International Business Machines Corporation

Shell Oil Comapny

Andrew R Conn

Lior Horesh

Theodore Gerard Van Kessel

Matthias Kormaksson

Moshood Omolade Saliu

Ulisses Mello

Chandramouli Visweswariah

Gijs Van Essen

Rudolf Adriaan Haring

Eduardo Antonio Jimenez Arismendi

Sippe Douma

Eduardo Antonio Arismendi Jimenez

Gijs Michiel Van Essen

Sippe Geert Douma

A method, system and computer program product are disclosed for integrating plural modalities of information to obtain values for a specified attribute of a given system. In one embodiment, the method comprises acquiring data of a first modality, conveying a first source of data of a first type of the system; configuring simulator with settings of physical sensors; acquiring data of a second modality from the system, conveying a second source of data of a second type of the system. The method further comprises converting the data of the second modality to data of the first type, while configuring a virtual set of sensors to enable acquisition of the converted data of the second modality; and configuring adjoints equipped simulator with settings of the virtual sensors, to mimic collection of data of the first type, while configured to measure data of second type.

Virtual sensing for adjoint based incorporation of supplementary data sources

One aspect includes a method for use with a plurality of steam assisted gravity drainage wells. The method includes designing a first modeling framework which provides a model of total output for the plurality of wells from one or more input variables for respective ones of the plurality of wells. The method also includes designing a second modeling framework which provides a model of output for a given one of the plurality of wells based at least in part on one or more input variables for the given one of the plurality of wells and one or more input variables for at least another one of the plurality of wells. The method further includes utilizing the first and second modeling frameworks to determine an optimal configuration for the wells and configuring the well to operate with the determined optimal configuration.

Cross-well allocation optimization in steam assisted gravity drainage wells

One aspect includes a method for use with a steam assisted gravity drainage well. The method includes generating an analytical framework for the well at least in part by modeling one or more observable variables as a function at least of one or more control variables; and modeling one or more output variables as a function at least of at least one of the one or more control variables; and at least one of the one or more observable variables. The method further includes utilizing the analytical framework to determine an optimal configuration for the well, the optimal configuration including a set of one or more values for the one or more control variables which produce one or more desired values for the one or more output variables; and configuring the well to operate with the determined optimal configuration.

Multi-level modeling of steam assisted gravity drainage wells

A method, system and computer program product for integrating plural modalities of information to obtain values for a specified attribute of a given system. In one embodiment, data of a first modality, conveying a first source of data of a first type of the system is acquired; a simulator is configured with settings of physical sensors; and data of a second modality from the system, conveying a second source of data of a second type of the system is acquired. The data of the second modality is converted to data of the first type, while configuring a virtual set of sensors to enable acquisition of the converted data of the second modality; and an adjoints equipped simulator is configured with settings of the virtual sensors, to mimic collection of data of the first type, while configured to measure data of the second type.

Virtual sensing for adjoint based incorporation of supplementary data sources in inversion

A method for controlling operation of a steam-assisted gravity drainage (SAGD) oil well system to increase efficiency in emulsion production includes obtaining a predictive model of the SAGD oil well system. The predictive model has control parameters including one or more continuous control parameters and one or more discrete control parameters. The method also includes utilizing the predictive model to determine values for the control parameters that meet one or more objectives for the SAGD oil well system subject to one or more constraints based on a sequence of iterations of solving a mixed integer non-linear problem. The method further includes adjusting controls of the SAGD oil well system based on the determined values of the control parameters.

Controlling operation of a steam-assisted gravity drainage oil well system utilizing continuous and discrete control parameters

A method for increasing efficiency in emulsion production for a steam-assisted gravity drainage (SAGD) oil well system includes generating a causal model of the SAGD oil well system and training the causal model of the SAGD oil well system utilizing historical time series data relating to one or more SAGD oil wells at one or more SAGD production sites of the SAGD oil well system. The historical time series data is obtained from a plurality of sensors in the SAGD oil well system. The method also includes utilizing the causal model to determine a forecast emulsion production and a forecast set of control parameters associated with one or more of the SAGD production sites of the SAGD oil well system. The method further includes adjusting a set of controls of the SAGD oil well system based on the forecast emulsion production and the forecast set of control parameters and subject to one or more constraints associated with the SAGD oil well system.

Controlling operation of a steam-assisted gravity drainage oil well system by adjusting controls based on forecast emulsion production

A method for increasing efficiency in emulsion production for a steam-assisted gravity drainage (SAGD) oil well system includes generating a multiple time step model of the SAGD oil well system and training the multiple time step model of the SAGD oil well system utilizing historical time series data relating to one or more SAGD oil wells at one or more SAGD production sites of the SAGD oil well system. The historical time series data is obtained from a plurality of sensors in the SAGD oil well system. The method also includes utilizing the multiple time step model to determine, based on a set of objectives and subject to one or more constraints, two or more sets of values for control parameters associated with one or more of the SAGD production sites of the SAGD oil well system for respective ones of two or more time steps. The method further includes adjusting, in each of the two or more time steps, a set of controls of the SAGD oil well system based on the determined values of the control parameters for the corresponding time step.

Controlling operation of a stem-assisted gravity drainage oil well system by adjusting multiple time step controls

A method for increasing efficiency in emulsion production for a steam-assisted gravity drainage (SAGD) oil well system includes generating a model of the SAGD oil well system and training the model of the SAGD oil well system utilizing historical time series data relating to one or more SAGD oil wells at one or more SAGD production sites of the SAGD oil well system. The historical time series data is obtained from a plurality of sensors in the SAGD oil well system. The method also includes determining an uncertainty of the model as a function of a control space of the model and utilizing the model to determine values for control variables associated with one or more of the SAGD production sites of the SAGD oil well system which reduce the model uncertainty while meeting one or more objectives subject to one or more constraints. The method further includes adjusting a set of controls of the SAGD oil well system based on the determined values for the control variables.

Controlling operation of a steam-assisted gravity drainage oil well system by adjusting controls to reduce model uncertainty

A system, method and a computer program product may be provided for characterizing natural resource subsurface attributes and compositions. The system prescribes alterations of one or more controls of a natural resource. The system applies the altered controls to the natural resource wells. The system collects measurement data of the natural resource wells that responds to the applied altered controls. The system determines, based on the collected measurement data, the natural resource subsurface attributes and compositions that pertain to the natural resource.

Induced control excitation for enhanced reservoir flow characterization

In one embodiment, a technique is provided to cluster model prior realizations into flow-equivalent sets. Thereby, far fewer representatives are identified from each cluster that can still predict the same range of future production reliably. Only representatives of each realization cluster that indicate unique forecasts will undergo the computationally expensive process of history matching.

Reduced space clustering representatives and its application to long term prediction

A method of pruning an initial timing graph for static optimization of a digital circuit includes examining a first group of nodes, which in turn include at least a first one of the nodes in the initial timing graph, to determine whether every node in the first group is prunable. The method further includes pruning the first group by pruning every node therein, if every node therein is prunable, and if the pruning would be beneficial. Furthermore, the method includes repeating the examining and pruning steps for a substantial number of additional groups of nodes, so as to create a pruned timing graph having enhanced numerical qualities and/or compactness compared to an initial timing graph. The pruning can be conducted on groups of more than one node at a time, or on only individual nodes at one time. Reduced size, and the benefits thereof, can be determined by a comparison of the number of nodes and number of directed edges before pruning, with the number of nodes and the number of directed edges after pruning, by summing the quantities before pruning and comparing the sum to the summed quantities after pruning, with or without weighting. Preferably, multistep pruning can be employed to first perform all pruning operations with a gain of a certain value, and to continue for gains of lower values. In an alternative method, an explicit representation of the problem as a timing graph is not employed, but the circuit optimization problem is restated in an analogous manner by pruning arrival times and manipulating constraints.

Method of reformulating static circuit optimization problems for reduced size, degeneracy and redundancy

A method of incorporating noise considerations during circuit optimization includes the steps of: specifying a circuit schematic to be optimized; specifying at least one noise criterion as a noise measurement, including the signal to be checked for noise, the sub-interval of time of interest, and the maximum allowable noise deviation; providing each noise criterion as either a semi-infinite constraint or a semi-infinite objective function; specifying at least one variable of the optimization; converting the semi-infinite noise constraints and the semi-infinite noise objective functions into time-integral equality constraints; optionally, if required, providing additional optimization criteria other than noise as, for each such criterion, either objective functions or constraints; creating a merit function to be minimized to solve the optimization problem; simulating the circuit in the time-domain; computing the values of the objective functions and constraints; efficiently computing the gradients of the merit function of the optimizer (including contributions of all objective functions and constraints and the time-integrals representing noise considerations) preferably by means of a single adjoint analysis; iteratively providing the constraint values, the objective function values and the gradients of the merit function to a nonlinear optimizer; and continuing the optimization iterations to convergence.

Method for incorporating noise considerations in automatic circuit

A method of efficient computation of gradients of a merit function of a system includes the steps of: specifying at least one parameter for which the gradients with respect to the at least one parameter are desired; specifying the merit function of interest in terms of observable measurements of the system; either solving or simulating the system to determine values of the measurements; expressing the gradients of the merit function as the gradient of a weighted sum of measurements; forming an appropriately configured adjoint system; and either solving or simulating the adjoint system to simultaneously determine the gradients of the merit function with respect to the at least one parameter by employing a single adjoint analysis. Preferably, the system may be modeled by a set of equations comprising at least one of the following: a nonlinear set of equations, a linear set of equations, a set of linear partial differential equations, a set of nonlinear partial differential equations, a set of linear differential algebraic equations or a set of nonlinear differential algebraic equations. Further, the system of interest may be a network and, preferably, may be an electrical circuit. Still further, elements of the adjoint network and excitations of the adjoint network are determined in order to obtain the gradients of the merit function by employing a single adjoint analysis. It is to be appreciated that, in a preferred embodiment, the gradients of merit function are computed for the purpose of optimization and the merit function may be either a Lagrangian merit function or an augmented Lagrangian merit function.

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Mount Vernon, NY, United States of America

Andrew R Conn