DETAILED ACTION
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Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities:
In the “Related Applications” section, the patents status should be updated.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 - 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Germain et al., (US 2014/0351183 A1) (hereinafter “Germain”).
Germain discloses;
Regarding claim 1, a method [i.e., a method for predicting operational outcomes of a drilling operation (See abstract)] comprising:
responsive to executing a first digital drilling plan for a first well that specifies plan result [i.e., gathering context data regarding offset wells, wherein the context data relate to a drilling operation that is not measured by physical sensors associated with a drilling operation (para 0018)], controlling field equipment to perform drilling operations for the first well [i.e., an operation performed using the context data that comprise years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)];
acquiring sensor data during the performance of the drilling operations for the first well [i.e., sensor data regarding the offset wells (para 0152) and (see figure 12)];
generating actual results using the sensor data [i.e., placing sensor data regarding the offset wells and the context data into a data store, and identifying a correlation between data in the data store and an operational outcome in a drilling operation (para 0152) and (see figure 12)];
training a machine learning model using the plan results and the actual results to generate a trained machine learning model [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)];
during executing of a second digital drilling plan for a second well, adjusting the second digital drilling plan using the trained machine learning model to generate an adjusted second digital drilling plan [i.e., gathering various data types into the data store enables the ability to query across the entire data set held in the data store, and the operational outcome is predicted based on the model, wherein the model is created based on the data set (para 0066)], [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)];
responsive to executing the adjusted second digital drilling plan for the second well, controlling filed equipment to perform drilling operations for the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 2, the method of claim 1, wherein the first well and the second well are in common field [i.e., the context data comprises years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)].
Regarding claim 3, the method of claim 1, wherein drilling of the first well commences prior to drilling of the second well [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)].
Regarding claim 4, the method of claim 3, wherein the drilling of the first well overlaps in time at least in part with the drilling of the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 5, the method of claim 1, wherein the plan results and the actual results pertain to a dogleg of the first well [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)].
Regarding claim 6, the method of claim 5, wherein the adjusted second digital drilling plan specifies drilling of a dogleg of the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 7, the method of claim 1, wherein the plan results and the actual results indicates a detrimental deviation [i.e., the context data comprises years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)].
Regarding claim 8, the method of claim 1, wherein the plan results and the actual results indicates a beneficial deviation [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)].
Regarding claim 9, the method of claim 1, wherein the plan results and the actual results correspond to drilling in a particular formation and wherein the adjusting adjusts the second digital drilling plan for drilling in the particular formation [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025) i.e., the context data comprises years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)].
Regarding claim 10, the method of claim 1, wherein using the trained machines learning model comprises inputting specifications for a bottom hole assembly for drilling of the second well [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)].
Regarding claim 11, the method of claim 1, wherein using the trained machine learning model comprises determining a likelihood of sticking of a drillstring used for drilling the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 12, the method of claim 1, wherein using the trained machine learning model comprises receiving a deviation between actual and plan results for drilling of the second well and determining that the deviation is beneficial or detrimental [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)].
Regarding claim 13, the method of claim 12, wherein the adjusting occurs responsive to the deviation being detrimental [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 14, the method of claim 1, wherein the trained machine learning model operates as classifier [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)].
Regarding claim 15, the method of claim 1, wherein the trained machine learning model operates to determine a probability of sticking of a drillstring [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)].
Regarding claim 16, the method of claim 1, wherein the trained machine learning model operates to identify a probable cause of sticking of a drillstring [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025) i.e., the context data comprises years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)].
Regarding claim 17, the method of claim 1, wherein the training comprises using synthetic training data [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)].
Regarding claim 18, the method of claim 17, wherein the synthetic training data comprise model-based results associated with sticking of a drillstring [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 19, a system [i.e., system (see figure 11)] comprising:
a processor [i.e., processor 1102 (see figure 11)]:
memory accessible by the processor [i.e., main memory 1104 is connected to processor 1102 (see figure 11)];
processor-executable instructions stored in the memory and executable to instruct the system to:
responsive to executing a first digital drilling plan for a first well that specifies plan result [i.e., gathering context data regarding offset wells, wherein the context data relate to a drilling operation that is not measured by physical sensors associated with a drilling operation (para 0018)], control field equipment to perform drilling operations for the first well [i.e., an operation performed using the context data that comprise years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)];
acquire sensor data during the performance of the drilling operations for the first well [i.e., sensor data regarding the offset wells (para 0152) and (see figure 12)];
generate actual results using the sensor data [i.e., placing sensor data regarding the offset wells and the context data into a data store, and identifying a correlation between data in the data store and an operational outcome in a drilling operation (para 0152) and (see figure 12)];
train a machine learning model using the plan results and the actual results to generate a trained machine learning model [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)];
during executing of a second digital drilling plan for a second well, adjust the second digital drilling plan using the trained machine learning model to generate an adjusted second digital drilling plan [i.e., gathering various data types into the data store enables the ability to query across the entire data set held in the data store, and the operational outcome is predicted based on the model, wherein the model is created based on the data set (para 0066)], [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)];
responsive to executing the adjusted second digital drilling plan for the second well, control filed equipment to perform drilling operations for the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Regarding claim 20, a non-transitory computer-readable storage medium storing instructions that when executed by a computer [i.e., main memory 1104 (see figure 11)], which includes a processor performs a method, the method comprising:
responsive to executing a first digital drilling plan for a first well that specifies plan result [i.e., gathering context data regarding offset wells, wherein the context data relate to a drilling operation that is not measured by physical sensors associated with a drilling operation (para 0018)], control field equipment to perform drilling operations for the first well [i.e., an operation performed using the context data that comprise years of drilling crew experience, type of drill bit used, drilling fluid type, daily cost of lease equipment, and physical characteristics of an underground formation (para 0018)];
acquire sensor data during the performance of the drilling operations for the first well [i.e., sensor data regarding the offset wells (para 0152) and (see figure 12)];
generate actual results using the sensor data [i.e., placing sensor data regarding the offset wells and the context data into a data store, and identifying a correlation between data in the data store and an operational outcome in a drilling operation (para 0152) and (see figure 12)];
train a machine learning model using the plan results and the actual results to generate a trained machine learning model [i.e., creating a training data set based on the correlation, and training a machine learning algorithm using the training data set (para 0163)];
during executing of a second digital drilling plan for a second well, adjust the second digital drilling plan using the trained machine learning model to generate an adjusted second digital drilling plan [i.e., gathering various data types into the data store enables the ability to query across the entire data set held in the data store, and the operational outcome is predicted based on the model, wherein the model is created based on the data set (para 0066)], [i.e., identifying the correlation between the data in the data store and the operational outcome in the drilling operation, creating the reduced data set by the identification, creating the model based on the reduced data set, and predicting the operation outcome based on the model (0018) and 0152)];
responsive to executing the adjusted second digital drilling plan for the second well, control filed equipment to perform drilling operations for the second well [i.e., the predicted operational outcome comprises rate of penetration of drilling (para 0025)].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Maniar (US 2020/0040719 A1) discloses obtaining, prior to the drilling operation, a target well data set specifying a target well to be drilled; selecting, from a plurality of existing wells, a plurality of analog wells that satisfy a pre-determined similarity criterion with respect to the target well; generating, from a plurality of analog well data sets of the plurality of analog wells, a training data set for the target well, wherein the training data set comprises a rate-of-penetration (ROP) profile for each of the plurality of analog wells; generating, using a machine-learning algorithm and based on the training data set, a drilling model that predicts the ROP profile of the target well; and performing, based on the drilling model, modeling of the drilling operation to generate a predicted ROP profile of the target well.
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/SYED A RONI/Primary Examiner, Art Unit 2432
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