Author: claimsbot

1. A composition comprising:
a phosphoric acid;
ammonium ions; and
a silicon compound,

wherein the silicon compound comprises a silicon atom, an atomic group comprising an amino group combined with the silicon atom, and at least two oxygen atoms combined with the silicon atom.
2. The composition of claim 1, wherein the atomic group is an amino alkyl group or an amino alkoxy group.
3. The composition of claim 2, wherein the atomic group comprises 1 to 10 carbon atoms.
4. The composition of claim 1, wherein the silicon compound is expressed by the following chemical Formula 1:
wherein, R1 is an amino alkyl group or an amino alkoxy group.
5. The composition of claim 1, wherein the silicon compound is expressed by the following chemical Formula 2:
wherein, R2, R3, R4 and R5 are hydrogen, an alkyl group, an amino alkyl group or an amino alkoxy group,
wherein at least one of R2, R3, R4 and R5 is an amino alkyl group or an amino alkoxy group, and wherein, n is 2 or 3.
6. The composition of claim 1, wherein a content of the silicon compound is within a range of about 0.01 wt % to about 15 wt % of the composition.
7. The composition of claim 1, wherein a content of the ammonium ions is within a range of about 0.01 wt % to about 10 wt % of the composition.
8. The composition of claim 1, wherein an etch selectivity of a silicon nitride layer to a silicon oxide layer is greater than 100.
9. The composition of claim 1, wherein the atomic group comprising the amino group stabilizes a bonding structure of the silicon compound.
10. An etchant comprising:
a phosphoric acid;
ammonium ions or a compound comprising ammonium ions; and
a silicon compound selected from the group consisting of amino propyl silanetriol, tri-(methyl, ethylamino-silane)methyl siloxane and tri-(di-ethylamino-silane)amino propyl siloxane.
11. The etchant of claim 10, wherein the etchant comprises the phosphoric acid in an amount up to about 99.8 wt % of the etchant.
12. The etchant of claim 10, wherein the etchant comprises ammonium ions or a compound comprising ammonium ions in an amount in a range from about 0.01 wt % to about 10 wt % of the etchant.
13. The etchant of claim 10, wherein the etchant comprises the silicon compound in an amount in a range from about 0.01 wt % to about 15 wt % of the etchant.
14-20. (canceled)

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A computer-implemented method of normalizing public HMDA data received from a federal agency, the method comprising:
receiving, by a processor, Home Mortgage Disclosure Act (HMDA) data including at least one of HMDA data reports and loan-level public HMDA data, the HMDA data having loan information that varies as a function of time and that is provided by multiple financial institutions;
correcting, by the processor, errors in the HMDA data;
normalizing, by the processor, the HMDA data across variations in the information, wherein normalizing comprises:
accessing a first database storing data indicative of which HMDA data is provided by each of the multiple financial institutions,
accessing a second database storing the names of the multiple financial institutions and associated identifying tags that identify relationships among the multiple financial institutions,
reducing, based on the data stored in the first database and the names of the multiple financial institutions and the associated identifying tags stored in the second database, a total number of the names of the multiple financial institutions, and
reassigning, based on the reduced total number of the names of the multiple financial institutions, the data stored in the first database;

summarizing, by the processor, the normalized HMDA data; and
outputting, by the processor, the summarized and normalized HMDA data to an application for analysis.
2. The method of normalizing HMDA data according to claim 1, wherein correcting errors comprises correcting at least one of a missing census tract number and an improper data code.
3. The method of claim 1, wherein normalizing the HMDA data further comprises:
retrieving geographic reference data related to the HMDA data;
applying rules to the geographic reference data to normalize current census tract data to previous census tract data, current and previous census tract data to counties, and county information to metropolitan statistical area data; and
outputting normalized census tracts to metropolitan statistical area data.
4. The method of claim 3, wherein normalizing the HMDA data further comprises:
retrieving data related to a goal required of a government-sponsored enterprise and the normalized census tracts to metropolitan statistical area data;
applying rules to the retrieved data to determine loans within census tracts that meet the required goal; and
identifying loans that meet the required goal.
5. The method of claim 1, wherein normalizing the HMDA data further comprises:
applying rules to the accessed data indicative of which HMDA data is provided by each of the financial institutions to determine a market share of a first financial institution in a specified year and to include the market share of other financial institutions acquired by the first financial institution after the specified year.
6. The method of claim 1, wherein normalizing the HMDA data further comprises:
applying rules to the HMDA data to identify loans associated with the HMDA data belonging to the sub-prime market and loans belonging to the prime market.
7. The method of claim 1, wherein normalizing the HMDA data further comprises:
applying rules to the HMDA data to identify conforming loans or non-conforming loans.
8. A computer-readable storage medium including program instructions which, when executed by a processor, performs a method of normalizing public Home Mortgage Disclosure Act (HMDA) data received from a federal agency, the method comprising:
receiving HMDA data including at least one of HMDA data reports and loan-level public HMDA data, the HMDA data having loan information that varies as a function of time and that is provided by multiple financial institutions;
correcting errors in the HMDA data;
normalizing the HMDA data across variations in the information, wherein normalizing comprises:
accessing a first database storing data indicative of which HMDA data is provided by each of the multiple financial institutions,
accessing a second database storing names of the multiple financial institutions and associated identifying tags that identify relationships among the multiple financial institutions,
reducing, based on the data stored in the first database and the names of the multiple financial institutions and the associated identifying tags stored in the second database, a total number of the names of the multiple financial institutions, and
reassigning, based on the reduced total number of the names of the multiple financial institutions, the data stored in the first database;

summarizing the normalized HMDA data; and
outputting the summarized and normalized HMDA data to an application for analysis.
9. The storage medium of claim 8, wherein correcting errors comprises correcting at least one of a missing census tract number and an improper data code.
10. The storage medium of claim 8, wherein normalizing the HMDA data further comprises:
retrieving geographic reference data related to the HMDA data;
applying rules to the geographic reference data to normalize current census tract data to previous census tract data, current and previous census tract data to counties, and county information to metropolitan statistical area data; and outputting normalized census tracts to metropolitan statistical area data.
11. The storage medium of claim 10, wherein normalizing the HMDA data further comprises:
retrieving data related to a goal required of a government-sponsored enterprise and the normalized census tracts to metropolitan statistical area data;
applying rules to the retrieved data to determine loans within census tracts that meet the required goal; and
identifying loans that meet the required goal.
12. The storage medium of claim 8, wherein normalizing the HMDA data further comprises:
applying rules to the accessed data indicative of which HMDA data is provided by each of the financial institutions to determine the market share of a first financial institution in a specified year and to include the market share of other financial institutions acquired by the first financial institution after the specified year.
13. The storage medium of claim 8, wherein normalizing the HMDA data further comprises:
applying rules to the HMDA data to identify loans associated with the HMDA data belonging to the sub-prime market and loans belonging to the prime market.
14. The storage medium of claim 8, wherein normalizing the HMDA data further comprises:
applying rules to the HMDA data to identify conforming loans or non-conforming loans.
15. A Home Mortgage Disclosure Act (HMDA) data analysis tool to perform a normalizing process and analysis of public HMDA data, received from a federal agency comprising:
a geographic translator to perform a cross-year process to normalize geographic information on locations of properties reported in the HMDA data provided by multiple financial institutions;
an income translator to perform a cross-year process to normalize HUD goals required of government-sponsored enterprises relative to the HMDA data;
a lender translator for to access a first database storing data indicative of which HDMA data is provided by each of the multiple financial institutions and a second database storing names of the multiple financial institutions and associated identifying tags that identify relationships among the multiple financial institutions, and to perform a cross-year process to normalize lender information reported in the HMDA data by: reducing, based on the data stored in the first database and the names of the multiple financial institutions and the associated identifying tags stored in the second database, a total number of the names of the multiple financial institutions, and reassigning, based on the reduced total number of the names of the multiple financial institutions, the data stored in the first database;
a rules repository for storing rules for performing the normalization process; and
a rules processor for executing the rules.
16. The HMDA data analysis tool of claim 15, wherein the cross-year process performed by the geographic translator further comprises:
determining changes in HMDA reporting standards and formatting of the HMDA data, data definitions, and a time period when a change occurred; and
normalizing the HMDA data based on the determined change and the time period of the change.
17. The HMDA data analysis tool of claim 15, wherein the determined change is a change in census tract data.
18. The HMDA data analysis tool of claim 15, wherein the determined change is a change in a metropolitan statistical area definition.
19. The HMDA data analysis tool of claim 15, wherein the cross-year process performed by the income translator further comprises:
analyzing the HMDA data in relation to the HUD goals;
and flagging loans meeting the goals required of government-sponsored enterprises by the U.S. Department of Housing and Urban Development.
20. The HMDA data analysis tool of claim 15, wherein the cross-year process performed by the lender translator further comprises:
determining a market share of a financial institution for a particular year; and
analyzing the HMDA data in relation to the determined market share.

1. A method comprising:
generating a website network graph to model one or more networks of websites relevant to subject matter of interest in a category, wherein generating the website network graph includes:
performing one or more searches relating to the subject matter of interest in a search engine application programming interface (API) using one or more relevant keywords in combination with the subject matter of interest;
extracting search results from the one or more searches; and

identifying online social media websites with content most relevant to the subject matter of interest based on the website network graph.
2. The method of claim 1, wherein the website network graph is generated to model search behaviors of search engine users to determine online social media websites with content relevant to subject matter of interest.
3. The method of claim 1, wherein the most relevant websites include one or more of:
websites most likely to be reached in online searches for information relating to the subject matter of interest; and
websites where high-affinity social media participants are exchanging opinions and making purchasing decisions; and
4. The method of claim 2, further comprising performing website and link scraping on websites found in search results including:
entering the websites found in the search results;
following links in each of the websites found in the search results to locate additional websites related to the subject matter of interest in the category; and
compiling a list of websites including the websites found in the search results and the additional websites related to the subject matter.
5. The method of claim 4, wherein the website and link scraping further comprises:
following additional links in each of the additional websites related to the subject matter in the category to find further additional websites related to the subject matter of interest; and
adding the further additional websites to the list of websites.
6. The method of claim 5, further comprising performing website network processing on the list of websites including:
determining frequency of occurrence of each website in the list of websites in conjunction with the subject matter of interest in the category;
determining relatedness of each website in the list of websites to the subject matter of interest; and
generating a website network graph to model a website network relating to the subject matter of interest based the frequency of occurrence of each website in the list of websites in conjunction with the subject matter of interest in the category and the relatedness of each website in the list of websites to the subject matter of interest.
7. The method of claim 6, wherein the website network processing further comprises:
counting website links between the websites that contain conversations relevant to the subject matter of interest to obtain an indication of how strongly each of the websites in the list of websites is interconnected; and
applying a betweenness centrality algorithm on the website network graph to obtain centrality values indicating how strongly connected a given website is to other relevant websites in the website network graph.
8. A method of claim 7, further comprising performing website advertisement network processing to obtain a list of most relevant advertisement networks on which to advertise the subject matter of interest including:
utilizing link patterns to identify advertisement networks placing advertisements within the websites in the list of websites;
compiling a list of the advertisement networks; and
storing the list of most relevant advertisement networks.
9. A method for enhancing targeted advertising campaigns comprising:
retrieving a website network graph stored in a database, the website network graph to model one or more networks of websites relevant to a product or service to be advertised;
identifying online social media websites with content most relevant to the product or service to be advertised based on the website network graph; and
enhancing targeted advertising campaigns based on the most relevant websites.
10. The method of claim 9, wherein the most relevant websites include one or more of:
websites most likely to be reached in online searches for information relating to the product or service; and
websites where high-affinity social media participants are exchanging opinions and making purchasing decisions regarding the product or service.
11. The method of claim 10, further comprising identifying most relevant advertisement networks associated with the most relevant websites.
12. An article of manufacture comprising:
a computer-readable storage medium providing instructions which, when executed by a computer, cause the computer to perform a method, the instructions comprising:
instructions to generate a website network graph to model one or more networks of websites relevant to subject matter of interest in a category, wherein generating the website network graph includes:
instructions to perform one or more searches relating to the subject matter of interest in a search engine application programming interface (API) using one or more relevant keywords in combination with the subject matter of interest;
instructions to extract search results from the one or more searches; and

identifying online social media websites with content most relevant to the subject matter of interest based on the website network graph.
13. The article of manufacture of claim 12, wherein the website network graph is generated to model search behaviors of search engine users to determine online social media websites with content relevant to subject matter of interest.
14. The article of manufacture of claim 12, wherein the most relevant websites include one or more of:
websites most likely to be reached in online searches for information relating to the subject matter of interest; and
websites where high-affinity social media participants are exchanging opinions and making purchasing decisions.
15. The article of manufacture of claim 13, further comprising instructions to perform website and link scraping on websites found in search results including:
instructions to enter the websites found in the search results;
instructions to follow links in each of the websites found in the search results to locate additional websites related to the subject matter of interest in the category; and
instructions to compile a list of websites including the websites found in the search results and the additional websites related to the subject matter.
16. The article of manufacture of claim 15, wherein the website and link scraping further comprises:
instructions to follow additional links in each of the additional websites related to the subject matter in the category to find further additional websites related to the subject matter of interest; and
instructions to add the further additional websites to the list of websites.
17. The article of manufacture of claim 16, further comprising instructions to perform website network processing on the list of websites including:
instructions to determine frequency of occurrence of each website in the list of websites in conjunction with the subject matter of interest in the category;
instructions to determine relatedness of each website in the list of websites to the subject matter of interest; and
instructions to generate a website network graph to model a website network relating to the subject matter of interest based the frequency of occurrence of each website in the list of websites in conjunction with the subject matter of interest in the category and the relatedness of each website in the list of websites to the subject matter of interest.
18. The article of manufacture of claim 17, wherein the website network processing further comprises:
instructions to count website links between the websites that contain conversations relevant to the subject matter of interest to obtain an indication of how strongly each of the websites in the list of websites is interconnected; and
instructions to apply a betweenness centrality algorithm on the website network graph to obtain centrality values indicating how strongly connected a given website is to other relevant websites in the website network graph.
19. The article of manufacture of claim 18, further comprising instructions to perform website advertisement network processing to obtain a list of most relevant advertisement networks on which to advertise the subject matter of interest including:
instructions to utilize link patterns to identify advertisement networks placing advertisements within the websites in the list of websites;
instructions to compile a list of the advertisement networks; and
instructions to store the list of most relevant advertisement networks.
20. An article of manufacture comprising:
a computer-readable storage medium providing instructions which, when executed by a computer, cause the computer to perform a method for enhancing targeted advertising campaigns, the instructions comprising:
instructions to retrieve a website network graph stored in a database, the website network graph to model one or more networks of websites relevant to a product or service to be advertised;
instructions to identify online social media websites with content most relevant to the product or service based on the website network graph; and
instructions to enhance targeted advertising campaigns based on the most relevant websites.
21. The article of manufacture of claim 20, wherein the most relevant websites include one or more of:
websites most likely to be reached in online searches for information relating to the product or service; and
websites where high-affinity social media participants are exchanging opinions and making purchasing decisions regarding the product or service.
22. The article of manufacture of claim 21, further comprising identifying most relevant advertisement networks associated with the most relevant websites.
23. An apparatus comprising:
a website network graph processing module configured to generate a website network graph to model one or more networks of websites relevant to subject matter of interest in a category; and
a website graph database to store the website network graph.
24. The apparatus of claim 23, further comprising:
a search queue configured to stage one or more search definitions to perform one or more searches relating to the subject matter of interest in the category;
a search engine application programming interface (API) configured to run one or more searches relating to the subject matter of interest using relevant keywords in combination with the subject matter of interest; and
a website and link scraping module configured to extract search results from the search engine API and compile a list of websites found in the search results.
25. The apparatus of claim 24, wherein the website and link scraping module is further configured to:
locate additional websites related to the subject matter in the category by following links in each of the websites found in the search results; and
add the additional websites to the list of websites found in the search results.
26. The apparatus of claim 25, further comprising memory to store the list of websites.
27. The apparatus of claim 26, wherein the website network graph processing module is configured to:
determine frequency of occurrence of each website in the list of websites in conjunction with the subject matter in the category;
determine relatedness of each website found in the list of websites to the subject matter in the category; and
generate a website network graph to model a website network relating to the subject matter in the category.
28. The apparatus of claim 23, wherein the website network graph processing module is configured to determine most relevant websites based on the website network graph.
29. The apparatus of claim 28, wherein the most relevant websites include websites most likely to be reached when running the one or more searches relating to the subject matter of interest.
30. The apparatus of claim 23, further comprising a website advertisement network processing module configured to generate a website advertisement network graph including a list of one or more most relevant advertisement networks for advertising the subject matter of interest.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A method of deriving information that characterizes value of a hydrocarbon-bearing reservoir fluid model based on downhole fluid sampling and analysis operations, the method comprising:
i) providing or deriving a first fluid model that describes properties of the reservoir fluid as a function of location within the reservoir of interest, wherein the first fluid model is based on downhole fluid sampling and analysis operations;
ii) providing or deriving a second fluid model that describes properties of the reservoir fluid as a function of location within the reservoir of interest, wherein the second fluid model is based on fluid sampling and analysis operations that are different from the downhole fluid sampling and analysis operations from which the first fluid model is based;
iii) using a reservoir simulator configured with the second fluid model of ii) and a set of control variables, to simulate production of reservoir fluids from the reservoir of interest, and calculating at least one net present value of fluid production simulated by the reservoir simulator;
iv) deriving optimal values for the set of control variables of iii) by maximizing a first objective function that is based on at least one net present value of the reservoir fluid production simulated by the reservoir simulator in iii); and
v) using the reservoir simulator configured with the first fluid model and the optimal values of the set of control variables as derived in iv), to simulate production of reservoir fluids from the reservoir of interest, and calculating at least one net present value of reservoir fluid production simulated by the reservoir simulator; and
vi) performing calculations involving a second objective function that is based on at least one net present value of reservoir fluid production simulated by the reservoir simulator in v) in order to derive information that characterizes value of the first fluid model.
2. A method according to claim 1, further comprising:
vii) using the reservoir simulator configured with the first fluid model and the set of control variables of iii), to simulate production of reservoir fluids from the reservoir of interest, and calculating at least one net present value of reservoir fluid production simulated by the reservoir simulator; and
viii) deriving optimal values for the set of control variables of vii) by maximizing a third objective function that is based on at least one net present value of the reservoir fluid production simulated by the reservoir simulator in vii); and
wherein the calculation of vi) involve results of the third objective function of viii).
3. A method according to claim 2, wherein:
the first, second and third objective functions are deterministic in nature and do not take into account uncertainty.
4. A method according to claim 3, wherein:
the first objective function is based on a single net present value of the reservoir fluid production simulated by the reservoir simulator in iii);
the second objective function is based on a single net present value of the reservoir fluid production simulated by the reservoir simulator in v); and
the third objective function is based on a single net present value of the reservoir fluid production simulated by the reservoir simulator in vii).
5. A method according to claim 2, wherein:
the first, second and third objective functions take into account uncertainty.
6. A method according to claim 5, further comprising:
defining a set of uncertainty parameters and corresponding values for use in the reservoir simulations of iii), v) and vii).
7. A method according to claim 6, wherein:
the first objective function is based on a plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in iii) for a number of different combinations of values of the set of uncertainty parameters;
the second objective function is based on a plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in v) for the number of different combinations of values of the set of uncertainty parameters;
the third objective function is based on a plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in vii) for the number of different combinations of values of the set of uncertainty parameters.
8. A method according to claim 7, wherein:
wherein the calculation of vi) involves calculating a figure of merit based on results of the second objective function and results of the third objective function.
9. A method according to claim 7, wherein:
the first objective function is based on statistics of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in iii);
the second objective function is based on statistics of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in v); and
the third objective function is based on statistics of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in vii).
10. A method according to claim 9, wherein:
the statistics of the first objective function are selected from group consisting of the mean and standard deviation of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in iii);
the statistics of the second objective function are selected from group consisting of the mean and standard deviation of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in v); and
the statistics of the third objective function are selected from group consisting of the mean and standard deviation of the plurality of net present values of the reservoir fluid production simulated by the reservoir simulator in vii).
11. A method according to claim 7, wherein:
the first objective function, the second objective function and the third objective function each have multiple instances based on different values of a risk aversion factor.
12. A method according to claim 11, wherein:
the first objective function, the second objective function and the third objective function each have the form
F\u03bb=\u03bc\u03bb\u2212\u03bb\u03c3,
where F\u03bb is the objective function for a specific value of a risk aversion factor \u03bb, and \u03bc\u03bb and \u03c3\u03bb are the mean and standard deviations of the objective function, respectively, for the specific value of the risk aversion factor \u03bb.
13. A method according to claim 1, further comprising:
utilizing a downhole tool to perform downhole fluid sampling and analysis operations at multiple measurements stations within a wellbore that traverses the reservoir of interest in order to derive the first fluid model.
14. A method according to claim 13, further comprising:
performing other fluid sampling operations and fluid analysis operations with respect to reservoir fluid of the reservoir of interest in order to derive the second fluid model, wherein the other fluid sampling operations and fluid analysis operations are different from the downhole fluid sampling and analysis operations from which the first fluid model is derived.
15. A method according to claim 14, wherein:
the other fluid sampling operations are performed at a single measurement station within a wellbore that traverses the reservoir of interest.
16. A method of deriving information that characterizes value of a hydrocarbon-bearing reservoir fluid model based on downhole fluid sampling and analysis operations, the method comprising:
i) providing or deriving a first fluid model that describes properties of the reservoir fluid as a function of location within the reservoir of interest, wherein the first fluid model is based on downhole fluid sampling and analysis operations;
ii) providing or deriving a second fluid model that describes properties of the reservoir fluid as a function of location within the reservoir of interest, wherein the second fluid model is based on fluid sampling and analysis operations that are different from the downhole fluid sampling and analysis operations from which the first fluid model is based;
iii) calculating information that characterizes value of the first fluid model based on the evaluation of a plurality of objective functions;
wherein said plurality of objective functions include a first objective function that is based on at least one net present value of the reservoir fluid production simulated by a reservoir simulator configured with the second fluid model and a set of control variables that are optimized to identify a first group of optimal values of the set of control variables;
wherein said plurality of objective functions further include a second objective function that is based on at least one net present value of the reservoir fluid production simulated by a reservoir simulator configured with the first fluid model and the first group of optimal values of the set of control variables; and
wherein said plurality of objective functions further include a third objective function that is based on at least one net present value of the reservoir fluid production simulated by a reservoir simulator configured with the first fluid model and the set of control variables that are optimized to identify a second group of optimal values of the set of control variables.
17. A method according to claim 15, wherein:
the first, second and third objective functions are deterministic in nature and do not take into account uncertainty.
18. A method according to claim 15, wherein:
the first, second and third objective functions include statistics that account for uncertainty.
19. A method according to claim 18, wherein:
the statistics relate to results of reservoir simulations that employ different combinations of values for a set of uncertainty parameters.
20. A method according to claim 18, wherein:
the calculation of iii) involves calculating a figure of merit based on results of the second objective function and results of the third objective function.
21. A method of visualizing information that characterizes the value of a hydrocarbon-bearing reservoir fluid model data based on downhole fluid sampling and analysis operations, the method comprising:
i) defining a set of uncertainty parameters and associated values;
ii) calculating a first objective function based on net present value of reservoir fluid production simulated by a reservoir simulator configured with a first fluid model derived from downhole fluid sampling and analysis operations, wherein the first objective function is calculated for a number of different combinations of values for the set of uncertainty parameters defined in i);
iii) calculating a second objective function based on net present value of reservoir fluid production simulated by a reservoir simulator configured with data derived from alternative fluid sampling and analysis operations, wherein the second objective function is calculated for the number of different combinations of values for the set of uncertainty parameters defined in i);
iv) for each different combination of values for the set of uncertainty parameters defined in i), calculating a difference between the first objective function as calculated in ii) and the second objective function as calculated in iii); and
v) generating a plot for visualizing the results of the calculating of iv), wherein the plot has four quadrants defined by the intersection of an x-axis and a y-axis that are orthogonal to one another, wherein the x-axis represents the difference between the first and second objective functions as calculated in iv) and the y-axis represents the value of the first objective function in the difference represented by the x-axis.
22. A method according to claim 21, wherein:
the first objective function is based on net present value of reservoir fluid production simulated by the reservoir simulator configured with the first fluid model and a set of control variables that are optimized to derive a first group of optimal values for the set of control variables; and
the second objective function is based on net present value of reservoir fluid production simulated by the reservoir simulator configured with the first fluid model and a second group of optimal values for the set of control variables.
23. A method according to claim 22, wherein:
the second group of optimal values for the set of control variables is derived by evaluating a third objective function, wherein the third objective function is based on net present value of reservoir fluid production simulated by the reservoir simulator configured with a second fluid model derived from the alternative sampling and fluid analysis operations.
24. A method according to claim 22, further comprising:
vi) outputting the plot generated in v) for display or printing.