Abstract: A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

Abstract: A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

Abstract: Systems and methods are provided for valuation and validation of options and opportunities in planning and operations for a liquefied natural gas project or portfolio of projects. The systems and methods use at least one of a supply chain design model, a shipping simulation model, a ship scheduling model, and an optionality planning model to make valuation and validation decisions.

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.

Abstract: A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

Abstract: A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. The method involves modeling the ship schedule via an LNG ship scheduling model and a LNG ship rescheduling model to provide optimized decisions for the LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships.

Abstract: A system, including: a memory that stores computer executable instructions; and processing circuitry that executes the instructions in order to, generate a graphical user interface, which includes an intelligent Gantt chart that displays a working schedule of a task assigned to a resource, receive a command to change the working schedule, integrate the change into the working schedule, and update and display a performance metric based on the change.

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 nm 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.

Abstract: Systems and methods are provided for valuation and validation of options and opportunities in planning and operations for a liquefied natural gas project or portfolio of projects. The systems and methods use at least one of a supply chain design model, a shipping simulation model, a ship scheduling model, and an optionality planning model to make valuation and validation decisions.

Abstract: A method of simulating shipping of liquefied natural gas (LNG) is provided. An LNG supply chain is modeled with a plurality of decision-making modules. The plurality of decision-making modules are configured to capture behavior of various elements of the LNG supply chain. Data representing a current state of at least a portion of the LNG supply chain is entered into a computer-based simulation system. Optimization techniques are employed with the plurality of decision-making modules to prescribe operations decisions for each element of the LNG supply chain. A simulation of the LNG supply chain is run using the plurality of decision-making modules, the data, and the optimization techniques. An LNG shipping schedule is outputted.

Abstract: A method for generating an LNG supply chain design. An LNG supply chain is modeled using a plurality of optimization models. Input data relevant to the modeled LNG supply chain is accepted. The input data is input into the optimization models. One or more solution algorithms are interfaced with the optimization models. The optimization models are nm using the interfaced solution algorithms to create an optimized supply chain design. The optimized supply chain design is outputted. Uncertainty is accounted for in the input data. The size, number, and design of ships, the number of berths and storage capacity at each LNG regasification terminal and LNG liquefaction terminal, and any other design decisions are treated as variables in the plurality of optimization models.

Abstract: A system and method is provided for generating an optimized ship schedule to deliver liquefied natural gas (LNG) from one or more LNG liquefaction terminals to one or more LNG regasification terminals using a fleet of ships. A plurality of optimization models model an LNG supply chain. The LNG supply chain includes the one or more LNG liquefaction terminals, the one or more LNG regasification terminals, and the fleet of ships. An input device accepts a plurality of inputs relevant to the LNG supply chain. The plurality of inputs are configured to be input into the optimization models. One or more solution algorithms are interfaced with the optimization models. A processor runs the optimization models using the interfaced solution algorithms to create an optimized ship schedule. Uncertainty is accounted for in the optimized ship schedule. An output device outputs the optimized ship schedule.

Abstract: A stochastic programming-based decision support tool for reservoir development planning can comprise a source of input data, an optimization model, a high fidelity model for simulating the reservoir, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The model incorporates the flexibility that a decision-maker has in the real world and allows the decision-maker to adjust the decisions based on new information. The model can systematically address uncertain data, for example comprehensively or even taking all uncertain data into account. Accordingly, the optimization model can provide flexible or robust solutions that remain feasible over an uncertainty space. Once the reservoir model is optimized, final development plans may be generated.

Abstract: A Markov decision process-based support tool for reservoir development planning can comprise a source of input data, an optimization model, a high fidelity model for simulating the reservoir, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The model incorporates the flexibility that a decision-maker has in the real world and allows the decision-maker to adjust the decisions based on new information. The model can systematically address uncertain data, for example comprehensively or even taking all uncertain data into account. Accordingly, the optimization model can provide flexible or robust solutions that remain feasible over an uncertainty space. Once the reservoir model is optimized, final development plans may be generated.

Abstract: A computer modeling application is disclosed for finding the optimal solution to maximize total net margin, for the assignment of vehicles (e.g., especially vessels) in an available fleet to perform a set of voyages to transport cargo comprising one or more bulk products during a planning period, as well as an apparatus and method employing the same. The fleet can include term vehicles and spot vehicles. The vehicles, voyages, and cargos can be heterogeneous. In one embodiment, the vehicles are crude carrier vessels and the bulk products are different grades of crude oil. To increase speed, the model is broken into linear programming and mixed integer (linear) programming problems. The model can be run on a real-time basis to support complex scheduling operations.

Abstract: A robust optimization-based decision support tool for reservoir development planning can comprise a source of input data, an optimization model, a high fidelity model for simulating the reservoir, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The model can systematically address uncertain data, for example, comprehensively or even taking all uncertain data into account. Accordingly, the optimization model can provide flexible or robust optimization solutions that remain feasible over an uncertainty space. Once the reservoir model is optimized, final development plans may be generated.

Abstract: An optimization system for transportation scheduling and inventory management of a bulk product from supply locations to demand locations is provided. The system has a mathematical model containing mathematical programming equations. In one embodiment, the objective function of the mathematical model is to minimize a cost basis of the product transported. The system also has a database system for data input that interfaces with the mathematical model. The last component of the system is a mathematical optimization solver that solves the equations provided by the mathematical model after the mathematical model receives data from the database system. As a result, the optimization system provides optimized or simulated results for the input data.

Abstract: The present invention is a computational system to generate feasible marine transportation schedules given a proposed bulk material supply chain. The decision support tool will fulfill the objectives and scope defined above. The technology employed will require (a) the use of mathematical programming modeling techniques (mixed integer linear programming) to mathematically formulate the business problem, and (b) advanced proprietary solution methods to produce solutions satisfying all constraints of the mathematical model while optimizing an objective function. The decision support tool has the following capabilities: Read input data, Generate feasible bulk material ship Schedule, Evaluate bulk material ship Schedule with User-defined Objective Function, Allow Case comparison, Graphical user interface (GUI) for specifying input data and displaying results.

Abstract: One or more methods for optimizing reservoir development planning include a source of characterized input data, an optimization model, a baseline model for simulating the reservoir, a modified model, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The modified model can systematically address uncertain data, for example comprehensively or even taking all uncertain data into account. Accordingly, the modified model is optimized to flexible or robust solutions. Final reservoir development plans are generated based on optimized model results.

Abstract: A computer modeling application is disclosed for finding the optimal solution to maximize total net margin, for the assignment of vehicles (e.g., especially vessels) in an available fleet to perform a set of voyages to transport cargo comprising one or more bulk products during a planning period, as well as an apparatus and method employing the same. The fleet can include term vehicles and spot vehicles. The vehicles, voyages, and cargos can be heterogeneous. In one embodiment, the vehicles are crude carrier vessels and the bulk products are different grades of crude oil. To increase speed, the model is broken into linear programming and mixed integer (linear) programming problems. The model can be run on a real-time basis to support complex scheduling operations.