Abstract: 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.

Abstract: 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.

Abstract: A method for stimulating a well includes mixing at least one thermochemical with fracturing fluid to create a fracturing fluid mixture, injecting the fracturing fluid mixture into the well, creating an exothermic reaction with the fracturing fluid mixture, generating a pressure pulse in the well from the exothermic reaction, and fracturing a formation around the well with pressure from the pressure pulse and a hydraulic pressure source.

Abstract: A method for the simultaneous removal of filter cake from a wellbore and fracturing of the wellbore using a mixture including a chelating agent and a thermochemical. The method including feeding a mixture into the wellbore, contacting the filter cake with the mixture, reacting the chelating agent and the thermochemical to produce heat and pressure, removing the filter cake from the wellbore, and creating microfractures in the wellbore using pressure produced from the reacting.

Abstract: A method for stimulating a well includes mixing at least one thermochemical with fracturing fluid to create a fracturing fluid mixture, injecting the fracturing fluid mixture into the well, creating an exothermic reaction with the fracturing fluid mixture, generating a pressure pulse in the well from the exothermic reaction, and fracturing a formation around the well with pressure from the pressure pulse and a hydraulic pressure source.

Abstract: A method for the simultaneous removal of filter cake from a wellbore and fracturing of the wellbore using a mixture including a chelating agent and a thermochemical. The method including feeding a mixture into the wellbore, contacting the filter cake with the mixture, reacting the chelating agent and the thermochemical to produce heat and pressure, removing the filter cake from the wellbore, and creating microfractures in the wellbore using pressure produced from the reacting.

Abstract: A barite filter cake removing composition, and single- and multi-stage methods of removing a barite filter cake from a wellbore. The composition comprises at least one polymer removal agent, at least one chelating agent, and at least one converting agent. The single-stage method includes contacting the barite filter cake with the composition to dissolve the barite filter cake from the wellbore. The multi-stage method includes contacting the barite filter cake from the wellbore with at least one polymer removal agent to remove a polymer coat present on the barite filter cake, contacting the barite filter cake with at least one converting agent to convert barium sulfate in the barite filter cake to a barium salt of carbonate, formate, cyanide, nitrate, and/or chloride, and removing the barium salt of carbonate, formate, cyanide, nitrate, and/or chloride with at least one chelating agent.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A barite filter cake removing composition, and single- and multi-stage methods of removing a barite filter cake from a wellbore. The composition comprises at least one polymer removal agent, at least one chelating agent, and at least one converting agent. The single-stage method includes contacting the barite filter cake with the composition to dissolve the barite filter cake from the wellbore. The multi-stage method includes contacting the barite filter cake from the wellbore with at least one polymer removal agent to remove a polymer coat present on the barite filter cake, contacting the barite filter cake with at least one converting agent to convert barium sulfate in the barite filter cake to a barium salt of carbonate, formate, cyanide, nitrate, and/or chloride, and removing the barium salt of carbonate, formate, cyanide, nitrate, and/or chloride with at least one chelating agent.

Abstract: A barite filter cake removing composition, and single- and multi-stage methods of removing a barite filter cake from a wellbore. The composition comprises at least one polymer removal agent, at least one chelating agent, and at least one converting agent. The single-stage method includes contacting the barite filter cake with the composition to dissolve the barite filter cake from the wellbore. The multi-stage method includes contacting the barite filter cake from the wellbore with at least one polymer removal agent to remove a polymer coat present on the barite filter cake, contacting the barite filter cake with at least one converting agent to convert barium sulfate in the barite filter cake to a barium salt of carbonate, formate, cyanide, nitrate, and/or chloride, and removing the barium salt of carbonate, formate, cyanide, nitrate, and/or chloride with at least one chelating agent.

Abstract: 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.

Abstract: A barite filter cake removing composition, and single- and multi-stage methods of removing a barite filter cake from a wellbore. The composition comprises at least one polymer removal agent, at least one chelating agent, and at least one converting agent. The single-stage method includes contacting the barite filter cake with the composition to dissolve the barite filter cake from the wellbore. The multi-stage method includes contacting the barite filter cake from the wellbore with at least one polymer removal agent to remove a polymer coat present on the barite filter cake, contacting the barite filter cake with at least one converting agent to convert barium sulfate in the barite filter cake to a barium salt of carbonate, formate, cyanide, nitrate, and/or chloride, and removing the barium salt of carbonate, formate, cyanide, nitrate, and/or chloride with at least one chelating agent.

Abstract: The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.

Abstract: The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.