公开(公告)号：US20170148111A1

公开(公告)日：2017-05-25

申请号：US15335627

申请日：2016-10-27

Applicant: ExxonMobil Research and Engineering Company

Inventor： Myun-Seok Cheon ,  Shivakumar Kameswaran ,  Anantha Sundaram ,  Dimitri J. Papageorgiou

IPC: G06Q50/04 ,  G06Q10/06 ,  G06Q50/18

CPC classification number: G06Q50/04 ,  G06Q10/06315 ,  G06Q50/188 ,  Y02P90/30

Abstract: A raw material valuation tool to assist purchasing decisions in the operation of a facility. The decision support tool allows a user to apply a modeling and analysis framework for a raw material valuation process. This optimization model allows raw material purchasing decisions to be divided into scenarios ahead of time, thereby addressing operational and market uncertainties of events that occur between the initial planning/scheduling and the final arrival of the raw materials at the facility. Price and availability data of a set of raw materials are input into the optimization model, including probability of occurrence of such data. The model calculates an optimal raw material purchasing scenario, which extends up to a moment in time when the raw material is used at the facility. The flexibility of this optimization model increases revenue generated at the facility, decreases cost of the raw material and improves operational decisions.

公开(公告)号：US20200167647A1

公开(公告)日：2020-05-28

申请号：US16662460

申请日：2019-10-24

Applicant: ExxonMobil Research and Engineering Company

Inventor： Stijn De Waele ,  Myun-Seok Cheon ,  Kuang-Hung Liu ,  Shivakumar Kameswaran ,  Francisco Trespalacios ,  Dimitri J. Papageorgiou

IPC: G06N3/08 ,  G06K9/62 ,  G06F17/50 ,  G06F17/18

Abstract: Aspects of the technology described herein comprise a surrogate model for a chemical production process. A surrogate model is a machine learned model that uses a collection of inputs and outputs from a simulation of the chemical production process and/or actual production data as training data. Once trained, the surrogate model can estimate an output of a chemical production process given an input to the process. Surrogate models are not directly constrained by physical conditions in a plant. This can cause them to suggest optimized outputs that the not possible to produce in the real world. It is a significant challenge to train a surrogate model to only produce outputs that are possible. The technology described herein improves upon previous surrogate models by constraining the output of the surrogate model to outputs that are possible in the real world.

公开(公告)号：US20200184401A1

公开(公告)日：2020-06-11

申请号：US16705899

申请日：2019-12-06

Applicant: ExxonMobil Research and Engineering Company

Inventor： Dimitri J. Papageorgiou ,  Francisco Trespalacios ,  Shivakumar Kameswaran ,  Myun-Seok Cheon ,  Timothy A. Barckholtz

IPC: G06Q10/06 ,  G06Q30/02 ,  G06Q10/04 ,  G06Q10/08

Abstract: Aspects of the technology described herein comprise a raw material valuation system that is able to quantify an outcome of various raw material management decisions. Raw material management decisions can include, but are not limited to, purchasing a raw material, selling a raw material, transferring a raw material within a chemical production system, and substituting a proposed purchase of a first raw material with the purchase of a second material. The raw material valuation system can quantify a contemplated changes to a raw material management plan by comparing an optimal reference usage plan to an optimal updated usage plan. The raw material valuation system can calculate a breakeven sale price for a raw material in inventory or a breakeven purchase price for a raw material to be purchased. The raw material valuation system used to generate the reference usage plan and the updated usage plan can use a multi-period optimization model.

公开(公告)号：US20200302293A1

公开(公告)日：2020-09-24

申请号：US16785855

申请日：2020-02-10

Applicant: ExxonMobil Research and Engineering Company

Inventor： Kuang-Hung Liu ,  Michael H. Kovalski ,  Myun-Seok Cheon ,  Xiaohui Wu

IPC: G06N3/08 ,  G05B19/4155

Abstract: An example apparatus for optimizing output of resources from a predefined field can comprise an Artificial Intelligence (AI)-assisted reservoir simulation framework configured to produce a performance profile associated with resources output from the field. The apparatus can further comprise an optimization framework configured for determining one or more financial constraints associated with the field, the optimization framework providing the one or more financial constraints to the AI-assisted reservoir simulation framework, and a deep learning framework configured for training a neural network for use by the optimization framework. The AI-assisted reservoir simulation framework determines, as an output, a plurality of actions for optimizing output of resources from the field.