Most interpretations are largely subjective because they are based primarily on well logs, which are non-diagnostic for facies interpretation, integrated with minimal core data. However, a new hybrid approach which combines machine learning and an expert system generates data-driven, reproducible and consistent results for facies prediction across an entire field, which significantly reduces uncertainties when building a conceptual model derived from facies distribution.

Unlike most machine learning models, the prediction is made considering data samples in the context of depositional units. First, the Gamma Ray curve is divided into different depositional units by tracking Gamma Ray value changes. The machine then automatically assigns to each depositional unit a Gamma Ray shape, lithofacies, boundary type and its stacking pattern with adjacent units. These parameters are integrated with a machine learning model built on core data from the studied basin plus any relevant biostratigraphic and then passed through an expert system containing a set of fuzzy rules to yield final probabilities curves for each of depositional facies. The facies above and below each unit, along with the style of each unit boundary, and their associated facies, are determined by looping back through the expert system after making the initial facies assignments, which produces a coherent stratigraphic succession. Outputs include predictions of the three most likely depositional facies for each unit and their respective probabilities.