US-12619927-B2 - Sentiment analysis for obtaining updated sustainability data for enterprise action plans

US12619927B2US 12619927 B2US12619927 B2US 12619927B2US-12619927-B2

Abstract

An enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system to determine a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of the enterprise. Additionally, the sustainability platform system may determine if changes to the input data are likely to have occurred based on the sentiment, and, if so, trigger a data search for the new input data. The sustainability platform system may also obtain the new input data via the input data sources based on the data search, generate one or more sustainability action plans for improving sustainability parameters of the enterprise based on the new input data, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans.

Inventors

Shashi Menon
Nader Salman
Stephanie Lee
Colin Wier
Neeraj KAMAT
Harshada Modak
Hemant Arora
David Seabrook
Gian-Marcio Gey
Hans Eric Klumpen
Debasish Das
Federico Sporleder
Jing Zhang
Rajarshi Ray

Assignees

SCHLUMBERGER TECHNOLOGY CORPORATION

Dates

Publication Date: 20260505
Application Date: 20240605

Claims (20)

1 . An enterprise computing system comprising: one or more computer devices configured to perform one or more respective operations of an enterprise; and a sustainability platform computing system configured to: determine a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of the enterprise, wherein determining the sentiment is based on a sentiment analysis of a context of the input data of the one or more input data sources, and wherein the sentiment analysis comprises: estimating individual sentiments of a plurality of input data sources regarding the input data from the context; and estimating the sentiment based on a weighted average of the individual sentiments of the plurality of input data sources, wherein different individual sentiments comprise different weightings of the weighted average; determine if changes to the input data are likely to have occurred, defining new input data, based on the sentiment; in response to determining that the changes have occurred, trigger a data search for the new input data; obtain the new input data via the one or more input data sources based on the data search; generate one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the new input data, wherein generating the one or more sustainability action plans comprises: determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the new input data and a sustainability model of the enterprise, wherein the sustainability model is representative of a state of operations of the enterprise; and generating the one or more sustainability action plans based on the at least one abatement technology; generate the sustainability model based on the new input data; simulate an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the new input data to generate one or more simulated sustainability parameters, wherein the one or more sustainability parameters comprise a carbon footprint of the one or more computer devices, a water usage of the one or more computer devices, a waste output of the one or more computer devices, a greenhouse gas emission of the one or more computer devices, or any combination thereof; and in response to determining that the one or more simulated sustainability parameters are within one or more thresholds, send one or more commands to the one or more computer devices to adjust the one or more respective operations according to the one or more sustainability action plans, wherein the one or more respective operations are associated with controlling a flow of hydrocarbons from a subsurface region via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof.
2 . The enterprise computing system of claim 1 , wherein the new input data comprises regulation data that has changed since a previous search of the one or more input data sources, carbon credit data that has changed since the previous search of the one or more input data sources, abatement technology data that has changed since the previous search of the one or more input data sources, or any combination thereof.
3 . The enterprise computing system of claim 1 , wherein the sentiment analysis comprises performing a trend analysis on a frequency of occurrence of one or more key words of the monitoring parameters.
4 . The enterprise computing system of claim 1 , wherein the sustainability platform computing system is configured to obtain the new input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the new input data; scraping the new input data from the one or more input data sources via a large language model machine learning algorithm; or both.
5 . The enterprise computing system of claim 4 , wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof.
6 . The enterprise computing system of claim 1 , wherein the sustainability platform computing system is configured to determine if the changes to the input data have occurred based on a comparison of one or more characteristic values of the sentiment to one or more threshold values.
7 . The enterprise computing system of claim 1 , wherein the sentiment analysis comprises determining an urgent sentiment, an anger sentiment, a happy sentiment, a worrisome sentiment, a neutral sentiment, or any combination thereof of the context of the input data.
8 . A method comprising: determining, via a computing system, a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of an enterprise, wherein determining the sentiment is based on a sentiment analysis of a context of the input data of the one or more input data sources, and wherein the sentiment analysis comprises: estimating individual sentiments of a plurality of input data sources regarding the input data from the context; and estimating the sentiment based on a weighted average of the individual sentiments of the plurality of input data sources, wherein different individual sentiments comprise different weightings of the weighted average; determining, via the computing system, if changes to the input data are likely to have occurred, defining new input data, based on the sentiment; in response to determining that the changes have occurred, triggering, via the computing system, a data search for the new input data; obtaining, via the computing system, the new input data via the one or more input data sources based on the data search; generating, via the computing system, one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the new input data, wherein generating the one or more sustainability action plans comprises: determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the new input data and a sustainability model of the enterprise, wherein the sustainability model is representative of a state of operations of the enterprise; and generating the one or more sustainability action plans based on the at least one abatement technology; generating, via the computing system, the sustainability model based on the new input data; simulating, via the computing system, an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the new input data to generate one or more simulated sustainability parameters, wherein the one or more sustainability parameters comprise a carbon footprint of 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; and in response to determining that the one or more simulated sustainability parameters are within one or more thresholds, sending, via the computing system, one or more commands to the one or more devices of the enterprise to adjust one or more respective operations of the one or more devices according to the one or more sustainability action plans, wherein the one or more respective operations are associated with controlling a flow of hydrocarbons from a subsurface region via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof.
9 . The method of claim 8 , wherein obtaining the new input data from the one or more input data sources comprises: querying one or more databases of the one or more input data sources for the new input data, wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof; scraping the new input data from the one or more input data sources via a large language model machine learning algorithm; or both.
10 . The method of claim 8 , comprising determining, via the computing system, if the changes to the input data have occurred based on a comparison of one or more characteristic values of the sentiment to one or more threshold values.
11 . The method of claim 8 , wherein the new input data comprises regulation data that has changed since a previous search of the one or more input data sources, carbon credit data that has changed since the previous search of the one or more input data sources, abatement technology data that has changed since the previous search of the one or more input data sources, or any combination thereof.
12 . The method of claim 8 , wherein the sentiment analysis comprises performing a trend analysis on a frequency of occurrence of one or more key words of the monitoring parameters.
13 . The method of claim 8 , wherein the sentiment analysis comprises determining an urgent sentiment, an anger sentiment, a happy sentiment, a worrisome sentiment, a neutral sentiment, or any combination thereof of the context of the input data.
14 . A non-transitory, machine-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of an enterprise, wherein determining the sentiment is based on a sentiment analysis of a context of the input data of the one or more input data sources, and wherein the sentiment analysis comprises: estimating individual sentiments of a plurality of input data sources regarding the input data from the context; and estimating the sentiment based on a weighted average of the individual sentiments of the plurality of input data sources, wherein different individual sentiments comprise different weightings of the weighted average; determining if changes to the input data are likely to have occurred, defining new input data, based on the sentiment; in response to determining that the changes have occurred, triggering a data search for the new input data; obtaining the new input data via the one or more input data sources based on the data search; generating one or more sustainability action plans for improving one or more sustainability parameters of the enterprise based on the new input data, wherein generating the one or more sustainability action plans comprises: determining at least one abatement technology estimated to improve the one or more sustainability parameters based on the new input data and a sustainability model of the enterprise, wherein the sustainability model is representative of a state of operations of the enterprise; and generating the one or more sustainability action plans based on the at least one abatement technology: generating the sustainability model based on the new input data; simulating an effect of the one or more sustainability action plans on the one or more sustainability parameters over a period of time based on the new input data to generate one or more simulated sustainability parameters, wherein the one or more sustainability parameters comprise a carbon footprint of 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; and in response to determining that the one or more simulated sustainability parameters are within one or more thresholds, sending one or more commands to the one or more devices of the enterprise to adjust one or more respective operations of the one or more devices according to the one or more sustainability action plans, wherein the one or more respective operations are associated with controlling a flow of hydrocarbons from a subsurface region via one or more pumps, one or more wellheads, one or more artificial lifts, or any combination thereof.
15 . The non-transitory, machine-readable medium of claim 14 , wherein the sentiment analysis comprises performing a trend analysis on a frequency of occurrence of one or more key words of the monitoring parameters.
16 . The non-transitory, machine-readable medium of claim 15 , wherein the new input data comprises regulation data that has changed since a previous search of the one or more input data sources, carbon credit data that has changed since the previous search of the one or more input data sources, abatement technology data that has changed since the previous search of the one or more input data sources, or any combination thereof.
17 . The non-transitory, machine-readable medium of claim 15 , wherein the sentiment analysis comprises determining an urgent sentiment, an anger sentiment, a happy sentiment, a worrisome sentiment, a neutral sentiment, or any combination thereof of the context of the input data.
18 . The non-transitory, machine-readable medium of claim 14 , comprising machine-readable instructions that, when executed by the one or more processors of a machine, cause the machine to obtain the new input data from the one or more input data sources by: querying one or more databases of the one or more input data sources for the new input data; scraping the new input data from the one or more input data sources via a large language model machine learning algorithm; or both.
19 . The non-transitory, machine-readable medium of claim 18 , wherein the one or more input data sources comprise government regulatory websites, social media websites, news publication websites, product catalogs corresponding to the one or more devices, or any combination thereof.
20 . The non-transitory, machine-readable medium of claim 14 , comprising machine-readable instructions that, when executed by the one or more processors of a machine, cause the machine to determine if the changes to the input data have occurred based on a comparison of one or more characteristic values of the sentiment to one or more threshold values.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63/471,174, filed Jun. 5, 2023, and entitled “SUSTAINABILITY PLATFORM FOR IMPROVING SUSTAINABILITY PARAMETERS ACROSS ENTERPRISE OPERATIONS,” which is incorporated herein by reference. Additionally, this application is related to U.S. Ser. No. 18/734,079, filed Jun. 5, 2024, entitled, “UPDATING SUSTAINABILITY ACTION PLANS FOR AN ENTERPRISE BASED ON DETECTED CHANGE IN INPUT DATA”; U.S. Ser. No. 18/734,238, filed Jun. 5, 2024, entitled, “OPTIMIZING SUSTAINABILITY PARAMETERS WITH AN ACTION PLANS FOR AN ENTERPRISE”; U.S. Ser. No. 18/733,939, filed Jun. 5, 2024, entitled, “MANAGING FACILITY AND PRODUCTION OPERATIONS ACROSS ENTERPRISE OPERATIONS TO ACHIEVE SUSTAINABILITY GOALS”; and U.S. Ser. No. 18/733,951, filed Jun. 5, 2024, entitled, “PREDICTING SUSTAINABILITY ACTION PLAN PERFORMANCE OVER TIME,” each of which is incorporated herein by reference. BACKGROUND This disclosure relates generally to providing plans, workflows, and recommendations for improving sustainability parameters across enterprise operations. As hydrocarbons are extracted from hydrocarbon reservoirs via hydrocarbon wells in oil and/or gas fields, the extracted hydrocarbons may be transported to various types of equipment, tanks, processing facilities, and the like via transport vehicles, a network of pipelines, and the like. For example, the hydrocarbons may be extracted from the reservoirs via the hydrocarbon wells and may then be transported, via the network of pipelines, from the wells to various processing stations that may perform various phases of hydrocarbon processing to make the produced hydrocarbons available for use or transport. The transported hydrocarbons may be processed or refined into suitable hydrocarbon products and ultimately distributed to end consumers. Overall, the hydrocarbon enterprise may be characterized as encompassing upstream, midstream, and downstream stages. At each of these stages, sustainability parameters such as energy, carbon, waste, water, and the like may be consumed or used. As enterprises move towards becoming more sustainable organizations, it may be challenging to track sustainability parameters while simultaneously identifying opportunities for improving sustainability parameters associated with the enterprise. This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of this disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. SUMMARY A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. In some embodiments, an enterprise system may include one or more devices that perform respective operations of an enterprise and a sustainability platform system to determine a sentiment regarding input data by monitoring one or more input data sources based on monitoring parameters associated with aspects of sustainability of the enterprise. Additionally, the sustainability platform system may determine if changes to the input data are likely to have occurred based on the sentiment, and, if so, trigger a data search for the new input data. The sustainability platform system may also obtain the new input data via the input data sources based on the data search, generate one or more sustainability action plans for improving sustainability parameters of the enterprise based on the new input data, and send one or more commands to the devices to adjust their respective operations according to the one or more sustainability action plans. Various refinements of the features noted above may be made in relation to various aspects of this disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may be made individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of this disclosure alone or in any combination. The brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of this disclosure without limitation to the claimed subject matter. For clarity and simplicity of description, not all combinations of elements provided in the aspects of the invention recited above hav