Schlumberger Technology Corporation

Services Petroliers Schlumberger

Schlumberger Holdings Limited

David Rowan

Mark Wakefield

Simon D Bulman

Tom Jonsthovel

Kieran Neylon

Bulent Balci

Omer Gurpinar

Rajarshi Banerjee

Gino Camillus Thielens

Ian Pallister

Paul A Fjerstad

A method includes training a proxy model to predict output from a reservoir model of a subterranean volume, receiving data representing an oilfield operation performed at least partially in the subterranean volume, predicting one or more performance indicators for the oilfield operation using the proxy model, and updating the reservoir model based at least in part on the one or more performance indicators predicted in the proxy model.

Machine learning proxy model for parameter tuning in oilfield production operations

A method for performing a field operation of a field. The method includes obtaining historical parameter values of a runtime parameter and historical core datasets, where the historical parameter values and the historical core datasets are used for a first simulation of the field, and where each historical parameter value results in a simulation convergence during the first simulation, generating a machine learning model based at least on the historical core datasets and the historical parameter values, obtaining, during a second simulation of the field, a current core dataset, generating, using the machine learning model and based on the current core dataset, a predicted parameter value of the runtime parameter for achieving the simulation convergence during the second simulation, and completing, using at least the predicted parameter value, the second simulation to generate a modeling result of the field.

Runtime parameter selection in simulations

A method, apparatus, and program product model an oilfield asset by selecting, for each of multiple sectors of the oilfield asset, a sector model from among a collection of sector models, building a multi-resolution integrated asset model of the oilfield asset using the selected sector model for each of the sectors, and performing a computer simulation using the multi-resolution integrated asset model. The collection of sector models for each sector includes multiple sector models modeled at varying resolutions. In addition, the multi-resolution integrated asset model includes a surface network model that couples the selected sector models to one another. As such, different sectors of an oilfield asset may be modeled at varying resolutions to balance accuracy and turnaround time when performing integrated oilfield asset modeling.

Integrated oilfield asset modeling using multiple resolutions of reservoir detail

A new adjoint method for calculating and using adjoint gradients in a Reservoir Simulator comprises: calculating adjoint gradients of an objective function with respect to changes in valve settings taking into account the modeling of pressure drop and fluid flow along a wellbore, and using the adjoint gradients to calculate sensitivities of a reservoir to changes in parameterization of downhole devices and using of these sensitivities in optimal control of the wells to optimize some objective function subject to production constraints.

Method system and program storage device for optimization of valve settings in instrumented wells using adjoint gradient technology and reservoir simulation

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Abingdon, United Kingdom

David Rowan