbon sequestration, determining the thermal recharge sources for geothermal exploration, or characterising the generation, migration, sink, and accumulation of critical element resources. In this regard, there is a need for spatiotemporal information on the mineral reactions that control the petrophysical properties of the rocks in order to map the best locations in the basin for capturing geothermal energy or storing carbon in basin-wide reservoirs for CO2 mitigation. Hydrothermal dolomitisation is one of these well-known diagenetic reactions that can enhance or destroy reservoir porosity. It has been extensively characterised by static reactive transport modelling, but not kinematic ones, due to the high computational cost of such reactive transport simulations. To address this issue, we built a machine-learning-based (ML) surrogate geochemical model for hydrothermal dolomitisation valid over a broad range of physical and chemical conditions encountered in such a context with the end goal of developing a generic exploration tool to be deployed in the basin calculator ArcTem for emerging energy needs. We train and test five regression algorithms (Random Forest, Gradient Boosting, AdaBoost, Support-Vector Machine, and Multilayer Perceptron) upon a synthetic dataset to predict the dissolved/precipitated amount of calcite and dolomite (referred to as δCaCO3 and δCaMg(CO3)2, respectively). The dataset contains 370 × 103 samples and six features (T, pH, NaCl, Ca, Mg, C) generated by a Latin Hypercube Sampling (LHS) between two end-members: (1) the modern seawater composition and a temperature of 80 °C for the lower bound and (2) a highly saline mantle-derived fluid and a temperature of 250 °C for the upper bound. The target values, i.e. dissolved/precipitated mole number of calcite/dolomite, are obtained by performing thermodynamic equilibrium calculations with the LHS samples as the initial unequilibrated solutions. Our results show that the artificial neural network (ANN) model best performs among the algorithms with rmse of 0.007 mol on δCaMg(CO3)2 prediction and 0.0084 mol on δCaCO3 prediction, R2 values above 0.99, and APE smaller than 0.3%, indicating its good generalisability to provide accurate predictions on unseen data in the studied range. Further, the ANN model is ∼1000 times faster than the geochemical calculator when solving the same system at once, illustrating the great potential of ML methods in emulating computationally intensive components of the numerical simulations.
•Machine learning is applied to reproduce hydrothermal dolomitisation equilibrium reaction under non-isothermal conditions.•Five algorithms are evaluated, and their performance is compared.•Neural network is the best-performing surrogate geochemical model with an average per cent error of <0.3% on the predictions.•Applicable on a broad range of physical and chemical conditions encountered in basin modelling.•The surrogate model results in a generic exploration tool for the pre-assessment of sedimentary basins' storage capacity.