DETAILED ACTION
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 (i.e., changing from AIA to pre-AIA ) 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.
Allowable Subject Matter
Claims 7 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 7 and 14, claims 7 and 14 are substantially similar and the prior art as described in the prosecution history does not describe:
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Examiner notes that AMI, MI, P&EI, OCI, and LCI are described in independent claims 1 and 9.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 5, 8-9, 12, 15-16, and 19 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
U.S. Patent Application Publication No. 2019/0292908 (Karimi).
Claim 1:
The cited prior art describes a hydrocarbon asset control system for managing hydrocarbon assets for a hydrocarbon operating facility, comprising: (Karimi: “Embodiments described herein are directed to improving a drilling and completions process at a hydrocarbon extraction site/region and to optimizing resource allocation at a hydrocarbon extraction region. In one embodiment, a computer system accesses data generated by hardware sensors implemented by drilling and completion equipment at the hydrocarbon extraction site.” Paragraph 0003; “Turning now to FIG. 1, a computing architecture 100 is provided which at least one embodiment described herein may be employed. The computing architecture 100 includes a computer system 101. The computer system 101 includes at least one processor 102 and at least some system memory 103. The computer system 101 may be any type of local or distributed computer system, including a cloud computer system.” Paragraph 0035)
a hydrocarbon operating facility that includes hydrocarbon assets; (Karimi: see the hydrocarbon extraction site 118 with drilling and completion equipment 119 as illustrated in figure 1)
at least one hydrocarbon asset monitoring module for receiving operational data related to the hydrocarbon assets; (Karimi: see the receiver 105 and the data formatter 107 as illustrated in figure 1)
a hydrocarbon asset operational data memory that stores operational data related to the hydrocarbon assets of the hydrocarbon operating facility; (Karimi: see the memory 103 as illustrated in figure 1)
a hydrocarbon asset management output translation module; (Karimi: see the transmitter 106 as illustrated in figure 1)
a hydrocarbon asset manager comprising a hydrocarbon asset management software module that, when executed by a processor, causes the hydrocarbon asset control system to perform at least the following: (Karimi: see the computer system 101 with various modules, functions, kernels, or processors as illustrated in figure 1 and as described in paragraph 0037)
receive, from the at least one hydrocarbon asset monitoring module via a transmitter of the hydrocarbon operating facility, at least a portion of the asset data associated with the hydrocarbon assets at an operations facility, wherein at least a portion of the asset data was received via a sensor of at least one of one of the hydrocarbon assets; (Karimi: “For instance, computer system 101 includes a data formatter 107 designed to receive and format sensor data 124. The sensor data 124 may be received from hardware sensors 120 which are used on various pieces of drilling and completion equipment 119 at a hydrocarbon extraction site 118. The hardware sensors 120 may be any type of hardware sensor including temperature sensors, vibration sensors, gas sensors, light sensors, audio sensors, movement sensors, depth or position sensors, velocity sensors or other types of sensors. Each of these hardware sensors 120 may generate sensor data 124 which is received at the communications module 104 and provided to the data formatter 107. The data formatter 107 interprets and formats the sensor data 124 from any or all of the sensors 120, and provides the formatted data 108 to the data miner 109. The formatted data 108 is in a form that is understandable and usable by the data miner 109.” Paragraph 0037)
determine a plurality of categories of asset data, (Karimi: “The analytics allow wells to be ranked based on various parameters including initial production, days to drill, normalized days to drill (e.g. days per 10k ft.), cost/ft., cost/bbl., Drilling Efficiency Index (DEI), Completion Efficiency Index (CEI), NPT, etc. This makes it very difficult, in traditional systems, to compare global efficiency of the wells takes the reservoir management parameters into account as well as D&C parameters. The embodiments herein provide a unifying system to assign each well a global drilling and completion score (e.g. 0-100) that can simplify the comparison of overall well performance and single out the problematic areas which can accelerate the diagnoses process.” Paragraph 0082)
wherein the plurality of categories include at least the following:
an asset maintainability index (MI),
an asset performance and efficiency index (P&EI), (Karimi: see the completion efficiency index (CEI) as described in paragraphs 0082, 0090-0093)
an objective compliance index (OCI), or (Karimi: see the drilling efficiency index (DEI) as described in paragraphs 0082-0089)
a level of competency index (LCI), (Karimi: see the completion tying efficiency (CTE) as described in paragraphs 0082, 0090-0093)
wherein each of the plurality of categories of asset data include key performance indicators (KPIs); (Karimi: “A score is obtained for each Key Performance Indicator (KPI): First, outliers are removed. For example, let v.sub.i be the KPI value for well i, μ and σ be the mean and standard deviation value of KPI for all wells. |v.sub.i-μ|>nσ is considered abnormal values. Let v.sub.best and v.sub.worst be the best and worst normal KPI values, respectively. The KPI score for well i is obtained as” paragraph 0098)
determine a value for each of the plurality of categories of asset data, wherein determining the value for each of the plurality of categories of asset data includes determining a value for each of the KPIs; (Karimi: “A score is obtained for each Key Performance Indicator (KPI)” paragraph 0098; see the obtain KPIs 504 as illustrated in figure 5)
determine a value for an asset management index (AMI) for the hydrocarbon operating facility, wherein the value for AMI includes the value for each of the plurality of categories of asset data; (Karimi: “The Global drilling score may be obtained as the weighted average of each KPI score. The weight for each KPI may vary for each application.” Paragraph 0098)
normalize the AMI; (Karimi: “The embodiments herein provide a unifying system to assign each well a global drilling and completion score (e.g. 0-100) that can simplify the comparison of overall well performance and single out the problematic areas which can accelerate the diagnoses process. Note that, at least in some embodiments, to make the comparison fair and meaningful, the global scores are based on well type (i.e., horizontal, vertical, deviated, highly deviated etc.) and targeted production zones. For instance, among all the horizontal wells drilled in field/region “X” which targeted production zone “Y”, the well with the best performance in the studied area would receive a 100 and the well with the worst performance would receive a 0.” Paragraph 0082)
determine an acceptable range for AMI; (Karimi: “The computer system may instantiate the machine learning unit 129 to analyze the current and historical rig operation data 126 to identify drilling and completion phase 135 non-productive time periods 130 during which the hydrocarbon extraction task is halted or is producing below a specified minimum productivity level (1540).” Paragraph 0124; “One objective during D&C operations is to minimize non-productive time (NPT) and the associated cost. As a rule, non-productive time is any time that the rig (or other piece of D&C equipment) is functioning below a specified level.” Paragraph 0046; “The inefficiencies 115 may identify any individual piece of D&C equipment 119 that is operating at a pace or level that is below what is possible. For instance, if historical data 126 indicates that a given piece of D&C equipment 119 has operated more efficiently in the past, other data surrounding production at that time may be analyzed to learn why that equipment was operating more efficiently at that time.” Paragraph 0110)
determine whether the AMI is within the acceptable range for AMI; and (Karimi: “A global drilling score might show that DEI for the subject well is too low in comparison with other vertical wells in the region while other metrics are in an acceptable range.” Paragraph 0089)
communicate data related to whether the AMI is within the acceptable range to the hydrocarbon asset management output translation module to enable a change to at least a portion of the hydrocarbon assets by adjusting a respective KPI until AMI is within the acceptable range for AMI. (Karimi: “Indeed, method 1400 includes, upon identifying the at least one drilling and completion inefficiency 115, performing at least one remediation step 117 to resolve the at least one identified inefficiency (1460). The remediation step 117, as determined by the remediation module 116, may change operating parameters of the extraction rig 121 and/or certain pieces of D&C equipment 119. Alternatively, the remediation step 117 may be to indicate that the extraction rig should be brought down for maintenance or for replacement of a given part, or that the rig may be pushed beyond the level at which it is currently producing, knowing based on historical data, that the equipment is capable of handling more. Many different scenarios are contemplated for the remediation step and the changes it can cause to take place in the extraction rig 121.” Paragraph 0111; “The remediation module 116 may use the identified D&C performance indicators 114 and inefficiencies 115 to make recommendations on which remediation steps 117 may be taken at the hydrocarbon extraction site 118 to reduce or eliminate the inefficiencies 114, and increase operational performance at the site.” Paragraph 0038)
Claim 2:
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the hydrocarbon asset management software module further causes the hydrocarbon asset control system to perform, at least the following: in response to determining that the AMI is not within the acceptable range for AMI, utilize the hydrocarbon asset management output translation module to implement a change to at least a portion of the hydrocarbon assets to adjust. (Karimi: “Indeed, method 1400 includes, upon identifying the at least one drilling and completion inefficiency 115, performing at least one remediation step 117 to resolve the at least one identified inefficiency (1460). The remediation step 117, as determined by the remediation module 116, may change operating parameters of the extraction rig 121 and/or certain pieces of D&C equipment 119. Alternatively, the remediation step 117 may be to indicate that the extraction rig should be brought down for maintenance or for replacement of a given part, or that the rig may be pushed beyond the level at which it is currently producing, knowing based on historical data, that the equipment is capable of handling more. Many different scenarios are contemplated for the remediation step and the changes it can cause to take place in the extraction rig 121.” Paragraph 0111; “The remediation module 116 may use the identified D&C performance indicators 114 and inefficiencies 115 to make recommendations on which remediation steps 117 may be taken at the hydrocarbon extraction site 118 to reduce or eliminate the inefficiencies 114, and increase operational performance at the site.” Paragraph 0038)
Claim 5:
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the KPIs for OCI include at least one of the following:
flaring index,
energy conservation index, or
production index. (Karimi: see the drilling efficiency index (DEI) as described in paragraphs 0082-0089)
Claim 8:
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the hydrocarbon asset management software module provides an AMI dashboard via a graphical user interface. (Karimi: “FIG. 9 illustrates a snapshot of a visualization tool used to facilitate the quality check process.” Paragraph 0016; “In at least one embodiment, the first step performed by the computer system 101 is to quickly and effectively process large amounts of D&C data (e.g. a daily drilling and completion report 122 with operation data 123 and/or sensor data 124) to extract detailed analytics from the data to identify D&C bottlenecks. A global scoring system is provided herein to identify the problems associated with each well, as well as proposed solutions. Eventually, these analyses are used to optimize future planning and resource allocation at the hydrocarbon extraction site/region 118, and to maximize production/or NPV and improve capital efficiency.” Paragraph 0042)
Claim 9:
Claim 9 is substantially similar to claim 1 and is rejected based on the same reasons and rationale.
9. A method for managing hydrocarbon assets for a hydrocarbon operating facility, comprising:
receiving, by a computing device, asset data associated with a plurality of hydrocarbon assets at an operations facility;
determining, by the computing device, a plurality of categories of asset data,
wherein the plurality of categories include at least the following:
an asset maintainability index (MI),
an asset performance and efficiency index (P&EI),
an objective compliance index (OCI), or
a level of competency index (LCI),
wherein each of the plurality of categories of asset data include key performance indicators (KPIs);
determining, by the computing device, a value for each of the plurality of categories of asset data, wherein determining the value for each of the plurality of categories of asset data includes determining a value for each of the KPIs;
determining, by the computing device, a value for an asset management index (AMI) for the hydrocarbon operating facility, wherein the value for AMI includes the value for each of the plurality of categories of asset data;
normalizing, by the computing device, the AMI;
determining, by the computing device, an acceptable range for AMI;
determining, by the computing device, whether the AMI is within the acceptable range for AMI; and
communicating, by the computing device, data related to whether the AMI is within the acceptable range to a hydrocarbon asset management output translation module to enable a change to at least a portion of the plurality of hydrocarbon assets by adjusting a respective KPI until AMI is within the acceptable range for AMI.
Claim 12:
Claim 12 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
12. The method of claim 9,
wherein the KPIs for OCI include at least one of the following:
flaring index,
energy conservation index, or
production index.
Claim 15:
Claim 15 is substantially similar to claim 8 and is rejected based on the same reasons and rationale
15. The method of claim 9, further comprising providing an AMI dashboard via a graphical user interface.
Claim 16:
Claim 16 is substantially similar to claims 1 and 2 and is rejected based on the same reasons and rationale.
16. A non-transitory computer-readable storage medium for managing hydrocarbon assets for a hydrocarbon operating facility that includes logic that, when executed by a computing device, causes the computing device to perform at least the following:
receive asset data associated with a plurality of hydrocarbon assets at an operations facility;
determine a plurality of categories of asset data, wherein the plurality of categories include at least the following: an asset maintainability index (MI), an asset performance and efficiency index (P&EI), an objective compliance index (OCI), or a level of competency index (LCI), wherein each of the plurality of categories of asset data include key performance indicators (KPIs);
determine a value for each of the plurality of categories of asset data, wherein determining the value for each of the plurality of categories of asset data includes determining a value for each of the KPIs;
determine a value for an asset management index (AMI) for the hydrocarbon operating facility, wherein the value for AMI includes the value for each of the plurality of categories of asset data;
determine an acceptable range for AMI;
determine whether the AMI is within the acceptable range for AMI; and
in response to determining that the AMI is not within the acceptable range for AMI, implement a change to at least one of the KPI until AMI is within the acceptable range for AMI.
Claim 19:
Claim 19 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
19. The non-transitory computer-readable storage medium of claim 16, wherein the KPIs for OCI include at least one of the following: flaring index, energy conservation index, or production index.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3-4, 6, 10-11, 13, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2019/0292908 (Karimi) in view of
U.S. Patent Application Publication No. 2008/0262898 (Tonchev).
Claim 3:
Karimi does not explicitly describe a KPI as described below. However, Tonchev teaches the KPI as described below.
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the KPIs for MI include at least one of the following:
rework data (RD),
qualitative repair history index (QRHI), (Tonchev: see the maintenance costs-hours as illustrated in figure 1)
failure reporting, analysis, and corrective action system (FRACAS) index,
mean time between failures (MTBF),
equipment availability (EA) data, (Tonchev: see the reliability, unplanned downtime, and maintenance backlog as illustrated in figure 1)
relief valve inspection index (RVII), or
on stream inspection index (OSII).
One of ordinary skill in the art would have recognized that applying the known technique of Karimi, namely, improving oil and gas drilling using a data driven approach, with the known techniques of Tonchev, namely, measuring operational performance of hydrocarbon facilities, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Karimi to receive and analyze data to improve hydrocarbon processing with the teachings of Tonchev to determine performance mechanisms for hydrocarbon competitiveness would have been recognized by those of ordinary skill in the art as resulting in an improved hydrocarbon data procsesing system (i.e., the combination of the references provides for a hydrocarbon processing system to analyze various KPIs to optimize the system based on the teaching of a hydrocarbon processing system to optimize the system based on data analysis of Karimi and the teachings of a hydrocarbon data analysis system using various KPIs in Tonchev).
Claim 4:
Karimi does not explicitly describe a KPI as described below. However, Tonchev teaches the KPI as described below.
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the KPIs for P&EI include at least one of the following:
asset integrity management solution (AIMS), (Tonchev: see the unplanned downtime as illustrated in figure 3B)
lubrication index (LI),
corrosion management solution (CMS), or
reliability, availability, and maintenance modeling (RAM) utilization.
Karimi and Tonchev are combinable for the same rationale as set forth above with respect to claim 3.
Claim 6:
Karimi does not explicitly describe a KPI as described below. However, Tonchev teaches the KPI as described below.
The cited prior art describes the hydrocarbon asset control system of claim 1, wherein the KPIs for LCI include at least one of the following:
a competency index for technicians, (Tonchev: see the technical support costs as illustrated in figure 1)
a competency index for operators, and (Tonchev: see the absenteeism as illustrated in figures 1, 3B)
a competency index for inspectors.
Karimi and Tonchev are combinable for the same rationale as set forth above with respect to claim 3.
Claim 10:
Claim 10 is substantially similar to claim 3 and is rejected based on the same reasons and rationale.
10. The method of claim 9, wherein the KPIs for MI include at least one of the following: rework data (RD), qualitative repair history index (QRHI), failure reporting, analysis, and corrective action system (FRACAS) index, mean time between failures (MTBF), equipment availability (EA) data, relief valve inspection index (RVII), or on stream inspection index (OSII).
Claim 11:
Claim 11 is substantially similar to claim 4 and is rejected based on the same reasons and rationale.
11. The method of claim 9, wherein the KPIs for P&EI include at least one of the following: asset integrity management solution (AIMS), lubrication index (LI), corrosion management solution (CMS), or reliability, availability, and maintenance modeling (RAM) utilization.
Claim 13:
Claim 13 is substantially similar to claim 6 and is rejected based on the same reasons and rationale.
13. The method of claim 9, wherein the KPIs for LCI include at least one of the following: a competency index for technicians, a competency index for operators, and a competency index for inspectors.
Claim 17:
Claim 17 is substantially similar to claim 3 and is rejected based on the same reasons and rationale.
17. The non-transitory computer-readable storage medium of claim 16, wherein the KPIs for MI include at least one of the following: rework data (RD), qualitative repair history index (QRHI), failure reporting, analysis, and corrective action system (FRACAS) index, mean time between failures (MTBF), equipment availability (EA) data, relief valve inspection index (RVII), or on stream inspection index (OSII).
Claim 18:
Claim 18 is substantially similar to claim 4 and is rejected based on the same reasons and rationale.
18. The non-transitory computer-readable storage medium of claim 16, wherein the KPIs for P&EI include at least one of the following: asset integrity management solution (AIMS), lubrication index (LI), corrosion management solution (CMS), or reliability, availability, and maintenance modeling (RAM) utilization.
Claim 20:
Claim 20 is substantially similar to claim 6 and is rejected based on the same reasons and rationale.
20. The non-transitory computer-readable storage medium of claim 16, wherein the KPIs for LCI include at least one of the following: a competency index for technicians, a competency index for operators, and a competency index for inspectors.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2017/0309094 describes a decision support system for maintenance recommendations.
U.S. Patent Application Publication No. 2018/0218307 describes a global benchmarking automation solution.
U.S. Patent Application Publication No. 2005/0091102 describes a manufacturing facility performance indicator benchmarking.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117