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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for predicting well markers, comprising: 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, by a hardware processor, 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. 2. The method of claim 1, further comprising, for the second set: grouping and stacking scattered interbedded facies into finer zones for target wells; and determining depth ratios of the finer zones following the grouping. 3. The method of claim 1, further comprising: calculating a mean coverage score for the finer zones for all training wells; and calculating a mean of proportion ratio for finer zones for all training wells. 4. The method of claim 1, further comprising, for finer zones with a mean sameness score less than or equal to the threshold value, grouping and stacking scattered interbedded facies into finer zones for target wells. 5. The method of claim 1, further comprising: feeding dominant facies detection statistics aspects for target well zones and well markers prediction into a memory; and receiving dominant facies identified from the training wells and statistical characteristics of all finer zones in preparation for processing the target wells. 6. The method of claim 2, further comprising calculating a sameness score and a coverage score between NN-predicted facies output and grouped and stacked finer zones for each target well, the grouped and stacked finer zones for each target well obeying the stacking order of finer zones corresponding to the training wells. 7. The method of claim 6, further comprising moving a window of observation (WOO) to calculate a sameness score for each depth range associated with the NN-predicted facies output and grouped and stacked finer zones for each target well. 8. The method of claim 2, further comprising determining the depth for remaining slots above, in-between, and below the calculated top and depth positions of each dominant facies zone for each target well. 9. The method of claim 8, further comprising, filling in zones in available slots above, below, and in-between each dominant facies zone for each target well, the filling of the zones proportional to the determined depth ratios of the finer zones. 10. A non-transitory, computer-readable medium storing computer-readable instructions for predicting well markers, the instructions executable by a hardware processor and configured to: separate 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; calculate, 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); calculate a mean sameness score for the finer zones for all training wells; identify finer zones with a mean sameness score greater than a threshold value as dominant facies zones; and iterate 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. 11. The medium of claim 10, further comprising, for the second set, instructions to: group and stack scattered interbedded facies into finer zones for target wells; determine depth ratios of the finer zones following the grouping; calculate a sameness score and a coverage score between NN-predicted facies output and grouped and stacked finer zones for each target well, the grouped and stacked finer zones for each target well obeying the stacking order of finer zones corresponding to the training wells; and move a window of observation (WOO) to calculate a sameness score for each depth range associated with the NN-predicted facies output and grouped and stacked finer zones for each target well. 12. The medium of claim 10, further comprising instructions to: calculate a mean coverage score for the finer zones for all training wells; and calculate a mean of proportion ratio for finer zones for all training wells. 13. The medium of claim 10, further comprising, for finer zones with a mean sameness score less than or equal to the threshold value, instructions to group and stack scattered interbedded facies into finer zones for target wells. 14. The medium of claim 10, further comprising instructions to: feed dominant facies detection statistics aspects for target well zones and well markers prediction into a memory; and receive dominant facies identified from the training wells and statistical characteristics of all finer zones in preparation for processing the target wells. 15. The medium of claim 11, further comprising instructions to: determine the depth for remaining slots above, in-between, and below the calculated top and depth positions of each dominant facies zone for each target well; and fill in zones in available slots above, below, and in-between each dominant facies zone for each target well, the filling of the zones proportional to the determined depth ratios of the finer zones. 16. A computer-implemented system for predicting well markers, comprising: at least one hardware processor interoperably coupled with a memory storage and configured to: separate 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; calculate, 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); calculate a mean sameness score for the finer zones for all training wells; identify finer zones with a mean sameness score greater than a threshold value as dominant facies zones; and iterate 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. 17. The system of claim 16, further configured, for the second set, to: group and stack scattered interbedded facies into finer zones for target wells; determine depth ratios of the finer zones following the grouping; calculate a sameness score and a coverage score between NN-predicted facies output and grouped and stacked finer zones for each target well, the grouped and stacked finer zones for each target well obeying the stacking order of finer zones corresponding to the training wells; move a window of observation (WOO) to calculate a sameness score for each depth range associated with the NN-predicted facies output and grouped and stacked finer zones for each target well; determine the depth for remaining slots above, in-between, and below the calculated top and depth positions of each dominant facies zone for each target well; and fill in zones in available slots above, below, and in-between each dominant facies zone for each target well, the filling of the zones proportional to the determined depth ratios of the finer zones. 18. The system of claim 16, further configured to: calculate a mean coverage score for the finer zones for all training wells; and calculate a mean of proportion ratio for finer zones for all training wells. 19. The system of claim 16, further configured, for finer zones with a mean sameness score less than or equal to the threshold value, to group and stack scattered interbedded facies into finer zones for target wells. 20. The system of claim 16, further configured to: feed dominant facies detection statistics aspects for target well zones and well markers prediction into a memory; and receive dominant facies identified from the training wells and statistical characteristics of all finer zones in preparation for processing the target wells.