A method and device for predicting a gas composition, including pre-processing, by non-negative matrix factorization, a set of input parameters related to a fluid mixture of hydrocarbons and non-hydrocarbons fed into a multistage separator, and training an extreme learning machine model to predict the composition of non-hydrocarbons in the fluid mixture.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting a gas composition, comprising: (a) receiving a set of input parameters related to a fluid mixture of hydrocarbons and non-hydrocarbons fed into a multistage separator, wherein: the input parameters comprise at least one member selected from the group consisting of a reservoir temperature, a reservoir pressure, a reservoir gas composition, a separator stage temperature and a separator stage pressure, and the non-hydrocarbons comprise at least one member selected from the group consisting of N2, CO2 and H2S; (b) pre-processing the set of input parameters by non-negative matrix factorization, with a processor, to obtain a reduced feature set; (c) providing a training dataset comprising the reduced feature set; (d) randomly selecting a first set percentage of the training dataset; (e) training an extreme learning machine model with the selected first set percentage of the training dataset, with a processor; (f) predicting a mole percentage of the non-hydrocarbons in the fluid mixture; (g) comparing the predicted mole percentage with the set of input parameters, and selecting a second set percentage of badly predicted training datasets based upon a pre-set threshold error value; and (h) repeating (b) through (g) one or more times on the second set percentage of badly predicted training datasets, using one or more factorization levels in the non-negative matrix factorization. 2. The method of claim 1, wherein the input parameters comprise the reservoir temperature. 3. The method of claim 2, wherein the reservoir temperature is 100° F. to 400° F. 4. The method of claim 1, wherein the input parameters comprise the reservoir pressure. 5. The method of claim 4, wherein the reservoir pressure is 500 to 6000 psi. 6. The method of claim 1, wherein the input parameters comprise the separator stage temperature. 7. The method of claim 6, wherein the separator stage temperature is a temperature of a first stage of the multistage separator, and is 75° F. to 225° F. 8. The method of claim 1, wherein the input parameters comprise the separator stage pressure. 9. The method of claim 8, wherein the separator stage pressure is a pressure of a first stage of the multistage separator, and is 50 to 300 psi. 10. A gas composition predicting device, comprising: an interface; and circuitry configured to (a) receive a set of input parameters related to a fluid mixture of hydrocarbons and non-hydrocarbons fed into a multistage separator via the interface, wherein: the input parameters comprise at least one member selected from the group consisting of a reservoir temperature, a reservoir pressure, a reservoir gas composition, a separator stage temperature and a separator stage pressure, and the non-hydrocarbons comprise at least one member selected from the group consisting of N2, CO2 and H2S; (b) pre-process the set of input parameters by non-negative matrix factorization, with a processor, to obtain a reduced feature set; (c) provide a training dataset comprising the reduced feature set; (d) randomly select a first set percentage of the training dataset; (e) train an extreme learning machine model with the selected first set percentage of the training dataset, with a processor; (f) predict a mole percentage of the non-hydrocarbons in the fluid mixture; (g) compare the predicted mole percentage with the set of input parameters, and select a second set percentage of badly predicted training datasets based upon a pre-set threshold error value; and (h) repeat (b) through (g) one or more times on the second set percentage of badly predicted training datasets, using one or more factorization levels in the non-negative matrix factorization. 11. The device of claim 10, wherein the input parameters comprise the reservoir temperature. 12. The device of claim 11, wherein the reservoir temperature is 100° F. to 400° F. 13. The device of claim 10, wherein the input parameters comprise the reservoir pressure. 14. The device of claim 13, wherein the reservoir pressure is 500 to 6000 psi. 15. The device of claim 10, wherein the input parameters comprise the separator stage temperature. 16. The device of claim 15, wherein the separator stage temperature is a temperature of a first stage of the multistage separator, and is 75° F. to 225° F. 17. The device of claim 10, wherein the input parameters comprise the separator stage pressure. 18. The device of claim 17, wherein the separator stage pressure is a pressure of a first stage of the multistage separator, and is 50 to 300 psi.