Exxonmobil Upstream Research Company

Exxonmobil Technology and Engineering Company

Yahan Yang

Xiaohui Wu

Rossen Parashkevov

Dachang Li

Xiao-Hui Wu

Adam Usadi

Sergey A Terekhov

Jing Wan

Linfeng Bi

Richard Thomas Mifflin

Weidong Guo

Qian-Yong Chen

Richard T Mifflin

Adam K Usadi

A semi-elimination methodology for simulating high flow features in a reservoir and wells is disclosed. The reservoir and wells may be divided into a plurality of cells, including small cells in wells and the reservoir and bulk cells in the bulk of the reservoir, where the small cells are smaller (e.g., by pore volume) than the bulk cells. Processing of all of the cells, including all of the small cells, may be too computationally expensive, particularly when processing is iterative. In that regard, at least some of the small cells are partly processed in an iteration, such as for flow rates, compositions, or flow derivatives. After which, some or all of the small cells are eliminated from further processing in the iteration. In that way, high flow features in a reservoir and wells may be simulated effectively.

Semi-elimination methodology for simulating high flow features in a reservoir

A method and system are described to form a subsurface model for use in hydrocarbon operations. The method and system utilize stability proxies with the subsurface models, such as simulation models, and to manage the reservoir simulation.

Method and system for stable and efficient reservoir simulation using stability proxies

There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model comprising a plurality of sub regions. At least one of the sub regions is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region. A machine learning algorithm is used to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media. The hydrocarbon reservoir can be simulated using the inverse operator approximated for the at least one sub region. The method also includes generating a data representation of a physical hydrocarbon reservoir can be generated in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.

Methods and systems for machine-learning based simulation of flow

There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of sub regions. A solution surrogate is obtained for a sub region by searching a database of existing solution surrogates to obtain an approximate solution surrogate based on a comparison of physical, geometrical, or numerical parameters of the sub region with physical, geometrical, or numerical parameters associated with the existing surrogate solutions in the database. If an approximate solution surrogate does not exist in the database, the sub region is simulated using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the sub region. A machine learning algorithm is used to obtain a new solution surrogate based on the set of training parameters. The hydrocarbon reservoir can be simulated using the solution surrogate obtained for the at least one sub region.

Methods and systems for machine&#x2014;learning based simulation of flow

A method is presented for modeling reservoir properties. The method includes an auxiliary time-stepping procedure of the reservoir between an old time and a new time, and calculating a plurality of masses explicitly. A plurality of phase component densities is updated linearly from the plurality of masses. A plurality of saturation changes is calculated based on the plurality of masses. A plurality of phase flow rates is updated based on the plurality of saturation changes, a plurality of phase flow rates at the old time, and a plurality of saturation derivatives of the phase flow rates at the old time. A plurality of component flow rates may be calculated based on the updated plurality of phase component densities and the plurality of phase flow rates. The method also includes a formulation method based on the auxiliary time stepping procedure.

Method and system for stabilizing formulation methods

A variable discretization method for general multiphase flow simulation in a producing hydrocarbon reservoir. For subsurface regions for which a regular or Voronoi computational mesh is suitable, a finite difference/finite volume method ('FDM') is used to discretize numerical solution of the differential equations governing fluid flow (). For subsurface regions with more complex geometries, a finite element method ('FEM') is used. The invention combines FDM and FEM in a single computational framework (). Mathematical coupling at interfaces between different discretization regions is accomplished by decomposing individual phase velocity into an averaged component and a correction term. The averaged velocity component may be determined from pressure and averaged capillary pressure and other properties based on the discretization method employed, while the velocity correction term may be computed using a multipoint flux approximation type method, which may be reduced to two-point flux approximation for simple grid and permeability fields.

Variable discretization method for flow simulation on complex geological models

There is provided a method for modeling a hydrocarbon reservoir that includes generating a reservoir model that has a plurality of coarse grid cells. A plurality of fine grid models is generated, wherein each fine grid model corresponds to one of the plurality of coarse grid cells that surround a flux interface. The method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation.

A method is presented for modeling reservoir properties. The method includes constructing a coarse computational mesh for the reservoir. The coarse computational mesh comprises a plurality of cells. The method further includes determining a plurality of flows for each of the plurality of cells based on Dirichlet boundary conditions. Additionally, the method includes determining a solution to a coarse pressure equation for the reservoir based on the plurality of flows.

Method and system for reservoir modeling

Methods and systems to reduce or eliminate numerical oscillations in solutions that occur when using conventional MPFA when modeling flow in a reservoir are provided. The technique may be referred to as enriched multi-point flux approximation (EMPFA) and may be used to improve the consistency and accuracy in constructing pressure interpolations in cells for the purpose of determining flux equations used in predicting flow in a reservoir.

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Pearland, TX, United States of America

Yahan Yang