Abstract: A method and a system for predicting true sand resistivity in laminated low resistivity sands is disclosed. The method includes obtaining basic log values of laminated low resistivity sands and determining a volume of solids, a volume of fluids, and first reservoir parameters using a multimineral formation evaluation based on the basic log values of the laminated low resistivity sands. Further, a volume of sand, a volume of silt, a volume of clay, and a volume of shale are determined using a silty sand analysis based on the determined volume of solids, the determined volume of fluids, and the first determined reservoir parameters. Additionally, second determined parameters are inputted to a trained machine learning model to determine the true sand resistivity and the true sand resistivity is predicted using the trained machine learning model based on the second determined parameters.

Abstract: Some implementations provide a method that includes: accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore; splitting the stream of input data into a training set of input data and a testing set of input data; training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data; evaluating the machine learning model using the testing set of input data; and in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.

Abstract: Methods and systems, including computer programs encoded on a computer storage medium are described for implementing a system that predicts bound fluid volumes for use in well drilling operations at a subsurface region. The system derives inputs from log data generated for one or more wells. A predictive model of the system processes each of the inputs based on algorithms used to train the predictive model. Based on the processing, the model computes correlations between data points in the log data and reference parameters that are indicative of a fluid volume at the subsurface region. Based on the computed correlation, the model generates a prediction that includes a bound fluid volume for the subsurface region. The system determines a characteristic of a non-hydrocarbon fluid at a first zone of the subsurface region based on the bound fluid volume.