# US 7,610,251 B2 · Well control systems and associated methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,610,251 B2 - Document ID: US7610251B2 - Status: Granted - Kind: B2 - Application number: 11/333,768 - Filed: 2006-01-17 - Published: 2007-07-19 - Granted: 2009-10-27 - Earliest claimed: 2006-01-17 - Assignees: Halliburton Energy Services, Inc. - Inventors: Sara Shayegi; Craig W. Godfrey; Dingding Chen; Roger L. Schultz - Web page: https://patentgrep.com/patent/US7610251B2 ## CPC - G05B 13/048: {using a predictor} - E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS ## Abstract Well control systems and associated methods. A well control method includes the steps of drilling a wellbore and predicting a change in flow between the wellbore and a reservoir prior to the change occurring, the predicting step being performed, and the change in flow occurring while drilling. Another well control method includes the steps of: sensing at least one first drilling operation variable while drilling a wellbore, thereby generating first sensed variables; sensing at least one second drilling operation variable while drilling the wellbore, thereby generating second sensed variables; and training a predictive device, using the first and second sensed variables, to predict the second drilling operation variable at a selected time. ## Claims ### Claim 1 A well control method, comprising the steps of: drilling a wellbore, thereby elongating the wellbore; and predicting a change in flow between the wellbore and a reservoir prior to the change occurring, the predicting step being performed during the drilling step, and the change in flow occurring while elongating the wellbore. ### Claim 2 (depends on 1) The method of claim 1, wherein the change in flow is an increased rate of fluid flow from the reservoir into the wellbore. ### Claim 3 (depends on 1) The method of claim 1, wherein the predicting step is performed by a predictive device. ### Claim 4 (depends on 3) The method of claim 3, wherein the predictive device includes a neural network. ### Claim 5 (depends on 3) The method of claim 3, wherein the predictive device includes an artificial intelligence device. ### Claim 6 (depends on 3) The method of claim 3, wherein the predictive device includes a genetic algorithm. ### Claim 7 (depends on 3) The method of claim 3, wherein the predictive device performs a regression analysis. ### Claim 8 (depends on 3) The method of claim 3, wherein the predictive device utilizes granular computing. ### Claim 9 (depends on 3) The method of claim 3, wherein the predictive device includes an adaptive model. ### Claim 10 (depends on 9) The method of claim 9, wherein an output of a first principle model is input to the adaptive model. ### Claim 11 (depends on 3) The method of claim 3, wherein the predictive device includes a nonlinear function which generalizes for real systems. ### Claim 12 (depends on 3) The method of claim 3, wherein the predictive device includes a floating point processing device. ### Claim 13 (depends on 3) The method of claim 3, wherein the predicting step includes adjusting terms in the predictive device based on derivatives of output of the predictive device with respect to the terms. ### Claim 14 (depends on 3) The method of claim 3, further comprising the step of training the predictive device. ### Claim 15 (depends on 14) The method of claim 14, wherein the training step further comprises inputting to the predictive device data obtained during the drilling step. ### Claim 16 (depends on 14) The method of claim 14, wherein the training step further comprises inputting to the predictive device data obtained while drilling at least one prior wellbore. ### Claim 17 (depends on 14) The method of claim 14, wherein the training step further comprises inputting to the predictive device data indicative of past errors in predictions produced by the predictive device. ### Claim 18 (depends on 1) The method of claim 1, wherein the drilling step is performed with an underbalanced condition in the wellbore. ### Claim 19 (depends on 1) The method of claim 1, wherein the drilling step is performed with a balanced condition in the wellbore. ### Claim 20 (depends on 1) The method of claim 1, wherein the drilling step is performed with an overbalanced condition in the wellbore. ### Claim 21 (depends on 1) The method of claim 1, wherein the drilling step is performed while fluid flows from the reservoir into the wellbore. ### Claim 22 (depends on 1) The method of claim 1, wherein the drilling step includes circulating fluid from the reservoir to a surface location. ### Claim 23 (depends on 1) The method of claim 1, wherein the change in flow between the wellbore and the reservoir occurs after the predicting step. ### Claim 24 (depends on 1) The method of claim 1, wherein the change in flow is an increased rate of flow from the wellbore to the reservoir. ### Claim 25 (depends on 1) The method of claim 1, wherein the predicting step further comprises predicting a change in bottom hole pressure during the drilling step. ### Claim 26 (depends on 1) The method of claim 1, wherein the predicting step includes performing regression on a nonlinear function. ### Claim 27 A well control method, comprising the steps of: sensing a drilling operation variable while elongating a wellbore, thereby generating sensed variables; and training a predictive device, using the sensed variables, to predict a value of the drilling operation variables, wherein the drilling operation variable is a change in flow rate between a reservoir and the wellbore. ### Claim 28 (depends on 27) The method of claim 27, wherein the sensing step is performed in the wellbore. ### Claim 29 (depends on 27) The method of claim 27, wherein the sensing step is performed at a surface location. ### Claim 30 (depends on 27) The method of claim 27, wherein the predictive device includes a neural network. ### Claim 31 (depends on 27) The method of claim 27, wherein the predictive device includes an artificial intelligence device. ### Claim 32 (depends on 27) The method of claim 27, wherein the predictive device includes a genetic algorithm. ### Claim 33 (depends on 27) The method of claim 27, wherein the predictive device performs a regression analysis. ### Claim 34 (depends on 27) The method of claim 27, wherein the predictive device utilizes granular computing. ### Claim 35 (depends on 27) The method of claim 27, wherein the predictive device includes an adaptive model. ### Claim 36 (depends on 35) The method of claim 35, wherein an output of a first principle model is input to the adaptive model. ### Claim 37 (depends on 27) The method of claim 27, wherein the predictive device includes a nonlinear function which generalizes for real systems. ### Claim 38 (depends on 27) The method of claim 27, wherein the predictive device includes a floating point processing device. ### Claim 39 (depends on 27) The method of claim 27, wherein the training step includes adjusting terms in the predictive device based on derivatives of output of the predictive device with respect to the terms. ### Claim 40 (depends on 27) The method of claim 27, wherein the training step further comprises inputting to the predictive device data obtained while drilling at least one prior wellbore. ### Claim 41 (depends on 27) The method of claim 27, wherein the training step further comprises inputting to the predictive device data indicative of past errors in predictions produced by the predictive device. ### Claim 42 (depends on 27) The method of claim 27, wherein the drilling operation variable is a rate of fluid flow between a reservoir and the wellbore. ### Claim 43 (depends on 27) The method of claim 27, wherein drilling the wellbore is performed in an underbalanced condition. ### Claim 44 (depends on 27) The method of claim 27, wherein drilling the wellbore is performed in a balanced condition. ### Claim 45 (depends on 27) The method of claim 27, wherein drilling the wellbore is performed in an overbalanced condition. ### Claim 46 (depends on 27) The method of claim 27, wherein the drilling operation variable is at least one of pressure, pressure differential, temperature, flow rate, produced gas rate, produced liquid rate, produced solids rate, true vertical depth, rate of penetration, bottom hole pressure, and fluid properties. ### Claim 47 A well control method, comprising the steps of: sensing at least one first drilling operation variable while elongating a wellbore, thereby generating first sensed variables; sensing at least one second drilling operation variable while elongating the wellbore, thereby generating second sensed variables; and training a predictive device, using the first and second sensed variables, to predict the second drilling operation variable at a selected time, wherein the second drilling operation variable is a change in flow rate between a reservoir and the wellbore. ### Claim 48 (depends on 47) The method of claim 47, further comprising the step of utilizing the trained predictive device to predict the second drilling operation variable at the selected time when the second sensed variable is unavailable. ### Claim 49 (depends on 47) The method of claim 47, wherein the selected time is when the first sensed variable is available, but the second sensed variable is unavailable. ### Claim 50 (depends on 47) The method of claim 47, wherein the second sensed variable is a rate of fluid flow between the wellbore and a reservoir. ### Claim 51 (depends on 47) The method of claim 47, wherein the second sensed variable is at least one of pressure, pressure differential, surface temperature, downhole temperature, multiphase flow rate, and single phase flow rate. ### Claim 52 (depends on 47) The method of claim 47, wherein the second sensed variable is at least one of rate of penetration and bottom hole pressure. ### Claim 53 (depends on 47) The method of claim 47, wherein the predictive device includes a neural network. ### Claim 54 (depends on 47) The method of claim 47, wherein the predictive device includes an artificial intelligence device. ### Claim 55 (depends on 47) The method of claim 47, wherein the predictive device includes a genetic algorithm. ### Claim 56 (depends on 47) The method of claim 47, wherein the predictive device performs a regression analysis. ### Claim 57 (depends on 47) The method of claim 47, wherein the predictive device utilizes granular computing. ### Claim 58 (depends on 47) The method of claim 47, wherein the predictive device includes an adaptive model. ### Claim 59 (depends on 58) The method of claim 58, wherein an output of a first principle model is input to the adaptive model. ### Claim 60 (depends on 47) The method of claim 47, wherein the predictive device includes a nonlinear function which generalizes for real systems. ### Claim 61 (depends on 47) The method of claim 47, wherein the predictive device includes a floating point processing device. ### Claim 62 (depends on 47) The method of claim 47, wherein the training step includes adjusting terms in the predictive device based on derivatives of output of the predictive device with respect to the terms. ### Claim 63 (depends on 47) The method of claim 47, wherein the training step further comprises inputting to the predictive device data obtained while drilling at least one prior wellbore. ### Claim 64 (depends on 47) The method of claim 47, wherein the training step further comprises inputting to the predictive device data indicative of past errors in predictions produced by the predictive device. ### Claim 65 (depends on 47) The method of claim 47, wherein the second drilling operation variable is a rate of fluid flow between a reservoir and the wellbore. ### Claim 66 (depends on 47) The method of claim 47, wherein drilling the wellbore is performed in an underbalanced condition. ### Claim 67 (depends on 47) The method of claim 47, wherein drilling the wellbore is performed in a balanced condition. ### Claim 68 (depends on 47) The method of claim 47, wherein drilling the wellbore is performed in an overbalanced condition. ### Claim 69 (depends on 47) The method of claim 47, wherein the selected time is a time after the training step is performed. ### Claim 70 (depends on 47) The method of claim 47, wherein a flow coefficient Cv of a choke is used in the training step as an input to train the predictive device. ### Claim 71 A well control method, comprising the steps of: sensing at least one first drilling operation variable while elongating a first wellbore, thereby generating first sensed variables; sensing at least one second drilling operation variable while elongating a second wellbore, thereby generating second sensed variables; and training a predictive device, using the first and second sensed variables, to predict the second drilling operation variable at a selected time, wherein the first drilling operation variable is a change in flow rate between a reservoir and the first wellbore. ### Claim 72 (depends on 71) The method of claim 71, further comprising the step of utilizing the trained predictive device to predict the second drilling operation variable at the selected time when the second sensed variable is unavailable. ### Claim 73 (depends on 71) The method of claim 71, wherein the selected time is when the first sensed variable is available, but the second sensed variable is unavailable. ### Claim 74 (depends on 71) The method of claim 71, wherein the second sensed variable is a rate of fluid flow between the second wellbore and a reservoir. ### Claim 75 (depends on 71) The method of claim 71, wherein the second sensed variable is at least one of pressure, pressure differential, surface temperature, downhole temperature, multiphase flow rate, and single phase flow rate. ### Claim 76 (depends on 71) The method of claim 71, wherein the predictive device includes a neural network. ### Claim 77 (depends on 71) The method of claim 71, wherein the predictive device includes an artificial intelligence device. ### Claim 78 (depends on 71) The method of claim 71, wherein the predictive device includes a genetic algorithm. ### Claim 79 (depends on 71) The method of claim 71, wherein the predictive device performs a regression analysis. ### Claim 80 (depends on 71) The method of claim 71, wherein the predictive device utilizes granular computing. ### Claim 81 (depends on 71) The method of claim 71, wherein the predictive device includes an adaptive model. ### Claim 82 (depends on 81) The method of claim 81, wherein an output of a first principle model is input to the adaptive model. ### Claim 83 (depends on 71) The method of claim 71, wherein the predictive device includes a nonlinear function which generalizes for real systems. ### Claim 84 (depends on 71) The method of claim 71, wherein the predictive device includes a floating point processing device. ### Claim 85 (depends on 71) The method of claim 71, wherein the training step includes adjusting terms in the predictive device based on derivatives of output of the predictive device with respect to the terms. ### Claim 86 (depends on 71) The method of claim 71, wherein the training step further comprises inputting to the predictive device data indicative of past errors in predictions produced by the predictive device. ### Claim 87 (depends on 71) The method of claim 71, wherein the first drilling operation variable is a rate of fluid flow between a reservoir and the first wellbore. ### Claim 88 (depends on 71) The method of claim 71, wherein drilling the second wellbore is performed in an underbalanced condition. ### Claim 89 (depends on 71) The method of claim 71, wherein drilling the second wellbore is performed in a balanced condition. ### Claim 90 (depends on 71) The method of claim 71, wherein drilling the second wellbore is performed in an overbalanced condition. ### Claim 91 (depends on 71) The method of claim 71, wherein the first drilling operation variable is at least one of pressure, pressure differential, temperature, and flow rate. ### Claim 92 (depends on 71) The method of claim 71, wherein the selected time is a time after the training step is performed. ### Claim 93 (depends on 1) The method of claim 1, further comprising the step of varying a restriction to flow through a choke in response to the predicting step. ### Claim 94 (depends on 93) The method of claim 93, wherein the varying step alters the predicted change in flow. ### Claim 95 (depends on 27) The method of claim 27, further comprising the step of varying a restriction to flow through a choke based on the value predicted in the training step. ### Claim 96 (depends on 95) The method of claim 95, wherein the varying step alters the predicted value. ### Claim 97 (depends on 47) The method of claim 47, further comprising the step of varying a restriction to flow through a choke based on the second drilling operation variable predicted in the training step. ### Claim 98 (depends on 97) The method of claim 97, wherein the varying step alters the predicted second drilling operation variable. ### Claim 99 (depends on 71) The method of claim 71, further comprising the step of varying a restriction to flow through a choke based on the second drilling operation variable predicted in the training step. ### Claim 100 (depends on 99) The method of claim 99, wherein the varying step alters the predicted second drilling operation variable.