ABSTRACT As demand for large‐scale seismic data interpretation tasks increases, machine learning‐based horizon autotracking methods have gained attention in the geological and geophysical fields. Although such methods have demonstrated time‐ and cost‐efficiency in large‐scale data interpretation, studies on the expansion of interpreted horizons into the reservoir characterization process are relatively limited. Hence, a reservoir characterization process that can incorporate the machine learning‐interpreted horizons and their structural uncertainties into the reservoir uncertainty assessment is necessary for an efficient reservoir modelling process. The proposed workflow consists of various modelling processes, including horizon construction where machine learning‐interpreted horizons are used instead of manually interpreted horizons, facies modelling and petrophysical modelling. The modelling algorithms are based on stochastic methods: sequential indicator simulation for facies models and Gaussian random function simulation for petrophysical properties. Each modelling process incorporates variables such as variogram parameters, facies ratios and modified porosity values. The results show promising performance in incorporating machine learning‐interpreted horizons into the uncertainty quantification process and analysing their impact by capturing the influence of structural uncertainties of horizons in the final reservoir pore volume.
Lee et al. (Thu,) studied this question.