DETAILED ACTION
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 .
This action is in response to the Application filed on 09/25/2023. Claims 1-20 are pending in the case. All claims are examined and rejected accordingly.
Information Disclosure Statement
3. As required by MPEP 609 (c), the Applicants’ submission of the Information Disclosure Statement(s) filed on 02/04/2025 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea, without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claim is directed to a computer implemented method, which is a process and falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1, 19 and 20,
At step 2A, prong 1, Does the claim recite a judicial exception?
Claim 1 further recites the steps of:
processing the time series data as input to a trained machine learning model (This step relies on mathematical analysis using LM model which falls into the “Mathematical Concepts” grouping of abstract ideas.),
to predict a future solids event (This step relies on predicting future events from collected data which involved evaluation, judgment and forecasting which can be performed in the human mind or pen and paper),
The claim recites evaluating operational data using a machine learning model to generate prediction. The prediction is produced through mathematical calculations performed by the trained model on input data, which is categorized as mathematical concepts through machine learning predictions. Accordingly, the claims recite an abstract idea.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application?
Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional elements recited of consist of:
receiving real-time, time series data from equipment at a wellsite that comprises a wellbore in contact with a fluid reservoir (This step is collecting data/information from sensors/equipment and does not integrate the exception into practical application)
…. trained machine learning model …; (generic use of ML ode to calculate prediction does not integrate the abstract idea to practical application)
related to influx of solids into the wellbore from the fluid reservoir; (This step is field of use limitation which limits the abstract idea to oil refinement environment and abstract calculation to field of use does not integrate the abstract idea to practical application (see MPEP 2106.05(h))), and
outputting a time of the future solids event. (This step is displaying or outputting the result of mathematical analysis which is insignificant post activity solution)
… at least one processor and memory; the at least one processor …” (claim19), (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
… computer readable media comprising processor executable instructions …, (claim 20) (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
The additional elements are recited at a high level of generality and do not amount to significantly more than the abstract idea (MPEP 2106.05(f)). The claim use a computer to perform a math and does not improve the function of the computer or other technology. Accordingly the claim does not integrate the abstract idea into practical application.
Thus the claim is directed towards the abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception?
No, as shown above with respect to integration of the abstract idea into a practical application, the additional element of:
receiving real-time, time series data from equipment at a wellsite that comprises a wellbore in contact with a fluid reservoir (This step is collecting data/information from sensors/equipment and does not integrate the exception into practical application)
related to influx of solids into the wellbore from the fluid reservoir; (This step is field of use limitation which limits the abstract idea to oil refinement environment and abstract calculation to field of use does not integrate the abstract idea to practical application (see MPEP 2106.05(h))), and
outputting a time of the future solids event. (This step is displaying or outputting the result of mathematical analysis which is insignificant post activity solution)
… at least one processor and memory; the at least one processor …” (claim19), (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
… computer readable media comprising processor executable instructions …, (claim 20) (Generic computer components on which to implement the math abstract idea (see MPEP 2106.05(f));
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application or add “significantly more.” Thus, the claims are not patent eligible. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity. All of these additional elements as generically claimed are thus considered well-understood, routine, and conventional. Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea.
Thus, these independent claims are not patent eligible.
The dependent claims respectively recite a judicial exception in limitations of: “wherein the solids event comprises a sand event related to influx of sand into the wellbore from the fluid reservoir.”(claims 2); “wherein the trained machine learning model comprises a 1 D convolution neural network.”(claims 3); “wherein the trained machine learning model comprises an encoder and a decoder.”(claims 4); “wherein the encoder and the decoder are components of an autoencoder.”(claims 5); “comparing output of the decoder to the input to predict the future solids event.”(claims 6); “computing a root mean square error based on the comparing and comparing the root mean square error to a threshold to predict the future solids event.”(claims 7), “training the machine learning model.”, (claims 8); “ wherein the training comprises utilizing controversial optimization that forces generation of output toward non-solids events and away from solids events.”, (claims 9); “wherein the training comprises utilizing training data from one or more wells for non-solids events.”, (claims 10); “issuing a control instruction to at least one piece of equipment at the wellsite.”, (claims 11); “wherein the at least one piece of equipment comprises one or more of a valve, a pump and a gas supply to at least one gas lift valve.” .”, (claims 12); “wherein the processing comprises utilizing a geo-mechanical model that models stability of reservoir rock of the fluid reservoir.”, (claims 13); “ wherein the processing comprises utilizing a mechanical earth model that models stresses based at least in part on reservoir rock properties.”, (claims 14); “updating the mechanical earth model using at least a portion of the real-time, time series data.”, (claims 15); “wherein the outputting outputs a log of critical drawdown pressure operational parameters for the well.”, (claims 16); “ wherein at least one of the critical drawdown operational parameters depends on the time of the future solids event.”, (claims 17); “wherein the outputting outputs a probability for the future solids event.”, (claims 18).
These additional limitations (in claims 2-18) also constitute concepts performed Mathematical concept or mathematical operation groupings of abstract ideas.
This judicial exception is not integrated into a practical application. Additional elements “computer readable medium comprising: computer program code (in claims 2-18), all amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of non-transitory computer readable medium comprising: computer program code are again insignificant extra-solution activity steps that cannot provide an inventive concept. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all of the dependent claims are also not patent eligible.
Examiner Comments
5. 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.
Claim Rejections - 35 USC § 103
6. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Al-Nasser (Pub. No. US 20210224682 A1, Pub. Date 2021-07-22) in view of THIRUVENKATANATHAN (Pub. No. US 20220349298 A1, 2022-11-03, hereinafter THIRUVEN.)
Regarding independent Claim 1,
Al-Nasser teaches a method comprising:
receiving real-time, time series data from equipment at a wellsite that comprises a wellbore in contact with a fluid reservoir (see Al-Nasser: Fig.2, [0037], “receives 204 information obtained from a hydrocarbon reservoir. The hydrocarbon reservoir is associated with multiple hydrocarbon wells. The information includes porosity logs 100, petrophysical data 104, rock typing data 122, pressure transient test results 124, vertical production logs 102, reservoir pressure logs 108, reservoir saturation logs 110, production performance 128, and injection performance 130.”).
processing the time series data as input to a trained machine learning mode, […] (see Al-Nasser: Fig.2, [0039], “The computer system trains 212 a machine learning algorithm to provide variations in reservoir saturation 136 of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained using the reservoir saturation logs 110, the vertical production logs 102, the production performance 128, the injection performance 130, the reservoir pressure logs 108, the petrophysical data 104, and the rock typing data 122.”).
As shown above, Al-Nasser teaches training machine learning models using porosity logs, petrophysical data, rock typing data, pressure transient test results, vertical production logs, reservoir pressure logs, reservoir saturation logs, production performance, and injection performance. The trained machine learning model predicts or forecasts changes to the reservoir conditions to support wellbore management, production planning and wellbore repair decision making process.
Al-Nasser does not teach the method wherein
predict a future solids event related to influx of solids into the wellbore from the fluid reservoir;
outputting a time of the future solids event.
However, THIRUVEN teach the method wherein:
predict a future solids event related to influx of solids into the wellbore from the fluid reservoir (see THIRUVEN: Fig.6, [0137], “utilizing operating envelopes (and underlying sand prediction model), predictions can be made as to whether sand ingress may occur at the one or more production zones under certain operating conditions (e.g., drawdown pressure, production rate, etc.). In some circumstances, one or more of the operating conditions for a well may be determined by other factors so that operation within the operating envelope is not possible (or at least no feasible) in certain scenarios. In some embodiments, the operating envelope generated by a sand prediction model may or shrink generally narrow over time as a result of a pressure reduction in the production zone and/or the overall reservoir.”)
outputting a time of the future solids event (see THIRUVEN: Fig.6, [0140], “The ability to use the model to predict future sanding based on certain operating parameters can allow for a wellbore to be designed to improve the overall amount of fluid produced from the wellbore in an economical fashion.”)
Because both Al-Nasser and THIRUVEN are in the same/similar field of endeavor of predictive analysis of oil production, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify the system of Al-Nasser to include the sand- ingress prediction technique to predict a future solids event related to influx of solids into the wellbore from the fluid reservoir and outputting a time of the future solids event as taught by THIRUVEN. One would have been motivated to make such a combination in order to improve well planning, production optimization by preventing sand from advancing into the wellbore (e.g., with a screen or other suitable device or completion method), or to minimize (or prevent entirely) the production of sand from the subterranean formation. (see THIRUVEN: [003])
Regarding Claim 2,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. THIRUVEN further teaches the method wherein:
the solids event comprises a sand event related to influx of sand into the wellbore from the fluid reservoir (see THIRUVEN: Fig.6, [0131], “includes receiving an indication of sand ingress at one or more production zones within a first wellbore at 402. In some embodiment, the indication of the sand ingress may be received or detected utilizing a sand monitoring system and a sand detection model in the manner previously described above for method 200.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify the system of Al-Nasser to include a sand event prediction technique to predict a future solids event related to influx of solids into the wellbore as taught by THIRUVEN. One would have been motivated to make such a combination in order to improve well planning, production optimization by preventing sand from advancing into the wellbore (e.g., with a screen or other suitable device or completion method), or to minimize (or prevent entirely) the production of sand from the subterranean formation. (see THIRUVEN: [003])
Regarding Claim 3,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
the trained machine learning model comprises a 1 D convolution neural network (see Al-Nasser: Fig.1, [0020], “the machine learning algorithm can use unsupervised learning (such as K-Means Clustering) or extract features of interest from the information obtained from the hydrocarbon reservoir. In some implementations, artificial neural networks, gradient boosting, or support vectors are used.”).
Regarding Claim 4,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
the trained machine learning model comprises an encoder and a decoder (see Al-Nasser: Fig.1, [0040], “the computer system trains the machine learning algorithm using any one or more of the following methods: independent component analysis, Isomap, Kernel PCA, latent semantic analysis, partial least squares, principal component analysis, multifactor dimensionality reduction, nonlinear dimensionality reduction, multilinear principal component analysis, multilinear subspace learning, semidefinite embedding, Autoencoder, and deep feature synthesis.”).
Regarding Claim 5,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 4. Al-Nasser further teaches the method wherein:
the encoder and the decoder are components of an autoencoder (see Al-Nasser: Fig.1, [0040], “the computer system trains the machine learning algorithm using any one or more of the following methods: independent component analysis, Isomap, Kernel PCA, latent semantic analysis, partial least squares, principal component analysis, multifactor dimensionality reduction, nonlinear dimensionality reduction, multilinear principal component analysis, multilinear subspace learning, semidefinite embedding, Autoencoder, and deep feature synthesis.”).
Regarding Claim 6,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 4. Al-Nasser further teaches the method wherein:
comparing output of the decoder to the input to predict the future solids event (see Al-Nasser: Fig.1, [0033], “the computer system determines locations of infill drillings and a logging frequency 118 based on the virtual 3D model 112. Based on a magnitude of a difference between the predicted dynamic reservoir saturation 136 and observed data, the logging frequency 118 and an annual surveillance program for the oilfield is improved compared to traditional methods. The computer system determines locations of infill drillings by the generated graphical maps of the predicted remaining saturation and pressure at a future time to target areas characterized by increased pressure and a slow change in saturation relative to other areas.”)
Regarding Claim 7,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 6. Al-Nasser further teaches the method wherein:
computing a root mean square error based on the comparing and comparing the root mean square error to a threshold to predict the future solids event (see Al-Nasser: Fig.1, [0029], “production performance 128, the injection performance 130, the reservoir pressure logs 108, and the rock typing data 122. In some implementations, the computer system normalizes the reservoir saturation logs 120 in accordance with time to provide the variations in reservoir saturation 136. The prediction capabilities of the virtual 3D model 112, achieved by artificial intelligence methods, are utilized to predict the amount of saturation in hydrocarbon wells that were not logged at the time of interest for mapping the oil saturation.”)
Regarding Claim 8,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
training the machine learning model (see Al-Nasser: Fig.2, [0039], “he computer system trains 212 a machine learning algorithm to provide variations in reservoir saturation 136 of the hydrocarbon reservoir in accordance with time.”)
Regarding Claim 9,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 8. THIRUVEN further teaches the method wherein:
utilizing controversial optimization that forces generation of output toward non-solids events and away from solids events (see THIRUVEN: Fig.6, [0139], “the completion plan provides a design or configuration for a wellbore that is not yet drilled or has been drilled but has not yet been completed. The plan can define the physical configuration of the equipment placed within the wellbore, the type of equipment or completion to be used, and/or the equipment locations. The completion plan can also comprise an operating plan paired with the completion plan to enable the drawdown of the new well to be improved or maximized within a certain operating time frame.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention to modify the system of Al-Nasser to apply controversial optimization that forces generation of output toward non-solids events and away from solids events as taught by THIRUVEN. One would have been motivated to make such a combination in order to improve well planning, production optimization by preventing sand from advancing into the wellbore (e.g., with a screen or other suitable device or completion method), or to minimize (or prevent entirely) the production of sand from the subterranean formation. (see THIRUVEN: [003])
Regarding Claim 10,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 8. Al-Nasser further teaches the method wherein:
the training comprises utilizing training data from one or more wells for non-solids events (see Al-Nasser: Fig.1, [0039], “The computer system trains 212 a machine learning algorithm to provide variations in reservoir saturation 136 of the hydrocarbon reservoir in accordance with time. The machine learning algorithm is trained using the reservoir saturation logs 110, the vertical production logs 102, the production performance 128, the injection performance 130, the reservoir pressure logs 108, the petrophysical data 104, and the rock typing data 122.”).
Regarding Claim 11,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
issuing a control instruction to at least one piece of equipment at the wellsite (see Al-Nasser: Fig.1, [0033], “The computer system determines locations of infill drillings by the generated graphical maps of the predicted remaining saturation and pressure at a future time to target areas characterized by increased pressure and a slow change in saturation relative to other areas.”).
Regarding Claim 12,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 11. Al-Nasser further teaches the method wherein:
at least one piece of equipment comprises one or more of a valve, a pump and a gas supply to at least one gas lift valve (see Al-Nasser: Fig.3, [0042], “illustrates an example vertical production profile 140. The vertical production profile 140 is obtained using the vertical production logs 102 obtained from PLTs. The vertical production logs 102 measure the flow rate in accordance with the depth indicating zones within the reservoir in addition to identifying water and oil entries. The vertical production profile 140 is sometimes referred to as a formation signature through which the ability of the different zones to flow oil, water, or both is defined relative to each other.”).
Regarding Claim 13,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
the processing comprises utilizing a geo mechanical model that model’s stability of reservoir rock of the fluid reservoir (see Al-Nasser: Fig.5, [0056], “lustrates a process for generation of a reservoir digital twin (for example, the virtual 3D model 112 illustrated in FIG. 1). In some implementations the process of FIG. 5 is performed by the computer system illustrated and described in more detail with reference to FIG. 4.”).
Regarding Claim 14,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 13. Al-Nasser further teaches the method wherein:
the processing comprises utilizing a mechanical earth model that models’ stresses based at least in part on reservoir rock properties (see Al-Nasser: Fig.5, [0057], “The computer system generates 504 the virtual 3D model 112 of a hydrocarbon reservoir using a machine learning algorithm. The machine learning algorithm is trained using information obtained from multiple hydrocarbon wells. The virtual 3D model 112 includes a reservoir pressure model 132 of the hydrocarbon reservoir indicating variations in reservoir pressure 114 in accordance with time. The virtual 3D model 112 further includes a fluid saturation model 134 of the hydrocarbon reservoir indicating variations in reservoir saturation 136 in accordance with time.”).
Regarding Claim 15,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 13. Al-Nasser further teaches the method wherein:
updating the mechanical earth model using at least a portion of the real-time, time series data (see Al-Nasser: Fig.1, [0038], “The computer system normalizes 208 the reservoir saturation logs 110 in accordance with time. Artificial intelligence can be used to predict variations in the reservoir saturation 136 based on normalizing the data in the reservoir saturation logs 110 with respect to time for mapping the physical parameters of the hydrocarbon reservoir to the virtual 3D model 112. The virtual 3D model 112 is illustrated and described in more detail with reference to FIG. 1.”).
Regarding Claim 16,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
the outputting outputs a log of critical drawdown pressure operational parameters for the well (see Al-Nasser: Fig.1, [0034], “he computer system determines a logging frequency 118 by uncertainty bound analysis to determine a gap between the measured and predicted data. A greater bound suggests that the logging frequency 118 should be increased for the targeted hydrocarbon well or areas.”).
Regarding Claim 17,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 16. Al-Nasser further teaches the method wherein:
at least one of the critical drawdown operational parameters depends on the time of the future solids event (see Al-Nasser: Fig.1, [0033], “. The computer system determines locations of infill drillings by the generated graphical maps of the predicted remaining saturation and pressure at a future time to target areas characterized by increased pressure and a slow change in saturation relative to other areas.”).
Regarding Claim 18,
As shown above, Al-Nasser and THIRUVEN teaches all the limitation of claim 1. Al-Nasser further teaches the method wherein:
the outputting outputs a probability for the future solids event (see Al-Nasser: Fig.3, [0042], “vertical production profile 140. The vertical production profile 140 is obtained using the vertical production logs 102 obtained from PLTs. The vertical production logs 102 measure the flow rate in accordance with the depth indicating zones within the reservoir in addition to identifying water and oil entries. The vertical production profile 140 is sometimes referred to as a formation signature through which the ability of the different zones to flow oil, water, or both is defined relative to each other.”).
Regarding independent Claim 19,
Claim 19 is directed to a system claim and has similar/same claim limitation as claim 1 and is rejected under the same rationale.
Regarding independent claim 20,
Claim 20 is directed to a computer-readable storage media claim and has similar/same claim limitation as claim 1 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 2021/0040837 A1
Al-Rabeh, Majed N.
Title: Automated sand grain bridge stability simulator
Description: The present disclosure relates to oil, gas and water field production and, in particular, to simulating stand-alone sand screen performance in laboratory and downhole environments.
US 12523142 B2
Lafond; Aurore
Title: Machine Learning Approaches To Detecting Pressure Anomalies
Description: The method includes training the machine learning system based on the set of training data and the set of supplemental data to generate a trained machine learning system, receiving real-time operational data, inputting the real-time operational data into the trained machine learning system, predicting a real-time equipment pressure measurement based on the inputting the real-time operational data into the trained machine learning system, and executing a computer-based instruction based on the predicting the real-time equipment pressure measurement
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZELALEM W SHALU whose telephone number is (571)272-3003. The examiner can normally be reached M- F 0800am- 0500pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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