Systems, methods, and apparatuses are provided for permeability prediction. The method acquires data associated with one or more geological formations, calculates, using processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters, and trains an artificial neural network for each of the plurality of clusters when the mode of operation is training mode. Further, the method acquires one or more formation properties corresponding to a geological formation, determines using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties, identifies a cluster associated with the one or more formation properties as a function of the log-likelihood score, and predicts a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting permeability, the method comprising: acquiring, using processing circuitry, data associated with one or more geological formations when a mode of operation is training mode; calculating, using the processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters when the mode of operation is training mode; training, using the processing circuitry, an artificial neural network for each of the plurality of clusters when the mode of operation is training mode; acquiring, using communication circuitry, one or more formation properties corresponding to a geological formation when the mode of operation is forecasting mode; determining, using the processing circuitry and the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties when the mode of operation is forecasting mode; identifying, using the processing circuitry, a cluster associated with the one or more formation properties as a function of the log-likelihood score when the mode of operation is forecasting mode; and predicting, using the processing circuitry, a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode. 2. The method of claim 1, wherein the step of training includes generating an artificial neural network structure using a first genetic algorithm, the artificial neural network structure having initial weights determined by a second genetic algorithm. 3. The method of claim 1, further comprising: serving the permeability to one or more host applications to determine a productivity estimate of the geological formation. 4. The method of claim 1, wherein the one or more formation properties include at least one of a micro-spherically focused log, deep resistivity, a porosity log, a density log, water saturation, a neutron porosity log, a caliper log, computed tomography, a density correction log, and gamma ray. 5. The method of claim 1, wherein the mode of operation is determined based on a location of the geological formation. 6. The method of claim 1, further comprising: determining whether a training is valid based on a predetermined accuracy. 7. A system for permeability prediction, the system comprising: communication circuitry configured to acquire data associated with one or more geological formations when operating in a training mode, and acquire one or more formation properties corresponding to a geological formation when a mode of operation is forecasting mode; processing circuitry configured to calculate, using a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters when the mode of operation is training mode, train an artificial neural network for each of the plurality of clusters when the mode of operation is training mode, determine, using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties when the mode of operation is forecasting mode, identify a cluster associated with the one or more formation properties as a function of the log-likelihood score when the mode of operation is forecasting mode, and predict a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode. 8. The system of claim 7, wherein the processing circuitry is further configure to generate an artificial neural network structure using a first genetic algorithm, the artificial neural network structure having initial weights determined by a second genetic algorithm. 9. The system of claim 7, wherein the processing circuitry is further configured to: serve the permeability to one or more host applications to determine a productivity estimate of the geological formation. 10. The system of claim 7, wherein the one or more formation properties include at least one of a micro-spherically focused log, deep resistivity, porosity log, a density log, water saturation, a neutron porosity log, a caliper log, computed tomography, a density correction log, and gamma ray. 11. The system of claim 7, wherein the mode of operation is determined based on a location of the geological formation. 12. The system of claim 7, wherein the processing circuitry is further configured to: determine whether a training is valid based on a predetermined accuracy. 13. A non-transitory computer readable medium storing computer-readable instructions therein which when executed by a computer cause the computer to perform a method for predicting permeability, the method comprising: acquiring data associated with one or more geological formations when a mode of operation is training mode; calculating, using a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters when the mode of operation is training mode; training an artificial neural network for each of the plurality of clusters when the mode of operation is training mode; acquiring one or more formation properties corresponding to a geological formation when the mode of operation is forecasting mode; determining, using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties when the mode of operation is forecasting mode; identifying a cluster associated with the one or more formation properties as a function of the log-likelihood score when the mode of operation is forecasting mode; and predicting a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode.