# US 11,078,428 B2 · Generating a soft sensor for crude stabilization in the petroleum industry > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,078,428 B2 - Document ID: US11078428B2 - Status: Granted - Kind: B2 - Application number: 15/670,679 - Filed: 2017-08-07 - Published: 2019-02-07 - Granted: 2021-08-03 - Earliest claimed: 2017-08-07 - Assignees: Saudi Arabian Oil Company - Applicants: Saudi Arabian Oil Company - Inventors: Shuyee Lee; Omar Al-Mohisin; Mohammed Al-Saeed - Web page: https://patentgrep.com/patent/US11078428B2 ## CPC - C10G 7/12: Controlling or regulating - C10G 7/02: Stabilising gasoline by removing gases by fractioning - G01K 7/02: using thermoelectric elements, e.g. thermocouples - G01L 19/0092: {Pressure sensor associated with other sensors, e.g. for measuring acceleration or temperature (G01L9/025, G01L9/045, G01L9/065, G01L9/085, G01L9/105, G01L9/125, G01L19/02, G01L19/04 take precedence; measuring two or more variable G01D21/02; temperature sensors with pressure compensation G01K1/26)} - G05B 13/04: involving the use of models or simulators - G05B 19/4148: {characterised by using several processors for different functions, distributed (real-time) systems (G05B19/4141 takes precedence)} - H10N 50/10: Magnetoresistive devices - C01B 17/16: Hydrogen sulfides - G01K 13/026: {of moving liquids} - C10G: CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION (cracking to hydrogen or synthesis gas C01B; cracking or pyrolysis of hydrocarbon gases to individual hydrocarbons or mixtures thereof of definite or specified constitution C07C; cracking to cokes C10B); RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES - G01K: MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR (radiation pyrometry G01J5/00) - G01L: MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE (weighing G01G) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - H10N: ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR - C01B: NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; {METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C} ## Abstract A global theoretical graphical representation of a soft sensor is generated based on a cursory model, where the soft sensor is used to control crude stabilization. A plurality of local real-life graphical representations are generated for the soft sensor, each of the plurality of local real-life graphical representations corresponding to a respective local regime. A global real-life graphical representation is generated for the soft sensor by combining the plurality of local real-life graphical representations. A set of numerical values for the soft sensor are generated based on the global real-life graphical representation. The soft sensor is updated based on lab results and a crude stabilization operation is controlled using the soft sensor. ## Claims ### Claim 1 A computer-implemented method, comprising: generating a global theoretical graphical representation of a soft sensor based on a cursory model; generating a plurality of local real-life graphical representations for the soft sensor based on the global theoretical graphical representation of the soft sensor, each of the plurality of local real-life graphical representations corresponding to a respective local regime; generating a global real-life graphical representation for the soft sensor by combining the plurality of local real-life graphical representations; generating a set of numerical values for the soft sensor based on the global real-life graphical representation; updating the soft sensor based on lab results and the set of numerical values; and using the updated soft sensor to control at least one of a stabilizer bottom temperature or a steam injection ratio in a crude production process. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising: identifying a primary input variable based on sensitivity study of the cursory model. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the local regime is partitioned based on statistical analysis on historical data. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: performing statistical analysis on the lab results before updating the soft sensor based on the lab results. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the soft sensor is a hydrogen sulfide (H2S) sensor. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the soft sensor is a Reid vapor pressure (RVP) sensor. ### Claim 7 A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: generating a global theoretical graphical representation of a soft sensor based on a cursory model; generating a plurality of local real-life graphical representations for the soft sensor based on the global theoretical graphical representation of the soft sensor, each of the plurality of local real-life graphical representations corresponding to a respective local regime; generating a global real-life graphical representation for the soft sensor by combining the plurality of local real-life graphical representations; generating a set of numerical values for the soft sensor based on the global real-life graphical representation; updating the soft sensor based on lab results and the set of numerical values; and using the updated soft sensor to control at least one of a stabilizer bottom temperature or a steam injection ratio in a crude production process. ### Claim 8 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations comprising: identifying a primary input variable based on sensitivity study of the cursory model. ### Claim 9 (depends on 7) The non-transitory, computer-readable medium of claim 7, wherein the local regime is partitioned based on statistical analysis on historical data. ### Claim 10 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations comprising: performing statistical analysis on the lab results before updating the soft sensor based on the lab results. ### Claim 11 (depends on 7) The non-transitory, computer-readable medium of claim 7, wherein the soft sensor is a hydrogen sulfide (H2S) sensor. ### Claim 12 (depends on 7) The non-transitory, computer-readable medium of claim 7, wherein the soft sensor is a Reid vapor pressure (RVP) sensor. ### Claim 13 A computer-implemented system, comprising: a computer memory; and a hardware processor interoperably coupled with the computer memory and configured to perform operations comprising: generating a global theoretical graphical representation of a soft sensor based on a cursory model; generating a plurality of local real-life graphical representations for the soft sensor based on the global theoretical graphical representation of the soft sensor, each of the plurality of local real-life graphical representations corresponding to a respective local regime; generating a global real-life graphical representation for the soft sensor by combining the plurality of local real-life graphical representations; generating a set of numerical values for the soft sensor based on the global real-life graphical representation; updating the soft sensor based on lab results and the set of numerical values; and using the updated soft sensor to control at least one of a stabilizer bottom temperature or a steam injection ratio in a crude production process. ### Claim 14 (depends on 13) The computer-implemented system of claim 13, the operations comprising: identifying a primary input variable based on sensitivity study of the cursory model. ### Claim 15 (depends on 13) The computer-implemented system of claim 13, wherein the local regime is partitioned based on statistical analysis on historical data. ### Claim 16 (depends on 13) The computer-implemented system of claim 13, the operations comprising: performing statistical analysis on the lab results before updating the soft sensor based on the lab results. ### Claim 17 (depends on 13) The computer-implemented system of claim 13, wherein the soft sensor is a hydrogen sulfide (H2S) sensor.