System and method for implementing machine learning for 3D geo-modeling of petroleum reservoirs

A computer system comprising one or more processors configured to perform operations of:

• accessing measurement data that characterize one or more petrophysical features at a range of depths inside a reservoir, wherein the measurement data are from more than one well locations at the reservoir;
• generating a filtered measurement dataset, comprising:
• detecting anomalous measurement data points by computing, for each measurement data point, a likelihood value using a probability density function based on a mean and a covariance matrix of the one or more petrophysical features, and identifying measurement data points having a likelihood value below a pre-determined threshold;
• and
• automatically removing the anomalous measurement data points;
• identifying a plurality of layers along the range of depths of the reservoir based on the filtered measurement dataset;
• within each layer of the plurality of layers, assigning, using a machine learning algorithm, filtered measurement data points corresponding to the layer among a plurality of clusters that are non-overlapping, wherein the clusters are determined based on the one or more petrophysical features of the measurement data,
• each cluster corresponding to a flow unit that identifies a volume of the reservoir based on one or more of flow potential and rock quality;
• generating a three-dimensional (3D) permeability model of the reservoir based on the flow units of each layer from the plurality of layers, wherein the 3D permeability model comprises grid blocks assigned petrophysical feature values, with flow units allocated to the grid blocks based on the assigned petrophysical feature values;
• and
• simulating a performance of the reservoir based on the 3D permeability model.