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 .
Status of the Claims
The Amendment filed on 09/11/2025 has been entered. Claims 1-20 are pending in the instant patent application. Claims 1, 7, 9 and 15 are amended. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections have been updated in light of the amendments and newly cited art.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that the claims are not directed towards a judicial exception, Examiner respectfully disagrees. Examiner maintains that the claim language recites abstract ideas, specifically Mental Processes. Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Regarding Applicant’s arguments that the claims integrate the abstract idea into a practical application, Examiner respectfully disagrees. Examiner states that while the Applicant has asserted alleged improvements, the claims as currently written do no recite the alleged improvements. Furthermore, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. In analyzing the specification, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, (ref: Oct 2019 Update: Subject Matter Eligibility).
Regarding Applicant’s arguments that the claims provide an inventive concept, Examiner respectfully disagrees. The additional elements presented in the claim are generic in nature and doing nothing more than performing their generic functions.
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 therefor, subject to the conditions and requirements of this title.
Regarding Claims 1-8, they are directed towards a system, however the claims are directed to a judicial exception without significantly more. Claims 1-8 are directed to the abstract idea of determining a sustainability action plan.
Performing the Step 2A Prong 1 analysis while referring specifically to Independent Claim 1, claim 1 recites to perform one or more respective operations of an enterprise, to measure operational parameters with regard to extracting hydrocarbons from a subsurface region of the earth, wherein the measured operational parameters comprise current values of one or more sustainability parameters; obtain a sustainability model representative of a state of operations of the enterprise based on the measured operational parameters and simulated operational parameters, wherein the state of operations of the enterprise comprises the one or more respective operations; obtain a plurality of sustainability action plans associated with improving at least one of the one or more sustainability parameters of the enterprise; wherein the plurality of sustainability action plans is obtained by identifying one or more engineering workflow systems of a plurality of engineering workflow systems, and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters; and receiving one or more responses configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameters; determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters in each of the plurality of sustainability action plans, in generating the sustainability model, or both; simulate an effect of each sustainability action plan on the at least one of the one or more sustainability parameters over a period of time based on the sustainability model and the confidence data to generate one or more simulated sustainability parameters for each sustainability action plan; present each sustainability action plan with a respective confidence value associated with an expected effectiveness in improving the at least one of the one or more sustainability parameters determined based on the confidence data; determine a selection of one sustainability action plan of the plurality of sustainability action plans based on the one or more simulated sustainability parameters and the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan, send one or more commands associated with the enterprise to cause to adjust the one or more respective operations according to the one sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region, and configured to adjust the one or more respective operations in response to receiving the one or more commands.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 2-8 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of one or more devices, a separate computing device, one or more engineering workflow systems, a database, one or more sensors, a sustainability platform system and a graphical user interface. The one or more devices, a separate computing device, one or more engineering workflow systems, a database, one or more sensors, a sustainability platform system and a graphical user interface are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1-7 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more devices, one or more sensors, one or more engineering workflow systems, a separate computing device, a database, a sustainability platform system, a graphical user interface and the generic computing elements described in the Applicant's specification in at least Para 0085-0087. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the claims recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...in at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 1-7, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 9-15, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 9-15 are directed to the abstract idea of determining a sustainability action plan.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 9, claim 9 recites obtaining, a sustainability model representative of a state of operations of an enterprise based on measured operational parameters associated with the enterprise and simulated operational parameters with regard to extracting hydrocarbons from a subsurface region of the earth as part of a hydrocarbon production system, wherein the measured operational parameters comprise current values of one or more sustainability parameters, wherein the state of operations of the enterprise comprises one or more respective operations; obtaining, a plurality of sustainability action plans associated with improving at least one of the one or more sustainability parameters of the enterprise, wherein the plurality of sustainability action plans is obtained by: identifying one or more engineering workflow systems of a plurality of engineering workflow systems, and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters; and receiving one or more responses configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameters; determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters in each of the plurality of sustainability action plans, in generating the sustainability model, or both; simulate an effect of each sustainability action plan on the at least one of the one or more sustainability parameters over a period of time based on the sustainability model and the confidence data to generate one or more simulated sustainability parameters for each sustainability action plan; present each sustainability action plan with a respective confidence value associated with an expected effectiveness in improving the at least one of the one or more sustainability parameters determined based on the confidence data; determine a selection of one sustainability action plan of the plurality of sustainability action plans based on the one or more simulated sustainability parameters and the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan, send one or more commands associated with the enterprise to cause to adjust the one or more respective operations according to the one sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region, and configured to adjust the one or more respective operations in response to receiving the one or more commands.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 10-15 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of one or more devices, a computing system, a separate computing device, one or more engineering workflow systems, a database and a graphical user interface. The one or more devices, a computing system, a separate computing device, one or more engineering workflow systems, a database and a graphical user interface are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 9-10, 12 and 14-15 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more devices, a computing system, a separate computing device, one or more engineering workflow systems, a database, a graphical user interface and the generic computing elements described in the Applicant's specification in at least Para 0085-0087. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the claims recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...in at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claims 9-10, 12 and 14-15, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 16-20, they are directed to a non-transitory machine readable medium, however the claims are directed to a judicial exception without significantly more. Claims 16-20 are directed to the abstract idea of determining a sustainability action plan.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 16, claim 16 recites obtaining, a sustainability model representative of a state of operations of an enterprise based on measured operational parameters associated with the enterprise and simulated operational parameters with regard to extracting hydrocarbons from a subsurface region of the earth as part of a hydrocarbon production system, wherein the measured operational parameters comprise current values of one or more sustainability parameters, wherein the state of operations of the enterprise comprises one or more respective operations; obtaining, a plurality of sustainability action plans associated with improving at least one of the one or more sustainability parameters of the enterprise, wherein the plurality of sustainability action plans is obtained by: identifying one or more engineering workflow systems of a plurality of engineering workflow systems, and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems; and querying comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters; and receiving one or more responses configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameters; determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters in each of the plurality of sustainability action plans, in generating the sustainability model, or both; simulate an effect of each sustainability action plan on the at least one of the one or more sustainability parameters over a period of time based on the sustainability model and the confidence data to generate one or more simulated sustainability parameters for each sustainability action plan; present each sustainability action plan with a respective confidence value associated with an expected effectiveness in improving the at least one of the one or more sustainability parameters determined based on the confidence data; determine a selection of one sustainability action plan of the plurality of sustainability action plans based on the one or more simulated sustainability parameters and the respective confidence value for each sustainability action plan; and in response to receiving the selection of the one sustainability action plan, send one or more commands associated with the enterprise to cause to adjust the one or more respective operations according to the one sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region, and configured to adjust the one or more respective operations in response to receiving the one or more commands.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 17-20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of one or more processors, a separate computing device, one or more engineering workflow systems, a database and one or more devices. The one or more processors, a separate computing device, one or more engineering workflow systems, a database and one or more devices are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 16 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, one or more devices, a separate computing device, one or more engineering workflow systems, a database and the generic computing elements described in the Applicant's specification in at least Para 0085-0087. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, the claims recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...in at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 16 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive. Furthermore, arguments are moot in light of newly amended language.
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 1-5, 8-14 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nielsen et al. (US 2023/0351300 A1) in view of Jones (US 2023/0085641 A1) further in view of Kuchuk et al. (US 2010/0076740 A1) .
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding Claim 1, Nielsen teaches the limitations of Claim 1 which state
one or more devices configured to perform one or more respective operations of an enterprise, wherein the one or more devices comprise one or more sensors configured to measure operational parameters of the one or more devices with regard to extracting hydrocarbons from a subsurface region of the earth as part of a hydrocarbon production system, wherein the measured operational parameters comprise current values of one or more sustainability parameters due to the one or more devices (Nielsen: Para 0007, 0013-0015, 0040 via The hydrocarbon operation may be a wellbore operation, such as a drilling operation, a completion operation, or a production operation, or the hydrocarbon operation may be other hydrocarbon exploration operations… The well system 100 can include a wellbore 102 extending through various earth strata. The wellbore 102 can extend through a subterranean formation 104 that can include hydrocarbon material such as oil, gas, coal, or other suitable material… A computing device 140 can be positioned at the surface 122 of the well system 100. In some examples, the computing device 140 can be positioned downhole in the wellbore 102, remote from the well system 100, or in other suitable locations with respect to the well system 100. The computing device 140 can be communicatively coupled to the well tool 110 or other suitable components of the well system 100 via a wired or wireless connections. For example, as illustrated in FIG. 1, the computing device 140 can include an antenna 142 that can allow the computing device 140 to receive and to send communications relating to the well system 100. The computing device 140 may be in communication with another computing device, such as the computing device 200 in FIG. 2, and can receive commands to adjust aspects of the well system 100 based on a determined sustainability recommendations associated with the well system 100. For example, the commands may adjust equipment usage or other aspects for the well system 100… the processor 202 can collect actual data 211 for an activity. The actual data 211 may be collected from sensors or other equipment associated with a hydrocarbon operation that includes the activity).
a sustainability platform system configured to: obtain a sustainability model representative of a state of operations of the enterprise based on the measured operational parameters of the one or more devices and simulated operational parameters of the one or more devices, wherein the state of operations of the enterprise comprises the one or more respective operations (Nielsen: Para 0010, 0017, 0026 via FIG. 2 is a block diagram of an example of a computing device 200 for implementing sustainability recommendations for hydrocarbon operations according to one example of the present disclosure. The hydrocarbon operations may be drilling operations, fracturing operations, completion operations, production operations, other hydrocarbon exploration operations, or a combination thereof. The computing device 200 can include a processor 202, a bus 206, a memory 204, and a display device 224… Upon determining the predicted sustainability state 216, the computing device 200 can generate a comparison between the predicted sustainability state 216 and the sustainability target 210. The comparison can provide insight about how the activity impacts reaching the sustainability target 210. If the comparison indicates that the predicted sustainability state 216 meets the sustainability target 210, the computing device 200 may generate an output indicating that the activity and the parameters of the activity are satisfactory for meeting the sustainability target 210… The sustainability target may be an emissions or resource usage target that is to be satisfied by a particular point in time. The level of assessment may be a particular well, an asset, a region, or an entity portfolio. The system can receive actual data for an activity associated with the hydrocarbon operation and generate a sustainability metric for the activity based on the actual data and parameters of the activity. Examples of the parameters can include equipment or energy sources that are to be used during the hydrocarbon operation. Examples of the sustainability metric may include an amount of carbon emissions or a cost associated with the hydrocarbon operation. The system can then generated a predicted sustainability state for the level of assessment at the particular point in time based on the sustainability metric, the actual data, and the parameters. If the predicted sustainability state exceeds the sustainability target, the system can generate a recommendation for at least one action that can be taken to reduce the predicted sustainability state. The action can be an adjustment to the parameters of the activity. The system can then output the recommendation and receive a selection of an action of the recommendation that is to be performed for the activity);
obtain a plurality of sustainability action plans associated with improving at least one of the one or more sustainability parameters of the enterprise obtain a plurality of sustainability action plans associated with improving at least one of the one or more sustainability parameters of the enterprise (Nielsen: Para 0011, 0028-0029 via The system can receive data indicating fuel use associated with a previous drilling operation in the region of a similar offset well. Based on the fuel use for the drilling operation, the system can determine that the region is expected to have a predicted sustainability state that exceeds the sustainability target in five years. The system can then generate recommendations of changes of drilling fluids, well designs, or other drilling conditions that are expected to result in a predicted sustainability state that satisfies the sustainability target in five years. The system can output the recommendations to a user that can provide a selection of one or more of the changes...The computing device 200 may generate and output multiple recommendations 218 that a user can compare. Each of the recommendations 218 can include indications of an associated impact of each recommended action on the predicted sustainability state 216, an associated uncertainty value for the predicted sustainability state 216 for each recommended action, or a contribution of each parameter of the activity to the predicted sustainability state 216. For example, a recommendation may indicate that the engine contributes the most to the predicted sustainability state 216 and that switching from a first type of engine to a second type of engine for a drilling operation can reduce the emissions by 2%±0.3%...A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation. In some examples, the computing device 200 may receive a selection of multiple actions);
identifying one or more engineering workflow systems of a plurality of engineering workflow systems, wherein each of the plurality of engineering workflow systems corresponds to a separate computing device (Nielsen: Para 0017, 0037, 0047, 0056 via hydrocarbon operations may be drilling operations, fracturing operations, completion operations, production operations, other hydrocarbon exploration operations, or a combination thereof… In block 310, the processor 202 can generate a recommendation 218 for at least one action based on the predicted sustainability state 216 and the sustainability target 210 for the hydrocarbon operation. The recommendation 218 can be recommendations for actions that result in the predicted sustainability state 216 aligning with the sustainability target 210. As an example, the processor 202 may generate a recommendation 218 of using 49% grid power, 28% tier 4 diesel engines, and 23% tier 2 diesel engines for the hydrocarbon operation to achieve the sustainability target 210 for the hydrocarbon operation… In some aspects, a system, a method, or a non-transitory computer-readable medium for generating sustainability recommendations for hydrocarbon operations according to one or more of the following examples: receiving a sustainability target for a level of assessment for a hydrocarbon operation; receiving actual data for an activity associated with the hydrocarbon operation; generating a sustainability metric based on the actual data and one or more parameters of the activity; generating, by at least one algorithm, a predicted sustainability state for the level of assessment at a subsequent point in time based on the sustainability metric, the actual data, and the one or more parameters of the activity; generating a recommendation for at least one action based on the predicted sustainability state and the sustainability target for the hydrocarbon operation; and outputting the recommendation for the at least one action for adjusting the activity of the hydrocarbon operation),
and wherein the one or more engineering workflow systems are identified by: broadcasting a request indicative of the one or more sustainability parameters to the plurality of engineering workflow systems (Nielsen: Para 0038 via the processor 202 can output the recommendation 218 for the at least one action for adjusting the activity of the hydrocarbon operation. The processor 202 can output the recommendation 218 to a user interface 226 of a display device 224. The user can provide a selection of one or more actions of the recommendation 218, and the processor 202 can perform the one or more actions for the hydrocarbon operations, such as by implementing adjustments to the hydrocarbon operation to align the parameters of the activity with the selected actions. The processor 202 can continue receiving actual data 211 for the activity after the actions have been performed. So, the processor 202 can continually update the sustainability metric 212, the predicted sustainability state 216, and the recommendations 218 so that the level of assessment can maintain a trajectory to satisfy with the sustainability target 210 at the subsequent point in time).
However, Nielsen does not explicitly disclose the limitation of Claim 1 which states querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters; and receiving one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameters.
Jones though, with the teachings of Nielsen, teaches of
querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters (Jones: Para 0055 via Demand response layer 214 can further include or draw upon one or more demand response policy definitions (e.g., databases, XML, files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user's application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.)); and
receiving one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameter (Jones: Para 0058 via Integrated control layer 218 can be configured to provide feedback to demand response layer 214 so that demand response layer 214 checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints can also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer 218 is also logically below fault detection and diagnostics layer 216 and automated measurement and validation layer 212. Integrated control layer 218 can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nielsen with the teachings of Jones in order to have querying a database comprising a list of abatement technologies associated with each of the plurality of engineering workflow systems, wherein each abatement technology of the list corresponds to performing an operation to modify the at least one of the one or more sustainability parameters; and receiving one or more responses from the one or more engineering workflow systems, wherein the one or more engineering workflow systems are configured to provide the one or more responses based on an association between a respective abatement technology of each of the one or more engineering workflow systems and the at least one of the one or more sustainability parameters, wherein the one or more responses comprise one or more sustainability action plans are associated with improving the at least one of the one or more sustainability parameters. The motivations behind this being to incorporate the teachings of improving the sustainability of a building. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, Nielsen does not explicitly disclose the limitations of Claim 1 which state determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters of the one or more devices in each of the plurality of sustainability action plans, in generating the sustainability model, or both.
Kuchuk though, with the teachings of Nielsen/Jones, teaches of
determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters of the one or more devices in each of the plurality of sustainability action plans, in generating the sustainability model, or both (Kuchuk: Para 0043-0045 via The present disclosure details the entire workflow of the TDIP in the design, acquisition and interpretation and describes the processes involved in detail. The overall TDIP Workflow is shown in FIG. 10, and includes a multi-step workflow including a preliminary design stage of FIG. 9, which includes steps and inputs employed for designing a well test operation prior to its implementation. Because of the high level of uncertainties about the reservoir and fluid properties, the output of the stage of FIG. 9 can include an expected distribution of test plans. The plan which guaranties, for example, with 90%-95% statistical confidence, achievement of all suitable test objectives, is then chosen for implementation. In FIG. 9, the data input step 902 includes a list of test objectives, which can be a most important data input to the TDIP because the whole testing activities are planned and implemented to meet the objectives. Expected reservoir models are also provided to take into account expectation of the reservoir behavior from the points of view of various disciplines. The expected reservoir model input can be in the form of simple analytical models or more complicated numerical reservoir models derived from a more complex geological model. This can include any suitable information about the reservoir layering, existence of fractures and faults, and the like. In both cases, the TDIP platform provides an appropriate connection with either kind of simulator to facilitate data and results exchange before and during the test implementation. Expected reservoir properties also are provided along with their corresponding range of uncertainty. This provides the opportunity to run more realizations of the reservoir behavior and calculate the range of uncertainties on the total test duration needed to achieve the test objectives. Expected fluid properties also are provided along with their expected range of accuracy. Metrology, including specifications of the available gauges for a certain job, also is input to the TDIP prior to testing. According to an appropriate test design, a check is made as to whether or not the current measurement devices are capable of accurate data acquisition, to a level that is needed for accurate interpretation. Based on the input data of step 902, a series of realizations of a well test are simulated and a series of total well test durations are generated at steps 904-920. This is done considering any suitable limitations in measurement devices. The output of this stage at step 920 is a distribution of total test duration, which enables the achievement of the test objectives with regard to the limitations in measurement systems. A decision is then made to select a test plan which can guarantee the achievement of the test objectives. Since a distribution of duration is available, selection of the test duration, and its corresponding test program, which provides at least 90% statistical confidence for meeting the test objectives, can be recommended. Advantageously, the TDIP provides an interactive workflow and the test program can be terminated earlier or extended beyond the base plan, depending on whether or not the test objectives are met).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nielsen/Jones with the teachings of Kuchuk in order to have determine confidence data associated with a likelihood that each sustainability action plan of the plurality of sustainability action plans will improve the at least one of the one or more sustainability parameters, wherein the confidence data is determined based on an uncertainty associated with using the measured operational parameters and the simulated operational parameters of the one or more devices in each of the plurality of sustainability action plans, in generating the sustainability model, or both. The motivations behind this being to incorporate the teachings of well test design and interpretation. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Nielsen/Jones/Kuchuk further teaches the limitations of Claim 1 which state
simulate an effect of each sustainability action plan on the at least one of the one or more sustainability parameters over a period of time based on the sustainability model and the confidence data to generate one or more simulated sustainability parameters for each sustainability action plan (Nielsen: Para 0036-0038 via the processor 202 can generate, by at least one algorithm 214, a predicted sustainability state 216 for the level of assessment at a subsequent point in time, which can correspond to the point in time in which the sustainability target 210 is to be met. The at least one algorithm 214 can generate the predicted sustainability state 216 based on the sustainability metric 212, the actual data 211, and the one or more parameters of the activity. Examples of the predicted sustainability state 216 can include a total greenhouse gas emissions in a defined time period, a total water use in the defined time period, or a total chemical wastage in the defined time period. In block 310, the processor 202 can generate a recommendation 218 for at least one action based on the predicted sustainability state 216 and the sustainability target 210 for the hydrocarbon operation. The recommendation 218 can be recommendations for actions that result in the predicted sustainability state 216 aligning with the sustainability target 210. As an example, the processor 202 may generate a recommendation 218 of using 49% grid power, 28% tier 4 diesel engines, and 23% tier 2 diesel engines for the hydrocarbon operation to achieve the sustainability target 210 for the hydrocarbon operation. In block 312, the processor 202 can output the recommendation 218 for the at least one action for adjusting the activity of the hydrocarbon operation. The processor 202 can output the recommendation 218 to a user interface 226 of a display device 224. The user can provide a selection of one or more actions of the recommendation 218, and the processor 202 can perform the one or more actions for the hydrocarbon operations, such as by implementing adjustments to the hydrocarbon operation to align the parameters of the activity with the selected actions. The processor 202 can continue receiving actual data 211 for the activity after the actions have been performed. So, the processor 202 can continually update the sustainability metric 212, the predicted sustainability state 216, and the recommendations 218 so that the level of assessment can maintain a trajectory to satisfy with the sustainability target 210 at the subsequent point in time; “confidence data” taught in Kuchuk Para 0043-0045, 0060, 0063);
present each sustainability action plan with a respective confidence value associated with an expected effectiveness in improving the at least one of the one or more sustainability parameters determined based on the confidence data via a graphical user interface (Kuchuk: Para 0043-0045 via The present disclosure details the entire workflow of the TDIP in the design, acquisition and interpretation and describes the processes involved in detail. The overall TDIP Workflow is shown in FIG. 10, and includes a multi-step workflow including a preliminary design stage of FIG. 9, which includes steps and inputs employed for designing a well test operation prior to its implementation. Because of the high level of uncertainties about the reservoir and fluid properties, the output of the stage of FIG. 9 can include an expected distribution of test plans. The plan which guaranties, for example, with 90%-95% statistical confidence, achievement of all suitable test objectives, is then chosen for implementation. In FIG. 9, the data input step 902 includes a list of test objectives, which can be a most important data input to the TDIP because the whole testing activities are planned and implemented to meet the objectives. Expected reservoir models are also provided to take into account expectation of the reservoir behavior from the points of view of various disciplines. The expected reservoir model input can be in the form of simple analytical models or more complicated numerical reservoir models derived from a more complex geological model. This can include any suitable information about the reservoir layering, existence of fractures and faults, and the like. In both cases, the TDIP platform provides an appropriate connection with either kind of simulator to facilitate data and results exchange before and during the test implementation. Expected reservoir properties also are provided along with their corresponding range of uncertainty. This provides the opportunity to run more realizations of the reservoir behavior and calculate the range of uncertainties on the total test duration needed to achieve the test objectives. Expected fluid properties also are provided along with their expected range of accuracy. Metrology, including specifications of the available gauges for a certain job, also is input to the TDIP prior to testing. According to an appropriate test design, a check is made as to whether or not the current measurement devices are capable of accurate data acquisition, to a level that is needed for accurate interpretation. Based on the input data of step 902, a series of realizations of a well test are simulated and a series of total well test durations are generated at steps 904-920. This is done considering any suitable limitations in measurement devices. The output of this stage at step 920 is a distribution of total test duration, which enables the achievement of the test objectives with regard to the limitations in measurement systems. A decision is then made to select a test plan which can guarantee the achievement of the test objectives. Since a distribution of duration is available, selection of the test duration, and its corresponding test program, which provides at least 90% statistical confidence for meeting the test objectives, can be recommended. Advantageously, the TDIP provides an interactive workflow and the test program can be terminated earlier or extended beyond the base plan, depending on whether or not the test objectives are met);
determine a selection of one sustainability action plan of the plurality of sustainability action plans based on the one or more simulated sustainability parameters and the respective confidence value for each sustainability action plan (Kuchuk: Para 0043-0045 via The present disclosure details the entire workflow of the TDIP in the design, acquisition and interpretation and describes the processes involved in detail. The overall TDIP Workflow is shown in FIG. 10, and includes a multi-step workflow including a preliminary design stage of FIG. 9, which includes steps and inputs employed for designing a well test operation prior to its implementation. Because of the high level of uncertainties about the reservoir and fluid properties, the output of the stage of FIG. 9 can include an expected distribution of test plans. The plan which guaranties, for example, with 90%-95% statistical confidence, achievement of all suitable test objectives, is then chosen for implementation. In FIG. 9, the data input step 902 includes a list of test objectives, which can be a most important data input to the TDIP because the whole testing activities are planned and implemented to meet the objectives. Expected reservoir models are also provided to take into account expectation of the reservoir behavior from the points of view of various disciplines. The expected reservoir model input can be in the form of simple analytical models or more complicated numerical reservoir models derived from a more complex geological model. This can include any suitable information about the reservoir layering, existence of fractures and faults, and the like. In both cases, the TDIP platform provides an appropriate connection with either kind of simulator to facilitate data and results exchange before and during the test implementation. Expected reservoir properties also are provided along with their corresponding range of uncertainty. This provides the opportunity to run more realizations of the reservoir behavior and calculate the range of uncertainties on the total test duration needed to achieve the test objectives. Expected fluid properties also are provided along with their expected range of accuracy. Metrology, including specifications of the available gauges for a certain job, also is input to the TDIP prior to testing. According to an appropriate test design, a check is made as to whether or not the current measurement devices are capable of accurate data acquisition, to a level that is needed for accurate interpretation. Based on the input data of step 902, a series of realizations of a well test are simulated and a series of total well test durations are generated at steps 904-920. This is done considering any suitable limitations in measurement devices. The output of this stage at step 920 is a distribution of total test duration, which enables the achievement of the test objectives with regard to the limitations in measurement systems. A decision is then made to select a test plan which can guarantee the achievement of the test objectives. Since a distribution of duration is available, selection of the test duration, and its corresponding test program, which provides at least 90% statistical confidence for meeting the test objectives, can be recommended. Advantageously, the TDIP provides an interactive workflow and the test program can be terminated earlier or extended beyond the base plan, depending on whether or not the test objectives are met);
and in response to receiving the selection of the one sustainability action plan, send one or more commands to the one or more devices associated with the enterprise to cause the one or more devices to adjust the one or more respective operations according to the one sustainability action plan, wherein the one or more devices correspond to controlling one or more properties associated with extracting the hydrocarbons from the subsurface region, and wherein the one or more devices are configured to adjust the one or more respective operations in response to receiving the one or more commands (Jones: Para 0142-0145 via The recommendation can be at least one of a FIM, operational improvements (e.g., adjustments to at least one operating parameter), building equipment maintenance routines and/or incentive programs… In step 1120, at least one recommendation is provided. The recommendations can be provided to a user device (e.g., the user device 318). The recommendations can be the recommendations generated in step 1115. The triage and planning system 302 can provide the recommendations to the user device 318. The triage and planning system 302 providing the recommendations to the user device 318 can cause the user device 318, via a user interface, to display the recommendations. The user interface can include the recommendations and one or more changes that can implement the recommendations. The operator of the user device 318 can engage with, interact with or otherwise interface with at least a portion of the user interface. For example, the operator of the user device 318 can select an icon that corresponds to at least one recommendation…In step 1125, at least one command is received. The command can correspond to the recommendation that was selected by the operator of the user device 318 in step 1120. The command can implement one or more changes to the operating parameters of the building 10 and/or the pieces of building equipment that pertain to the building 10. The command can include at least one operational improvement that was generated in step 1115…In step 1130, at least one set of operating parameters are revised. The operating parameters can be revised for at least one piece of building equipment that pertains to the building 10. The operating parameters can be revised to correspond with the operational improvements that were included in the command in step 1125. Control signals can be, responsive to the revising the operating parameters, provided to the pieces of building equipment that pertain to the revised set of operation parameters. The control signals can cause at least one of operational changes to the pieces of building equipment, parameter adjustments to the pieces of building equipment and/or adjust in at least one sustainability parameter that pertains to the pieces of building equipment. The control signals can be similar to the control signals described in relation to the system 200).
Regarding Claim 2, the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 2 which state
wherein the sustainability platform system is configured to provide the simulated sustainability parameters and the confidence data for the one sustainability action plan to a user device (Nielsen: Para 0028-0029 via The computing device 200 may generate and output multiple recommendations 218 that a user can compare. Each of the recommendations 218 can include indications of an associated impact of each recommended action on the predicted sustainability state 216, an associated uncertainty value for the predicted sustainability state 216 for each recommended action, or a contribution of each parameter of the activity to the predicted sustainability state 216. For example, a recommendation may indicate that the engine contributes the most to the predicted sustainability state 216 and that switching from a first type of engine to a second type of engine for a drilling operation can reduce the emissions by 2%?0.3%... A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation. In some examples, the computing device 200 may receive a selection of multiple actions).
Regarding Claim 3, the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 3 which state
wherein the sustainability platform system is configured to determine the selection of the one sustainability action plan by receiving a user selection of the one sustainability action plan or by receiving an approval of the one sustainability action plan from the user device (Nielsen: Para 0029 via A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation. In some examples, the computing device 200 may receive a selection of multiple actions).
Regarding Claim 4, the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 4 which state
determining whether the one sustainability action plan is effective to improve the at least one of the one or more sustainability parameters based on a comparison of the one or more simulated sustainability parameters to sustainability target data, wherein the sustainability target data comprises one or more threshold limits, one or more ranges, or both; and in response to determining that the one sustainability action plan is effective, selecting the sustainability action plan (Nielsen: Para 0026, 0029 via Upon determining the predicted sustainability state 216, the computing device 200 can generate a comparison between the predicted sustainability state 216 and the sustainability target 210. The comparison can provide insight about how the activity impacts reaching the sustainability target 210. If the comparison indicates that the predicted sustainability state 216 meets the sustainability target 210, the computing device 200 may generate an output indicating that the activity and the parameters of the activity are satisfactory for meeting the sustainability target 210 ..A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation. In some examples, the computing device 200 may receive a selection of multiple actions).
Regarding Claim 5, the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 5 which state
obtain the alternate sustainability action plan (Nielsen: Para 0027 via Alternatively, if the comparison indicates that the predicted sustainability state 216 does not meet the sustainability target 210, the computing device 200 can generate recommendations 218 for at least one action based on the predicted sustainability state 216 and the sustainability target 210 for the hydrocarbon operation. The actions can be adjustments to the parameters of the activity. The computing device 200 can use a machine-learning model to determine the recommendations 218. The recommendations 218 may be based on the sustainability metric 212 for the hydrocarbon operation. For example, the sustainability metric 212 may be emissions or cost, so the recommendations 218 can be adjustments of the parameters that minimize the emissions or the cost. Training data can be used to train a machine learning network for generating the recommendations 218. In an example, the training data can be historical data associated with parameters and resulting sustainability metrics for hydrocarbon operations);
determine second confidence data associated with the alternate sustainability action plan; simulate a second effect of the alternate sustainability action plan on the at least one of the one or more sustainability parameters over the period of time based on the sustainability model and the second confidence data to generate one or more second simulated sustainability parameters (Kuchuk: Para 0043-0045 via The present disclosure details the entire workflow of the TDIP in the design, acquisition and interpretation and describes the processes involved in detail. The overall TDIP Workflow is shown in FIG. 10, and includes a multi-step workflow including a preliminary design stage of FIG. 9, which includes steps and inputs employed for designing a well test operation prior to its implementation. Because of the high level of uncertainties about the reservoir and fluid properties, the output of the stage of FIG. 9 can include an expected distribution of test plans. The plan which guaranties, for example, with 90%-95% statistical confidence, achievement of all suitable test objectives, is then chosen for implementation. In FIG. 9, the data input step 902 includes a list of test objectives, which can be a most important data input to the TDIP because the whole testing activities are planned and implemented to meet the objectives. Expected reservoir models are also provided to take into account expectation of the reservoir behavior from the points of view of various disciplines. The expected reservoir model input can be in the form of simple analytical models or more complicated numerical reservoir models derived from a more complex geological model. This can include any suitable information about the reservoir layering, existence of fractures and faults, and the like. In both cases, the TDIP platform provides an appropriate connection with either kind of simulator to facilitate data and results exchange before and during the test implementation. Expected reservoir properties also are provided along with their corresponding range of uncertainty. This provides the opportunity to run more realizations of the reservoir behavior and calculate the range of uncertainties on the total test duration needed to achieve the test objectives. Expected fluid properties also are provided along with their expected range of accuracy. Metrology, including specifications of the available gauges for a certain job, also is input to the TDIP prior to testing. According to an appropriate test design, a check is made as to whether or not the current measurement devices are capable of accurate data acquisition, to a level that is needed for accurate interpretation. Based on the input data of step 902, a series of realizations of a well test are simulated and a series of total well test durations are generated at steps 904-920. This is done considering any suitable limitations in measurement devices. The output of this stage at step 920 is a distribution of total test duration, which enables the achievement of the test objectives with regard to the limitations in measurement systems. A decision is then made to select a test plan which can guarantee the achievement of the test objectives. Since a distribution of duration is available, selection of the test duration, and its corresponding test program, which provides at least 90% statistical confidence for meeting the test objectives, can be recommended. Advantageously, the TDIP provides an interactive workflow and the test program can be terminated earlier or extended beyond the base plan, depending on whether or not the test objectives are met);
and in response to determining that the alternate sustainability action plan is effective to improve the at least one of the one or more sustainability parameters, determining the selection of the alternate sustainability action plan and sending one or more other commands to the one or more devices associated with the enterprise to cause the one or more devices to adjust the one or more respective operations according to the alternate sustainability action plan (Nielsen: Para 0027, 0029, 0038 via Alternatively, if the comparison indicates that the predicted sustainability state 216 does not meet the sustainability target 210, the computing devicе 200 can generate recommendations 218 for at least one action based on the predicted sustainability state 216 and the sustainability target 210 for the hydrocarbon operation. The actions can be adjustments to the parameters of the activity. The computing device 200 can use a machine-learning model to determine the recommendations 218. The recommendations 218 may be based on the sustainability metric 212 for the hydrocarbon operation. For example, the sustainability metric 212 may be emissions or cost, so the recommendations 218 can be adjustments of the parameters that minimize the emissions or the cost. Training data can be used to train a machine learning network for generating the recommendations 218. In an example, the training data can be historical data associated with parameters and resulting sustainability metrics for hydrocarbon operations...A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason...the processor 202 can output the recommendation 218 for the at least one action for adjusting the activity of the hydrocarbon operation. The processor 202 can output the recommendation 218 to a user interface 226 of a display device 224. The user can provide a selection of one or more actions of the recommendation 218, and the processor 202 can perform the one or more actions for the hydrocarbon operations, such as by implementing adjustments to the hydrocarbon operation to align the parameters of the activity with the selected actions).
Regarding Claim 8, the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 8 which state
wherein the at least one of the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof (Nielsen: Para 0021-0023 via The computing device 200 may include a sustainability target 210 for a hydrocarbon operation. The computing device 200 may receive the sustainability target 210 as input from a user associated with the hydrocarbon operation. The sustainability target 210 may be set for a desired level of assessment, forecast, or review. For example, a level of assessment for the hydrocarbon operation could include, but is not limited to, sustainability targets for specific wells, assets, regions, or entity portfolios. Examples of the sustainability target 210 may be an emissions limit, a water usage limit, a fuel consumption limit, etc...The computing device 200 may receive the actual data 211 as an input from the user, or the computing device 200 may access a database that stores the actual data 211. The actual data 211 can be determined by the activity being modelled, and could include, but is not limited to, collected data on equipment usage hours, fuel consumption, logistics movements, well design parameters, production statistics, completions parameters, water usage, and waste statistics. The activity may be one or more stages of the hydrocarbon operation. For example, the activity may be a drilling operation, cementing operation, completion operation, fracturing operation, perforation operation, production operation, or a combination thereof...the computing device 200 can generate a sustainability metric 212 based on the actual data 211 and one or more parameters of the activity. As an example, the sustainability metric 212 may be an amount carbon dioxide emissions for the activity or a water usage for the activity in a particular time period, such as an hour or a day. The parameters can involve types of energy sources and equipment used during the activity).
Regarding Claim 9, it is analogous to Claim 1 and is rejected for the same reasons.
Regarding Claim 10, it is analogous to Claim 4 and is rejected for the same reasons.
Regarding Claim 11, it is analogous to Claim 8 and is rejected for the same reasons.
Regarding Claim 12, it is analogous to Claim 2 and is rejected for the same reasons.
Regarding Claim 13, it is analogous to Claim 3 and is rejected for the same reasons.
Regarding Claim 14, it is analogous to Claim 5 and is rejected for the same reasons.
Regarding Claim 16, it is analogous to Claim 1 and is rejected for the same reasons (Nielsen: Para 0018, 0047 teaches of a non-transitory machine readable medium and one or more processors).
Regarding Claim 17, the combination of Nielsen/Kuchuk, teaches the limitations of Claim 17 which state
determining whether the one sustainability action plan is effective to improve the at least one of the one or more sustainability parameters based on a comparison of the one or more simulated sustainability parameters to sustainability target data, wherein the sustainability target data comprises one or more threshold limits, one or more ranges, or both (Nielsen: Para 0026, 0029 via Upon determining the predicted sustainability state 216, the computing device 200 can generate a comparison between the predicted sustainability state 216 and the sustainability target 210. The comparison can provide insight about how the activity impacts reaching the sustainability target 210. If the comparison indicates that the predicted sustainability state 216 meets the sustainability target 210, the computing device 200 may generate an output indicating that the activity and the parameters of the activity are satisfactory for meeting the sustainability target 210 ..A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation. In some examples, the computing device 200 may receive a selection of multiple actions),
and wherein the at least one of the one or more sustainability parameters comprise a carbon footprint of the one or more devices, a water usage of the one or more devices, a waste output of the one or more devices, a greenhouse gas emission of the one or more devices, or any combination thereof (Nielsen: Para 0021-0023 via The computing device 200 may include a sustainability target 210 for a hydrocarbon operation. The computing device 200 may receive the sustainability target 210 as input from a user associated with the hydrocarbon operation. The sustainability target 210 may be set for a desired level of assessment, forecast, or review. For example, a level of assessment for the hydrocarbon operation could include, but is not limited to, sustainability targets for specific wells, assets, regions, or entity portfolios. Examples of the sustainability target 210 may be an emissions limit, a water usage limit, a fuel consumption limit, etc...The computing device 200 may receive the actual data 211 as an input from the user, or the computing device 200 may access a database that stores the actual data 211. The actual data 211 can be determined by the activity being modelled, and could include, but is not limited to, collected data on equipment usage hours, fuel consumption, logistics movements, well design parameters, production statistics, completions parameters, water usage, and waste statistics. The activity may be one or more stages of the hydrocarbon operation. For example, the activity may be a drilling operation, cementing operation, completion operation, fracturing operation, perforation operation, production operation, or a combination thereof...the computing device 200 can generate a sustainability metric 212 based on the actual data 211 and one or more parameters of the activity. As an example, the sustainability metric 212 may be an amount carbon dioxide emissions for the activity or a water usage for the activity in a particular time period, such as an hour or a day. The parameters can involve types of energy sources and equipment used during the activity); and
in response to determining that the one sustainability action plan is effective, selecting the one sustainability action plan (Nielsen: Para 0029 via A user may then select a recommendation of the recommendations 218 for the hydrocarbon operation. The user may select the recommendation that is indicated as having the best impact on the predicted sustainability state 216. That is, the user may select the recommendation that is predicted to result in the predicted sustainability state 216 aligning the closest with the sustainability target 210. Alternatively, the user may select a recommendation for a different reason. The computing device 200 can receive the selection of the recommendation).
Regarding Claim 18, it is analogous to Claim 5 and is rejected for the same reasons.
Claims 6-7, 15 and 19-20 is/are rejected under 35 U.S.C. 103 as being obvious over Nielsen et al. (US 2023/0351300 A1) in view of Jones (US 2023/0085641 A1) in view of Kuchuk et al. (US 2010/0076740 A1) further in view of Levine et al. (US 2016/0350778 A1).
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). The teachings, suggestions, and motivations in this
prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding Claim 6, while the combination of Nielsen/Jones/Kuchuk teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 6 which state wherein the sustainability platform system is configured to obtain the sustainability model by: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data.
Levine though, with the teachings of Nielsen/Jones/Kuchuk, teaches of
wherein the sustainability platform system is configured to obtain the sustainability model by: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data (Levine: Para 0170 via FIGS. 16E and 16F depict example interfaces 1650 and 1660 for entering and altering parameters of a solar energy simulation, according to an embodiment. In an embodiment, for a given solar energy offering, one or more solar energy simulations may be performed as described with respect to FIGS. 3 and 4. Interface 1650 may include panels 1652, 1654, 1636, 1655, and 1639. Panel 1652 may display parameters for a solar energy simulation such as, but not limited to, solar array size, solar module power, calculated direct sunlight to the solar array, average conventional electricity monthly usage, current electric utility provider, current electric utility schedule, current conventional electricity rate (e.g., electricity costs per kWh), estimated conventional electricity annual rate increase, and average conventional electricity monthly cost. In an embodiment, direct Page 40 sunlight may be calculated by a data collection module, such as data collection module 1412 of FIG. 14, prior to performing the solar energy simulation. Panel 1654 may display additional parameters for a solar energy simulation such as, but not limited to, battery storage, mounting options, and equipment upgrades (e.g., panel upgrades. In an embodiment, each parameter may be altered by a user interacting with interface 1650. A solar energy simulation may be run each time a parameter is altered, or manually after entering or altering one or more parameters. Panel 1655 may enable a user to create an account with the online solar marketplace in order to save details the solar energy offering, solar energy simulation parameters, and solar energy simulation results for later viewing and action).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nielsen/Jones/Kuchuk with the teachings of Levine in order to have wherein the sustainability platform system is configured to obtain the sustainability model by: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data. The motivations behind this being to incorporate the teachings of carbon emission reduction at targeted properties/regions. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 7, while the combination of Nielsen/Jones/Kuchuk teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 6 which state wherein the sustainability platform system is configured to obtain the one sustainability action plan by: identifying the one or more engineering workflow systems correlating with the sustainability model and associated with the at least one of the one or more sustainability parameters; and generate, via the one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model.
Levine though, with the teachings of Nielsen/Jones/Kuchuk, teaches of
wherein the sustainability platform system is configured to obtain the one sustainability action plan by: identifying one or more engineering workflow systems correlating with the sustainability model and associated with the at least one of the one or more sustainability parameters; and generate, via the identified one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model (Levine: Para 0073-0074 via Detailed installation models and planning may be performed on the virtual property models to produce an optimal system design for best solar performance. These may also be made code compliant, as, in an embodiment, the marketplace may be coupled to a building code database and permitting database via a network, such as the Internet. When running a simulation, various factors may be taken into account to determine the best design and offering. As illustrated in FIG. 3, these factors may include, but are not limited to, latitude and atmospheric conditions, roof shadows, slope and orientation, buildable area, setbacks, and obstructions, equipment type, installed cost, efficiency, and warranty, load profiles, utility rates, available incentives, lease terms, loan terms, financing costs, and installer certifications. In one embodiment, the design process may be fully automated, with different equipment and financing options being fit to the 3D property model, or scene, and each option being compared, for example by a genetic algorithm, to determine the best system design is available).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nielsen/Jones/Kuchuk with the teachings of Levine in order to have wherein the sustainability platform system is configured to obtain the one sustainability action plan by: identifying one or more engineering workflow systems correlating with the sustainability model and associated with the at least one of the one or more sustainability parameters; and generate, via the identified one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model. The motivations behind this being to incorporate the teachings of carbon emission reduction at targeted properties/regions. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 15, it is analogous to Claim 7 and is rejected for the same reasons.
Regarding Claim 19, while the combination of Nielsen/Jones/Kuchuk, teaches the limitations of Claim 16, it does not explicitly disclose the limitations of Claim 19 which state obtaining the sustainability model comprises: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data; and obtaining the one sustainability action plan comprises: identifying one or more engineering workflow systems that correlate with the sustainability model and are associated with the at least one of the one or more sustainability parameters; and generating, via the identified one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model.
Levine though, with the teachings of Nielsen/Kuchuk, teaches of
obtaining the sustainability model comprises: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data (Levine: Para 0170 via FIGS. 16E and 16F depict example interfaces 1650 and 1660 for entering and altering parameters of a solar energy simulation, according to an embodiment. In an embodiment, for a given solar energy offering, one or more solar energy simulations may be performed as described with respect to FIGS. 3 and 4. Interface 1650 may include panels 1652, 1654, 1636, 1655, and 1639. Panel 1652 may display parameters for a solar energy simulation such as, but not limited to, solar array size, solar module power, calculated direct sunlight to the solar array, average conventional electricity monthly usage, current electric utility provider, current electric utility schedule, current conventional electricity rate (e.g., electricity costs per kWh), estimated conventional electricity annual rate increase, and average conventional electricity monthly cost. In an embodiment, direct sunlight may be calculated by a data collection module, such as data collection module 1412 of FIG. 14, prior to performing the solar energy simulation. Panel 1654 may display additional parameters for a solar energy simulation such as, but not limited to, battery storage, mounting options, and equipment upgrades (e.g., panel upgrades. In an embodiment, each parameter may be altered by a user interacting with interface 1650. A solar energy simulation may be run each time a parameter is altered, or manually after entering or altering one or more parameters. Panel 1655 may enable a user to create an account with the online solar marketplace in order to save details the solar energy offering, solar energy simulation parameters, and solar energy simulation results for later viewing and action); and
obtaining the one sustainability action plan comprises: identifying one or more engineering workflow systems that correlate with the sustainability model and are associated with the at least one of the one or more sustainability parameters; and generating, via the identified one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model (Levine: Para 0073-0074 via Detailed installation models and planning may be performed on the virtual property models to produce an optimal system design for best solar performance. These may also be made code compliant, as, in an embodiment, the marketplace may be coupled to a building code database and permitting database via a network, such as the Internet. When running a simulation, various factors may be taken into account to determine the best design and offering. As illustrated in FIG. 3, these factors may include, but are not limited to, latitude and atmospheric conditions, roof shadows, slope and orientation, buildable area, setbacks, and obstructions, equipment type, installed cost, efficiency, and warranty, load profiles, utility rates, available incentives, lease terms, loan terms, financing costs, and installer certifications. In one embodiment, the design process may be fully automated, with different equipment and financing options being fit to the 3D property model, or scene, and each option being compared, for example by a genetic algorithm, to determine the best system design is available).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Nielsen/Jones/Kuchuk with the teachings of Levine in order to have obtaining the sustainability model comprises: receiving enterprise data indicative of the state of operations of a portion of the enterprise associated with a geographical region, wherein the state of operations of the portion of the enterprise comprises a listing of the one or more devices and emissions data associated with the one or more devices; and generating the sustainability model based on the enterprise data; and obtaining the one sustainability action plan comprises: identifying one or more engineering workflow systems that correlate with the sustainability model and are associated with the at least one of the one or more sustainability parameters; and generating, via the identified one or more engineering workflow systems, the one sustainability action plan based on an estimated improvement to the at least one of the one or more sustainability parameters based on the sustainability model. The motivations behind this being to incorporate the teachings of carbon emission reduction at targeted properties/regions. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Nielsen/Jones/Kuchuk/Levine, teaches the limitations of Claim 20 which states
wherein different data sources of the enterprise data are associated with different amounts of uncertainty, and wherein the confidence data is based on the different amounts of uncertainty of the different data sources, wherein the different data sources comprise the one or more devices (Nielsen: Para 0028 via The computing device 200 may generate and output multiple recommendations 218 that a user can compare. Each of the recommendations 218 can include indications of an associated impact of each recommended action on the predicted sustainability state 216, an associated uncertainty value for the predicted sustainability state 216 for each recommended action, or a contribution of each parameter of the activity to the predicted sustainability state 216. For example, a recommendation may indicate that the engine contributes the most to the predicted sustainability state 216 and that switching from a first type of engine to a second type of engine for a drilling operation can reduce the emissions by 2%±0.3%).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623