Method for developing a long-term strategy for allocating a supply of liquefied natural gas

• US 10,013,663 B2
• Filed: 11/15/2012
• Issued: 07/03/2018
• Est. Priority Date: 12/09/2011
• Status: Active Grant

• Current Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Original Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Inventors: Furman, Kevin C., Goel, Vikas, Hoda, Samid A., Sawaya, Nicolas
• Primary Examiner(s): Gart, Matthew S
• Assistant Examiner(s): Swartz, Stephen S
• Application Number:
  US14/358,668
• Publication Number:
  US 20150012326A1
• Time in Patent Office: 2,056 Days
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A method is disclosed for developing a long-term strategy for allocating a supply of liquefied natural gas (LNG) while adhering to limitations of available shipping capacity An LNG market is modeled using one or more optimization models. The LNG market includes at least one buyer of LNG, at least one seller of LNG, and at least one means of transporting LNG. A plurality of inputs relevant to the LNG market are accepted. The inputs are configured to be input into the optimization models. One or more solution algorithms are interfaced with the optimization models. The optimization models are run using the interfaced solution algorithms to identify potential options in the LNG market. Uncertainty is accounted for in the identified potential options. The identified potential options are outputted.

Claims

1. A method for shipping liquefied natural gas (LNG) comprising:

developing a long-term strategy for allocating a supply of LNG, wherein the strategy identifies potential options in the LNG market while adhering to limitations of available shipping capacity, wherein identifying the potential options comprises;

modeling an LNG market using one or more optimization models in a computer system, wherein the LNG market includes at least one buyer of LNG, at least one seller of LNG, an LNG terminal, and at least one ship for transporting LNG, wherein the one or more optimization models comprises a stochastic programming model, a stochastic dynamic program, a robust optimization model, a mixed integer linear programming model, a dynamic programming model, an approximate dynamic programming model, a constraint programing model, or a combination thereof, and wherein the one or more optimization models incorporate a network flow model and uses discrete variables to represent fixed penalties, costs and incentives on various options, batch cargo movements, limits on options, and IF-THEN logic on option constraints;

inputting a plurality of inputs into the one or more optimization models, wherein uncertainty in the one or more inputs is represented as one or more of multiple scenarios, probability distribution functions, ranges of values, and a discrete set of values, wherein the uncertainty in the plurality of inputs includes one or more of shipping capacity on particular routes and the number, size, and speed of ships traveling on a particular route, and wherein the plurality of inputs comprise at least one of ship routes between all supply and destination terminals and types or grades of available LNG;

interfacing one or more solution algorithms with the one or more optimization models in the computer system; and

running in the computer system the one or more optimization models using the interfaced one or more solution algorithms to identify potential options in the LNG market, wherein uncertainty is accounted for in the identified potential options, and wherein the identified potential options include limits on potential deals, including one or more of maximum number of parties on one side of a deal, maximum number of parties per deal, maximum number of sets of parties dealing, and disallowed deals;

outputting the identified potential options; and

shipping LNG according to one or more of the identified potential options.

2. The method of claim 1, wherein the plurality of inputs comprise at least one of projects and parties to consider,

percent ownership of each party in each project,

a fraction of the supply committed at a location,

projected production rates at each LNG terminal for each supplier, and

local and regional gas supply and demand.

3. The method of claim 1, wherein the plurality of inputs comprise at least one of shipping capacity constraints,

constraints of

each project based on number, class, size, fuel type, and speed range of ships,

whether ships are dedicated or pooled, and

whether ships are owned, long-term chartered, spot, or short-term chartered.

4. The method of claim 1, wherein the plurality of inputs comprise at least one market for LNG ship outchartering.

5. The method of claim 1, wherein the plurality of inputs comprise at least one market for LNG ship inchartering.

6. The method of claim 1, wherein the plurality of inputs comprise at least one of contractual demand at each destination terminal for each consumer required from each particular supplier, known and assumed fiscal terms for each project'"'"'s contract, sale price structure, flexibility of the contracts with regard to options such as the ability to incharter or outcharter ships, divert cargos, or buy or sell to spot markets, and time horizon.

7. The method of claim 1, wherein the plurality of inputs comprise at least one of cost of service estimates for each shipping route, and LNG price range projections at each market locale over a time horizon.

8. The method of claim 1, further comprising determining a next best alternative strategy available to at least one side of an LNG purchase negotiation.

9. The method of claim 1, further comprising analyzing flexibility and optionality in product routing.

10. The method of claim 1, wherein the one or more optimization models are run to determine improved or optimized profitability from a perspective of one or more parties in a transaction.

11. The method of claim 1, further comprising identifying sub-optimal potential options in the LNG market.

12. The method of claim 1, wherein the identified potential options are evaluated over a time horizon.

13. The method of claim 12, wherein the time horizon is a single time period snapshot.

14. The method of claim 13, wherein the time horizon is a multi-period time horizon.

15. The method of claim 1, wherein the identified potential options include one or more of diversions, swaps, backhauls, ship outcharter, ship incharter, and co-loading of ships.

16. The method of claim 1, wherein identifying potential options includes a sensitivity analysis to a data set.

17. The method of claim 1, wherein uncertainty in the plurality of inputs includes one or more of natural gas prices, shipping cost of service, fuel cost, and market supply and demand scenarios.

18. A computer program product having computer executable logic recorded on a tangible, non-transitory machine-readable medium, comprising:

code for developing a long-term strategy for allocating a supply of liquefied natural gas (LNG) by identifying potential options in the LNG market while adhering to limitations of available shipping capacity, wherein the code for identifying the potential options comprises;

code for modeling an LNG market using one or more optimization models, wherein the LNG market includes at least one buyer of LNG, at least one seller of LNG, an LNG terminal, and at least one means of transporting LNG comprising one or more ships, wherein the one or more optimization models is one of a stochastic programming model, a stochastic dynamic program, and a robust optimization model, and wherein the one or more optimization models incorporate a network flow model and uses discrete variables to represent fixed penalties, costs and incentives on various options, batch cargo movements, limits on options, and IF-THEN logic on option constraints;

code for accepting a plurality of inputs relevant to the LNG market, wherein the plurality of inputs are configured to be input into the one or more optimization models, wherein uncertainty in the one or more inputs is represented as one or more of multiple scenarios, probability distribution functions, ranges of values, and a discrete set of values, and wherein the uncertainty in the plurality of inputs includes one or more of shipping capacity on particular routes and the number, size, and speed of ships traveling on a particular route, and wherein the plurality of inputs comprise at least one of ship routes between all supply and destination terminals and types or grades of available LNG;

code for interfacing one or more solution algorithms with the one or more optimization models;

code for running the one or more optimization models using the interfaced one or more solution algorithms to identify potential options in the LNG market, wherein uncertainty is accounted for in the identified potential options, and wherein the identified potential options include limits on potential deals, including one or more of maximum number of parties on one side of a deal, maximum number of parties per deal, maximum number of sets of parties dealing, and disallowed deals; and

code for outputting the identified potential options.