Exxonmobil Upstream Research Company

Exxonmobil Research and Engineering Company

Kevin C Furman

Vikas Goel

Gary Robert Kocis

Philip H Warrick

Jin-Hwa Song

Bora Tarhan

Joshua R Lowry

Yufen Shao

Mark E Osmer

Thomas A Wheaton

Michael K McDonald

Samid Ameer Hoda

Amr S El-Bakry

Federico D Carvallo

Sangbum Lee

James Dreher

Aristides Miroyannis

Nicolas Sawaya

Richard Perry Connell

Chad D Reimann

Marco A Duran

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.

Method of generating an optimized ship schedule to deliver liquefied natural gas

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.

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

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.

Markov decision process-based support tool for reservoir development planning

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.

Stochastic programming-based decision support tool for reservoir development planning

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.

System for optimizing transportation scheduling

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.

Robust optimization-based decision support tool for reservoir development planning

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.

System for optimizing transportation scheduling and inventory management of bulk product from supply locations to demand locations

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.

Bulk material ship routing and inventory management schedule optimization

A computer application loaded on a computer readable medium, a computer apparatus comprising the same, and process employing the same, is described herein. The computer application, when executed, causes a computer to optimize, for maximum net profit margin, the product allocation, transportation routing, transportation vehicle/route scheduling and, optionally, blending, of bulk products that are produced by and loaded from supply locations and delivered to and consumed by demand locations, using a heterogeneous fleet of transportation vehicles over a pre-defined period of time.

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Houston, TX, United States of America

Kevin C Furman