Abstract: Well data is retrieved from a connected database for data analysis of well data. One or more Constraints are applied to the retrieved well data to determine if the retrieved well data includes low-quality or incorrect data. Upon determination of the presence of low-quality or incorrect data in the retrieved well data, the determination is reported. The low-quality or incorrect data is analyzed for quality control. The low-quality or incorrect data is remediated based on the quality control analysis.

Abstract: Well data is retrieved from a connected database for data analysis of well data. One or more Constraints are applied to the retrieved well data to determine if the retrieved well data includes low-quality or incorrect data. Upon determination of the presence of low-quality or incorrect data in the retrieved well data, the determination is reported. The low-quality or incorrect data is analyzed for quality control. The low-quality or incorrect data is remediated based on the quality control analysis.

Abstract: This disclosure generally describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for predicting well markers. One computer-implemented method includes separating neural-network (NN)-predicted facies output associated with a plurality of wells into two sets, a first set of NN-predicted facies output of training wells and a second set of NN-predicted facies output of target wells, calculating, for each training well of the plurality of wells, a sameness score between zones of NN-predicted facies output and human-identified lithostratigraphic units (finer zones), calculating a mean sameness score for the finer zones for all training wells, identifying finer zones with a mean sameness score greater than a threshold value as dominant facies zones, and iterating over each target well to calculate a top and depth position of each dominant facies zone determined based upon the NN-predicted facies output of the target well.

Abstract: This disclosure generally describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for predicting well markers. One computer-implemented method includes separating neural-network (NN)-predicted facies output associated with a plurality of wells into two sets, a first set of NN-predicted facies output of training wells and a second set of NN-predicted facies output of target wells, calculating, for each training well of the plurality of wells, a sameness score between zones of NN-predicted facies output and human-identified lithostratigraphic units (finer zones), calculating a mean sameness score for the finer zones for all training wells, identifying finer zones with a mean sameness score greater than a threshold value as dominant facies zones, and iterating over each target well to calculate a top and depth position of each dominant facies zone determined based upon the NN-predicted facies output of the target well.