Landmark Graphics Corporation

Qinghua Wang

Graham Christopher Fleming

James William Watts

Hanife Meftun Erdogan

Dong Li

Dominic Camilleri

Qin Lu

Richard Edward Hinkley

Hui Dong

Kefei Wang

Wei David Liu

Naga Balaji Govindaraju

James William Iii Watts

Hybrid computing that utilizes a computer processor coupled to one or more graphical processing units (GPUs) is configured to perform computations that generate outputs related to reservoir simulations associated with formations that may include natural gas and oil reservoirs.

Reservoir simulation utilizing hybrid computing

An intelligent data management system leverages heterogeneous database technologies and cloud technology to manage data for reservoir simulations across the lifetime of a corresponding energy asset(s) and facilitates access of that data by various consumers despite changing compute platforms and adoption of open source paradigms. The intelligent data management system identifies the various data units that constitute a reservoir simulation output for storage and organization. The intelligent data management system organizes the constituent data units across a file system and object database based on correspondence with different simulation run attributes: project, study, and model. The intelligent data management system also learns to specify or guide configuration of simulation runs.

Smart data warehouse for cloud-based reservoir simulation

A method for determining active constraint equations in a network of wells and surface facilities includes constructing at least one constraint equation for a connection in the network. Each constraint equation includes a respective slack variable and a respective slack variable multiplier. The method further includes constructing a base equation for the connection. The base equation includes the respective slack variable and another respective slack variable multiplier. The method further includes introducing a pseudo slack variable for another connection in the network such that a Schur complement, of a matrix of constraint and base equations dependent only on slack variable multipliers, is sparse. The method further includes solving for each respective slack variable using the Shur complement matrix. The method further includes adjusting a variable parameter of the network using results from solving for each respective slack variable.

Determining active constraints in network using pseudo slack variables

In some embodiments, a system, as well as a method and an article, may operate to generate a first matrix, based on equations that model a reservoir, that includes mass conservation and volume balance information for grid blocks in the reservoir; to generate a second matrix, based on the first matrix, that includes saturation information and pressure information of each grid block; to remove the saturation information from the second matrix to generate a third matrix that includes only pressure information; to solve the third matrix to generate a first pressure solution; to solve the second matrix based on the first pressure solution to generate a first saturation solution and a second pressure solution; and to use the first saturation solution and the second pressure solution to generate a solution of the first matrix. Additional apparatus, systems, and methods are disclosed.

Multi-stage linear solution for implicit reservoir simulation

Oil or gas fields and associated facilities are computer simulated by solving linear algebraic equations using a Schwarz method with at least one well node included in the overlap. According to another aspect, equations for the wellbore nodes are processed to eliminate some non-pressure variables, e.g. component compositions. Other features are also provided.

Oil or gas production using computer simulation of oil or gas fields and production facilities

Simulation system and method for a reservoir represented by a model having a plurality of matrix nodes, a plurality of fracture nodes, a fracture zone and a fracture-free zone are described. One embodiment includes computer-implemented steps of, for each of a plurality of matrix nodes in interconnected zones, characterizing the matrix nodes as dual matrix nodes or non-dual matrix nodes. Dual matrix nodes may be treated as a block and eliminated, after which the coefficient matrix and RHS vector for the model are updated. In-fill patterns between and among fracture nodes and non-dual matrix nodes resulting from the elimination are determined. For non-dual matrix nodes, the nodes are merged to a fracture grid, after which the coefficient matrix and RHS vector for the model are updated accordingly. The resultant linear system is linearly solved, and backsolving is performed for any eliminated dual matrix nodes.

System and method for flexible and efficient simulation of varying fracture density in a reservoir simulator

Systems and methods for flexible block ILU factorization that will support sparse blocks with variable sizes.

Flexible block ILU factorization

Systems and methods for parallel incomplete LU (ILU) factorization in distributed sparse linear systems, which order nodes underlying the equations in the system(s) by dividing nodes into interior nodes and boundary nodes and assigning no more than three codes to distinguish the boundary nodes. Each code determines an ordering of the nodes, which in turn determines the order in which the equations will be factored and the solution performed.

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Katy, TX, United States of America

Qinghua Wang