Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show fail to show text labels for the numerals in figures 1-3 as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 2 objected to because of the following informalities: “storage site assessment”. The claims and specs use the terminology of “storage risk assessment” and the claim is being interpreted as such. The applicant should change “site” to “risk”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under the 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, an abstract idea, that does not amount to significantly more than the judicial exception.
The following is an analysis of independent claim 1 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claim 1 is directed to a method.
Step 2A Prong 1, Judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mental processes or generic computer functions, as the claims cover performance of the limitations of the human mind or a generic computer processor, given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 1 recites the following limitations:
A method for predicting a CO2 storage risk assessment, comprising the steps of:
determining a set of well integrity rules;
determining a classification process based on the set of well integrity rules;
providing data for a first well located in a subsurface formation;
extracting data relevant to the set of well integrity rules from the data for the first well;providing the extracted data to the classification process;
computing a prediction for a first subsurface CO2 storage risk assessment for the first well.
The limitations determining a set of well integrity rules and determining a classification process based on the set of well integrity rules (one can make an observation, and then use critical thinking to make a determined decision, using pen and paper if necessary), providing data for a first well located in a subsurface formation and extracting data relevant to the set of well integrity rules from the data for the first well, and providing the extracted data to the classification process (one can observe a system, structure, or process and visually/mentally collect data from said system, structure, or process and then further use critical thinking with a pen and paper to determine relevancy. One could then further relay their observations and critical thinking analysis to a different system, structure, or process via pen and paper) and computing a prediction for a first subsurface CO2 storage risk assessment for the first well (one can use critical thinking to perform extrapolated, educated guesses based on known data received from observations).
Step 2A Prong 2, Integration into a Practical Application:
Claim 1 recites additional limitations of CO2 storage that would fall under general field of use, specifically generally linking (see MPEP § 2106.05(h)).
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of Claim 1 do not amount to significantly more than the judicial exceptions for the same reasons above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional limitations identified as general field of use above are carried over and also do not provide significantly more than the abstract idea. See MPEP § 2106.05(h).
Therefore, considering the claim limitations in combination as a whole make Claim 1 ineligible under 35 U.S.C. 101.
Claim 2 recites equivalent limitations to claim 1, additionally applied to analysis of a second well. Therefore, Claim 2 is also rejected under the same rationale.
Claim 8 only recite limitations that fall under the judicial exception of “mental processes” and then insignificant extra-solution activity as per MPEP 2106.05(g), selecting a particular data source or type of data to be manipulated. Therefore, it remains ineligible for the same reasons as set forth for Claim 1.
Claims 4-7 are dependent on Claim 1 and only recite limitations directed towards an abstract idea that do not cure the issues of Claim 1. Therefore, they remain ineligible for the same reasons as set forth for Claim 1.
Claims 10-11 are dependent on Claim 1 and only recite limitations directed towards an abstract idea that do not cure the issues of Claim 1. Therefore, they remain ineligible for the same reasons as set forth for Claim 1.
Claims 3 and 9 only recite limitations that fall under the judicial exception of “mental processes” and then “generally linking” generally linking to a technical field, as per MPEP 2106.05(h). Therefore, they remain ineligible for the same reasons as set forth for Claim 2.
Claim Rejections - 35 USC § 102
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.
Claims 1-4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li (Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network).
Regarding Claim 1, Li teaches:
determining a set of well integrity rules (Abstract: The well integrity conditions were assessed by analyzing the well attribute data (well type, well age, CO2 exposed period, well construction details and materials), well operation histories and regulatory changes.)
determining a classification process based on the set of well integrity rules (Page 4: In order to develop the decision method to identify which well has more likely leakage potential than others, SCD,the base of usable quality water depth(BUQW), cement top of surface casing and production casing should be considered. The CO2 exposed well could be classified into three groups by two criteria as shown in Table 2. Well construct default indicator was also assigned to each well based on the structure of the wells. More description on well leakage indicator were detailed expressed the author’s previous paper. Table 2. IWS Classification Criteria.... Page 5: Fig. 4 describes the schematic of a designed neural network for the CO2 injection well leakage risk assessment.)
providing data for a first well located in a subsurface formation; (Page 3: Two CO2 sequestration fields were identified in the Texas Gulf Coast region suitable for this study. They are the Hasting West oil field and Oyster Bayou oil field in southern Texas. Location of these two fields are shown in Fig. 1. Both of the two fields are CO2 EOR fields but with different geological parameters as shown in Table 1. A comprehensive database which included values of 67 parameters describing well design and operation conditions for 510 wells was created via online data mining and onsite data mining of the West Hastings oil field (Fig. 2) and Oyster Bayou oil field (Fig. 3). This data set has a nature of non hierarchical structure with high dimension of variables.)
extracting data relevant to the set of well integrity rules from the data for the first well; (Page 3: from 5125 to 6125 feet. A major, northwesterly trending and northeasterly dipping, normal fault divides the field into two segments. Both reservoirs are productive in the upthrown, southwestern segment; whereas only the upper Frio reservoir is productive in the downthrown, northeastern segment. The continuity of these reservoirs is interrupted by many, smaller faults. The West Hastings oil field started CO2 injection process in December 2010. CO2 has been injected into the Frio sandstone formation at 5700 ft with average pay zone thickness 100 ft. The average porosity of reservoir is about 0.3, and the average horizontal permeability is about 900 md. The API gravity of the oil is 31°. Well data for 428 CO2 exposed wells (56 CO2 injection wells and 372 abandoned wells) in the field were collected)
providing the extracted data to the classification process; (Page 8: So, twenty two wells were used to train and test the neural network. In order to train and validate the neural network, the data was divided into two groups, named training data and testing data, respectively. The training data was input into the network during the training process. The testing data was used to test and validate the performance of the trained network. Twelve wells, including nine potential-safe wells and three potential-fail wells, were randomly selected to train the neural network. The rest of ten wells, including seven potential safe wells and three potential failure wells, were used to validate the neural network.)
computing a prediction for a first subsurface CO2 storage risk assessment for the first well. (Page 5: The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.)
Regarding Claim 2, Li further teaches:
providing data for a second well located in the subsurface formation (Page 3: Two CO2 sequestration fields were identified in the Texas Gulf Coast region suitable for this study. They are the Hasting West oil field and Oyster Bayou oil field in southern Texas. Location of these two fields are shown in Fig. 1. Both of the two fields are CO2 EOR fields but with different geological parameters as shown in Table 1. A comprehensive database which included values of 67 parameters describing well design and operation conditions for 510 wells was created via online data mining and onsite data mining of the West Hastings oil field (Fig. 2) and Oyster Bayou oil field (Fig. 3). This data set has a nature of non hierarchical structure with high dimension of variables.)
extracting data relevant to the set of well integrity rules from the data for the second well; (Page 3: Two CO2 sequestration fields were identified in the Texas Gulf Coast region suitable for this study. They are the Hasting West oil field and Oyster Bayou oil field in southern Texas. Location of these two fields are shown in Fig. 1. Both of the two fields are CO2 EOR fields but with different geological parameters as shown in Table 1. A comprehensive database which included values of 67 parameters describing well design and operation conditions for 510 wells was created via online data mining and onsite data mining of the West Hastings oil field (Fig. 2) and Oyster Bayou oil field (Fig. 3). This data set has a nature of non hierarchical structure with high dimension of variables.)
providing the extracted data to the classification process; (Page 8: So, twenty two wells were used to train and test the neural network. In order to train and validate the neural network, the data was divided into two groups, named training data and testing data, respectively. The training data was input into the network during the training process. The testing data was used to test and validate the performance of the trained network. Twelve wells, including nine potential-safe wells and three potential-fail wells, were randomly selected to train the neural network. The rest of ten wells, including seven potential safe wells and three potential failure wells, were used to validate the neural network.)
computing a prediction for a second subsurface CO2 storage risk assessment for the second well; (Page 5: The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.)
computing a prediction of a formation CO2 storage risk assessment based on the first subsurface CO2 storage risk assessment and the second subsurface CO2 storage site assessment. (Page 7: The proposed neural network algorithm was developed and programmed in VBA code. One hundred CO2 exposed wells of the West Hastings field and Oyster Bayou field were randomly selected to optimized the neural network. The outputs of the neural network are LPIs of the CO2 exposed wells, as discussed in the previous section. The performance of the neural network can be adjusted by change of the number of hidden neurons. Sensitive analysis was performed to optimize the number of neurons in the hidden layer and RMSEmax of the neural network model. RMSE was used to evaluate the performance of the neural network, which was calculated by Eq. (7) and controlled by Eq. (6)…. Page 8: The proposed neural network was optimized and proved to be re liable and reasonable based on the sevsitivity analysis in the previous section. A hundred CO2 exposed wells of both west hastings field and oyster bayou field was randomly selected to train and validate the neural networks. Sixteen of these CO2 exposed wells were identified as potential- safe wells while six of these CO2 exposed wells were identified as potential-failure wells. So, twenty two wells were used to train and test the neural network. In order to train and validate the neural network, the data was divided into two groups, named training data and testing data, respectively. The training data was input into the network during the training process. The testing data was used to test and validate the performance of the trained network. Twelve wells, including nine potential-safe wells and three potential-fail wells, were randomly selected to train the neural network. The rest of ten wells, including seven potential safe wells and three potential failure wells, were used to validate the neural network.)
Regarding Claim 3, Li further teaches:
modifying the first subsurface CO2 storage risk assessment in view of the second subsurface CO2 risk assessment. (Page 7: The proposed neural network algorithm was developed and programmed in VBA code. One hundred CO2 exposed wells of the West Hastings field and Oyster Bayou field were randomly selected to optimized the neural network. The outputs of the neural network are LPIs of the CO2 exposed wells, as discussed in the previous section. The performance of the neural network can be adjusted by change of the number of hidden neurons. Sensitive analysis was performed to optimize the number of neurons in the hidden layer and RMSEmax of the neural network model. RMSE was used to evaluate the performance of the neural network, which was calculated by Eq. (7) and controlled by Eq. (6)…. Page 8: The proposed neural network was optimized and proved to be re liable and reasonable based on the sevsitivity analysis in the previous section. A hundred CO2 exposed wells of both west hastings field and oyster bayou field was randomly selected to train and validate the neural networks. Sixteen of these CO2 exposed wells were identified as potential- safe wells while six of these CO2 exposed wells were identified as potential-failure wells. So, twenty two wells were used to train and test the neural network. In order to train and validate the neural network, the data was divided into two groups, named training data and testing data, respectively. The training data was input into the network during the training process. The testing data was used to test and validate the performance of the trained network. Twelve wells, including nine potential-safe wells and three potential-fail wells, were randomly selected to train the neural network. The rest of ten wells, including seven potential safe wells and three potential failure wells, were used to validate the neural network.)
Regarding Claim 4, Li teaches:
the set of well integrity rules comprises criteria selected from the group consisting of presence of a cap rock seal, well casing integrity, open or closed perforations in the wells, proximity to groundwater zone, isolation of groundwater zones using plugs or otherwise, fluid communication with a permeable zone, industry standards, industry guidelines, governmental regulations, and combinations thereof. (Page 3: Mechanisms can be related to wellbore construction or plugging problems such as leakage path around the plug cement, or it can be related to the cement failure due to insufficient inner casing pressure and excessive outer casing pressure due to CO2 injection.)
Claim Rejections - 35 USC § 103
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.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Pawar (Recent advances in risk assessment and risk management of geologic CO2 storage). Li teaches all the limitations of Claim 1.
Regarding Claim 5,
Li does not teach:
the first subsurface CO2 storage risk assessment is a vertical risk assessment
However, Pawar does teach:
the first subsurface CO2 storage risk assessment is a vertical risk assessment (Page 4-5: Risk assessment approaches for wellbore integrity in GCS (e.g. Viswanathan et al., 2008, Stauffer et al., 2009, Oldenburg et al., 2009, Bai et al., 2015) have used permeability as a key quantitative measure of the potential consequences of well leakage, where permeability around the well is used to quantify the amount of CO2 or brine that could migrate along damaged wells… Page 5, 2.2 Caprock Integrity: Risk assessment of caprock integrity is similar to wellbore integrity in the sense that the inherent properties of good-quality caprock (typically shale or evaporites; e.g. Grunau, 1987) are more than adequate to isolate CO2 in the subsurface. Risk assessment then involves determining whether such caprock properties are present across the project area and whether the planned injection operation can be conducted without damaging the caprock.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the risk assessment for measuring damage surrounding wells and the caprock of Pawar with the risk assessment of Li “the schematic of a designed neural network for the CO2 injection well leakage risk assessment.” in order to assess CO2 storage conditions as described by Li “The well integrity conditions were assessed by analyzing the well attribute data”.
Regarding Claim 6,
Li does not teach:
the second subsurface CO2 storage risk assessment is a vertical risk assessment
However, Pawar does teach:
the second subsurface CO2 storage risk assessment is a vertical risk assessment (Page 4-5: Risk assessment approaches for wellbore integrity in GCS (e.g. Viswanathan et al., 2008, Stauffer et al., 2009, Oldenburg et al., 2009, Bai et al., 2015) have used permeability as a key quantitative measure of the potential consequences of well leakage, where permeability around the well is used to quantify the amount of CO2 or brine that could migrate along damaged wells… Page 5, 2.2 Caprock Integrity: Risk assessment of caprock integrity is similar to wellbore integrity in the sense that the inherent properties of good-quality caprock (typically shale or evaporites; e.g. Grunau, 1987) are more than adequate to isolate CO2 in the subsurface. Risk assessment then involves determining whether such caprock properties are present across the project area and whether the planned injection operation can be conducted without damaging the caprock.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the risk assessment for measuring damage surrounding wells and the caprock of Pawar with the risk assessment of Li “the schematic of a designed neural network for the CO2 injection well leakage risk assessment.” in order to assess CO2 storage conditions as described by Li “The well integrity conditions were assessed by analyzing the well attribute data”.
Regarding Claim 7, Li does not teach:
the formation CO2 storage risk assessment is an areal risk assessment.
However, Pawar does teach:
the formation CO2 storage risk assessment is an areal risk assessment. (Page 4-5: Risk assessment approaches for wellbore integrity in GCS (e.g. Viswanathan et al., 2008, Stauffer et al., 2009, Oldenburg et al., 2009, Bai et al., 2015) have used permeability as a key quantitative measure of the potential consequences of well leakage, where permeability around the well is used to quantify the amount of CO2 or brine that could migrate along damaged wells… Page 5, 2.2 Caprock Integrity: Risk assessment of caprock integrity is similar to wellbore integrity in the sense that the inherent properties of good-quality caprock (typically shale or evaporites; e.g. Grunau, 1987) are more than adequate to isolate CO2 in the subsurface. Risk assessment then involves determining whether such caprock properties are present across the project area and whether the planned injection operation can be conducted without damaging the caprock.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the risk assessment for measuring damage surrounding wells and the caprock of Pawar with the risk assessment of Li “the schematic of a designed neural network for the CO2 injection well leakage risk assessment.” in order to assess CO2 storage conditions as described by Li “The well integrity conditions were assessed by analyzing the well attribute data”.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Romdhane (Screening, Monitoring, and Remediation of Legacy Wells to Improve Reservoir Integrity for Large-Scale CO2 Storage—An Example From the Smeaheia Structure in the Northern North Sea), in view of Ingelson (LONG-TERM LIABILITY FOR CARBON CAPTURE AND STORAGE IN DEPLETED NORTH AMERICAN OIL AND GAS RESERVOIRS - A COMPARATIVE ANALYSIS), in further view of Stork (Assessing the Effect of Velocity Model Accuracy on Microseismic Interpretation at the In Salah Carbon Capture and Storage Site). Li teaches all the limitations of Claim 1.
Regarding Claim 8:
Li does not teach:
providing a recommendation for repairs to the first well,
However, Romdhane teaches:
providing a recommendation for repairs to the first well (Page 10, The well status given by the Norwegian Petroleum Directorate is P&A, meaning “the well is plugged and abandoned, and cannot be reentered for further use. The wellhead is removed or else made unavailable for further well operations.” The REX-CO2 tool (Pawar and van der Valk, 2020) requires test intervals in the last 12 months; thus, all questions regarding recent inspections and re-using of well equipment are answered with “No.” In total, 47 questions are answered and evaluated (Supplementary Table S1), resulting in the recommendation “severe remediation required” for all subcategories (Figure 5A)).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the well remediation of Romdhane with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2
leakage risk evaluation and prediction.”
Li also does not teach:
abandoning the well
However, Ingelson teaches:
abandoning the well (Page 19: The court then concluded that provincial environmental protection legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination was an act of general application for safe operating practices and protection of the public.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the well abandonment of Ingelson “legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination” with the combination of Li and Romdhane, specifically in the combination of the well remediation tactic of Romdhane “detection of well casing corrosion and finally propose a method to remediate damaged cement” with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.”
Li also does not teach:
injecting CO2 at a specified depth, and combinations thereof
However, Stork does teach:
injecting CO2 at a specified depth, and combinations thereof (Page 1, Abstract: The events occur in two main clusters with estimated locations of well recorded events within one of these clusters to be within 1km horizontal distance from the geophone and between 2.1km and 2.7km deep, at least 200m below the injection depth and CO2 storage interval.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the CO2 injection of Stork “at least 200m below the injection depth and CO2 storage interval” with the well abandonment of Ingelson “legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination”, with the combination of Li, Romdhane, and Ingelson, specifically to the well remediation tactic of Romdhane “detection of well casing corrosion and finally propose a method to remediate damaged cement”, further with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.”
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Romdhane, in view of Ingelson, in further view of Stork. Li teaches all the limitations of Claim 1.
Li does not teach:
providing a recommendation for repairs to the first well,
However, Romdhane teaches:
providing a recommendation for repairs to the first well (Page 10, The well status given by the Norwegian Petroleum Directorate is P&A, meaning “the well is plugged and abandoned, and cannot be reentered for further use. The wellhead is removed or else made unavailable for further well operations.” The REX-CO2 tool (Pawar and van der Valk, 2020) requires test intervals in the last 12 months; thus, all questions regarding recent inspections and re-using of well equipment are answered with “No.” In total, 47 questions are answered and evaluated (Supplementary Table S1), resulting in the recommendation “severe remediation required” for all subcategories (Figure 5A)).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the well remediation of Romdhane with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2
leakage risk evaluation and prediction.”
Li also does not teach:
abandoning the well
However, Ingelson teaches:
abandoning the well (Page 19: The court then concluded that provincial environmental protection legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination was an act of general application for safe operating practices and protection of the public.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the well abandonment of Ingelson “legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination” with the combination of Li and Romdhane, specifically in the combination of the well remediation tactic of Romdhane “detection of well casing corrosion and finally propose a method to remediate damaged cement” with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.”
Li also does not teach:
injecting CO2 at a specified depth, and combinations thereof
However, Stork does teach:
injecting CO2 at a specified depth, and combinations thereof (Page 1, Abstract: The events occur in two main clusters with estimated locations of well recorded events within one of these clusters to be within 1km horizontal distance from the geophone and between 2.1km and 2.7km deep, at least 200m below the injection depth and CO2 storage interval.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the CO2 injection of Stork “at least 200m below the injection depth and CO2 storage interval” with the well abandonment of Ingelson “legislation that provided for the plugging and abandonment of oil and gas wells to prevent groundwater contamination”, with the combination of Li, Romdhane, and Ingelson, specifically to the well remediation tactic of Romdhane “detection of well casing corrosion and finally propose a method to remediate damaged cement”, further with the well integrity assessment of Li “well integrity conditions were assessed by analyzing the well attribute data” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction.”
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Mehyadin (CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW). Li teaches all the limitations of Claim 1.
Regarding Claim 10,
Li does not teach:
the classification process is selected from a supervised classification process,
an unsupervised classification process,
and a semi-supervised classification process
However, Mehyadin does teach:
the classification process is selected from a supervised classification process (Page 1: ML is generally grouped into three types of training: supervised, unsupervised and semi-supervised training [3]. The Input data in classification algorithms are used to calculate the probability that subsequent information would fall into one of the designated groups),
an unsupervised classification process, (Page 1: ML is generally grouped into three types of training: supervised, unsupervised and semi-supervised training [3]. The Input data in classification algorithms are used to calculate the probability that subsequent information would fall into one of the designated groups),
and a semi-supervised classification process (Page 1: ML is generally grouped into three types of training: supervised, unsupervised and semi-supervised training [3]. The Input data in classification algorithms are used to calculate the probability that subsequent information would fall into one of the designated groups).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the supervised, unsupervised and semi-supervised classification of Mehyadin “three types of training: supervised, unsupervised and semi-supervised training[3].The Input data in classification algorithms…” with the classification process of Li “The CO2 exposed well could be classified into three groups by two criteria… designed neural network for the CO2 injection well leakage risk assessment.” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction”.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Mohaghegh (Virtual Magnetic Imaging Logs: Generation of Synthetic MRI Logs from Conventional Well Logs). Li teaches all the limitations of Claim 1.
Regarding Claim 11,
Li teaches:
the classification process is trained with data selected from the group consisting of real well data (Page 8: Twelve wells, including nine potential-safe wells and three potential-fail wells, were randomly selected to train the neural network. The rest of ten wells, including seven potential safe wells and three potential failure wells, were used to validate the neural network.)
Li does not teach:
synthetically generated well data,
augmented well data, and combinations thereof.
However, Mohaghegh does teach:
synthetically generated well data (Page 1, Abstract: MRI logs are well logs that use nuclear magnetic resonance to accurately measure free fluid, irreducible water (MBVI), and effective porosity (MPHI). Permeability is then calculated using a mathematical function that incorporates these measured properties. This paper describes the methodology developed to generate synthetic Magnetic Resonance Imaging logs using data obtained by conventional well logs such as SP, Gamma Ray, Caliper, and Resistivity. The synthetically generated logs are named Virtual Magnetic Imaging Logs or "VMRI" logs for short.),
augmented well data, and combinations thereof (Page 1, Abstract: MRI logs are well logs that use nuclear magnetic resonance to accurately measure free fluid, irreducible water (MBVI), and effective porosity (MPHI). Permeability is then calculated using a mathematical function that incorporates these measured properties. This paper describes the methodology developed to generate synthetic Magnetic Resonance Imaging logs using data obtained by conventional well logs such as SP, Gamma Ray, Caliper, and Resistivity. The synthetically generated logs are named Virtual Magnetic Imaging Logs or "VMRI" logs for short).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to combine the augmented and synthetic well data of Mohaghegh “This paper describes the methodology developed to generate synthetic Magnetic Resonance Imaging logs using data obtained by conventional well logs” with the real well data of Li “including seven potential safe wells and three potential failure wells, were used to validate the neural network”, with the classification process of Li “The CO2 exposed well could be classified into three groups by two criteria… designed neural network for the CO2 injection well leakage risk assessment.” for prediction and evaluation as described by Li “The main idea in this study is to use the neural networks for data training and CO2 leakage risk evaluation and prediction”.
Conclusion
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/HAYDEN JAMES DANSEREAU/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
