Methods for selection of composition and concentration of a corrosion inhibitor package used in stimulation of subterranean formations involving acid-containing fluids

Abstract

The present disclosure relates to a method for stimulating a subterranean formation that includes selecting a wellbore for an acid stimulation treatment and initial pumping schedule using a wellbore treatment fluid system; obtaining information on metal used for pipe in the wellbore and a maximum corrosion rate threshold and/or a pitting index threshold to perform the acid stimulation treatment; determining composition and concentration of a corrosion inhibitor package for the wellbore treatment fluid system to obtain a corrosion rate and/or a pitting index of surfaces of the metal; updating the pumping schedule based on the determined composition and concentration of the corrosion inhibitor package; and performing hydraulic acid fracturing or an acidizing treatment using the updated pumping schedule.

1 . A method for stimulating a subterranean formation, comprising:
 (a) selecting a wellbore for an acid stimulation treatment and initial pumping schedule using a wellbore treatment fluid system;   (b) obtaining information on metal used for pipe in the wellbore and a maximum corrosion rate threshold and/or a pitting index threshold to perform the acid stimulation treatment;   (c) determining composition and concentration of a corrosion inhibitor package for the wellbore treatment fluid system to obtain a corrosion rate and/or a pitting index of surfaces of the metal;   (d) updating the pumping schedule based on the determined composition and concentration of the corrosion inhibitor package; and   (e) performing hydraulic acid fracturing or an acidizing treatment using the updated pumping schedule.   
     
     
         2 . The method of  claim 1 , wherein step (c) comprises:
 setting an initial composition and an initial concentration of the corrosion inhibitor package;   executing an algorithm to predict the corrosion rate and/or the pitting index of surfaces of the metal;   if the predicted corrosion rate is above a threshold, then the fluid system composition is automatically modified by increasing or decreasing concentrations of additives or adding or removing additives until the fluid system confirms that the predicted corrosion rate and/or the predicted pitting index are below respective thresholds; and   if the predicted corrosion rate is below the threshold, then the required fluid system is confirmed.   
     
     
         3 . The method of  claim 2 , wherein the algorithm comprises:
 finding, in a database, a nearest fluid system composition associated with a known corrosion rate and pitting index;   using the known corrosion rate and pitting index as initial predictions;   determining whether distances between the selected fluid system and available fluid systems in the database are above a tolerance; and   performing an experiment and updating the database by using an obtained value as the predicted value.   
     
     
         4 . The method of  claim 2 , wherein the algorithm comprises:
 building a proxy model based on a machine learning algorithm; and   predicting the corrosion rate and/or the pitting index for a given fluid system composition using the proxy model.   
     
     
         5 . The method of  claim 1 , wherein step (c) comprises:
 setting the fluid system composition to include an acid-containing fluid with a known set of chemical additives, wherein concentrations of the acid-containing fluid and the set of chemical additives are not known;   executing an algorithm to find all compositions of the fluid system that confirm that the predicted corrosion rate and/or the predicted pitting index are below respective thresholds; and   selecting a fluid composition from the compositions based on availability of chemical components to perform stimulation in the subterranean formation.   
     
     
         6 . The method of  claim 5 , wherein step (c) comprises selecting the fluid composition from the compositions based on an associated cost and/or environmental considerations. 
     
     
         7 . The method of  claim 1 , wherein step (c) comprises:
 setting the fluid system composition to include an acid-containing fluid with a known set of chemical additives, wherein concentrations of the acid-containing fluid and the set of chemical additives are known and defined as input to an algorithm; and   executing the algorithm to find the corrosion rate and/or the pitting index for different metal types.   
     
     
         8 . The method of  claim 1 , wherein step (c) is performed in substantially real-time at a wellsite for situations inclusive of:
 a) low rate, long periods of acid pumping due to limited formation injectivity; and   b) acid cycling where pumps are turned on and off at intervals due to lack of stable rate injectivity into the subterranean formation.   
     
     
         9 . The method of  claim 1 , wherein step (c) is performed using a machine learning based algorithm or a predictive modeling architecture inclusive of regression, classification, clustering techniques based on bagging, boosting, or some combination thereof. 
     
     
         10 . The method of  claim 1 , wherein step (c) comprises utilizing an artificial intelligence model for all types and environments of fracturing, stimulation treatments, inclusive of:
 a) proppant fracturing, acid fracturing, matrix acidizing, sand control, or water control treatments;   b) all clastic, carbonate, and volcanic rock geologic sequences;   c) geothermal wells where power is extracted from the subsurface steam/heat extraction;   d) vertical, deviated, and horizontal wells; and   e) all completion types such as cemented cased hole, open hole, open hole with fracturing sleeves and isolation packers, and pre-perforated liners.   
     
     
         11 . The method of  claim 1 , wherein step (c) comprises optimizing a cost of the corrosion inhibitor package. 
     
     
         12 . The method of  claim 1 , wherein step (c) comprises reducing formation damage incurred by the corrosion inhibitor chemistry. 
     
     
         13 . A data processing system, comprising:
 one or more processors configured to execute processor-executable instructions stored in memory media, wherein the processor-executable instructions, when executed by the one or more processors, cause the data processing system to:
 (a) select a wellbore for an acid stimulation treatment and initial pumping schedule using a wellbore treatment fluid system; 
 (b) obtain information on metal used for pipe in the wellbore and a maximum corrosion rate threshold and/or a pitting index threshold to perform the acid stimulation treatment; 
 (c) determine composition and concentration of a corrosion inhibitor package for the wellbore treatment fluid system to obtain a corrosion rate and/or a pitting index of surfaces of the metal; 
 (d) update the pumping schedule based on the determined composition and concentration of the corrosion inhibitor package; and 
 (e) automatically control operating parameters of hydraulic acid fracturing or an acidizing treatment using the updated pumping schedule. 
   
     
     
         14 . The data processing system of  claim 13 , wherein step (c) comprises:
 setting an initial composition and an initial concentration of the corrosion inhibitor package;   executing an algorithm to predict the corrosion rate and/or the pitting index of surfaces of the metal;   if the predicted corrosion rate is above a threshold, then the fluid system composition is automatically modified by increasing or decreasing concentrations of additives or adding or removing additives until the fluid system confirms that the predicted corrosion rate and/or the predicted pitting index are below respective thresholds; and   if the predicted corrosion rate is below the threshold, then the required fluid system is confirmed.   
     
     
         15 . The data processing system of  claim 14 , wherein the algorithm comprises:
 finding, in a database, a nearest fluid system composition associated with a known corrosion rate and pitting index;   using the known corrosion rate and pitting index as initial predictions;   determining whether distances between the selected fluid system and available fluid systems in the database are above a tolerance; and   performing an experiment and updating the database by using an obtained value as the predicted value.   
     
     
         16 . The data processing system of  claim 14 , wherein the algorithm comprises:
 building a proxy model based on a machine learning algorithm; and   predicting the corrosion rate and/or the pitting index for a given fluid system composition using the proxy model.   
     
     
         17 . The data processing system of  claim 13 , wherein step (c) comprises:
 setting the fluid system composition to include an acid-containing fluid with a known set of chemical additives, wherein concentrations of the acid-containing fluid and the set of chemical additives are not known;   executing an algorithm to find all compositions of the fluid system that confirm that the predicted corrosion rate and/or the predicted pitting index are below respective thresholds; and   selecting a fluid composition from the compositions based on availability of chemical components to perform stimulation in the subterranean formation.   
     
     
         18 . The data processing system of  claim 17 , wherein step (c) comprises selecting the fluid composition from the compositions based on an associated cost and/or environmental considerations. 
     
     
         19 . The data processing system of  claim 13 , wherein step (c) comprises:
 setting the fluid system composition to include an acid-containing fluid with a known set of chemical additives, wherein concentrations of the acid-containing fluid and the set of chemical additives are known and defined as input to an algorithm; and   executing the algorithm to find the corrosion rate and/or the pitting index for different metal types.   
     
     
         20 . A data processing system configured to:
 select a wellbore for an acid stimulation treatment and initial pumping schedule using a wellbore treatment fluid system;   obtain information on metal used for pipe in the wellbore and a maximum corrosion rate threshold and/or a pitting index threshold to perform the acid stimulation treatment;   determine composition and concentration of a corrosion inhibitor package for the wellbore treatment fluid system to obtain a corrosion rate and/or a pitting index of surfaces of the metal;   update the pumping schedule based on the determined composition and concentration of the corrosion inhibitor package; and   automatically control operating parameters of hydraulic acid fracturing or an acidizing treatment using the updated pumping schedule.