Exxonmobil Upstream Research Company

Exxonmobil Research and Engineering Company

Effector Therapeutics, Inc.

Vikas Goel

Kevin C Furman

Federico D Carvallo

Bora Tarhan

Yufen Shao

Joshua R Lowry

Samid Ameer Hoda

Anoop A Mullur

Amr S El-Bakry

Kevin R Webster

Jin-Hwa Song

Sangbum Lee

Richard Perry Connell

Nicolas Sawaya

Cassandra M McZeal

James Dreher

Aristides Miroyannis

Cassandra Mczeal

Federico Carvallo

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

The present disclosure relates to the use MNK-specific inhibitors to inhibit immunosuppression components, such as immune checkpoint proteins PD-1, PD-L1, LAG3, and/or immunosuppressive cytokines, such as IL-10, in order to inhibit or release immune suppression in certain diseases, such as cancer and infectious disease.

Inhibitors of immune checkpoint modulators and related methods

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

Systems and methods which implement surrogate (e.g., approximation) models to systematically reduce the parameter space in an optimization problem are shown. In certain embodiments, rigorous (e.g., higher fidelity) models are implemented with respect to the reduced parameter space provided by use of surrogate models to efficiently and more rapidly arrive at an optimized solution. Accordingly, certain embodiments build surrogate models of an actual simulation, and systematically reduce the number of design parameters used in the actual simulation to solve optimization problems using the actual simulation. A multi-stage method that facilitates optimization of decisions related to development planning and reservoir management may be provided. Iterative processing may be implemented with respect to a multi-stage optimization method. There may be uncertainty in various parameters, such as in reservoir parameters, which is taken into account according to certain embodiments.

Systems and methods for reservoir development and management optimization

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 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

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.

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Houston, TX, United States of America

Vikas Goel