Landmark Graphics Corporation

Halliburton Energy Services, Inc.

Landmark Graphics Corporaton

Yevgeniy Zagayevskiy

Srinath Madasu

Terry Wayne Wong

Dominic Camilleri

Shohreh Amini

Hui Dong

Harsh Biren Vora

Charles Hai Wang

Courtney Leeann Beck

Azor Nwachukwu

Zhi Chai

Hanzi Mao

Jeffrey Marc Yarus

Andrey V Filippov

Genbao Shi

Vitaly Khoriakov

Rae Mohan Srivastava

Maurice Christopher Gehin

Gaetan Bardy

Yongchae Cho

Yang Cao

Yuribia Patricia Munoz

An apparatus for generating forecasts from a high-dimensional parameter data space comprising a reservoir model, a model order reduction module, and an assisted history matching module. The reservoir model having input variables, output variables, and an algorithmic model. The input variables, output variables, and the algorithmic model are generated by a flow simulator module and from a formation and reservoir properties database and a field production database. The model order reduction module generates a subset of the original or transformed input variables. The assisted history matching module adjust values of the output variables based on a difference between the at least one of the output variables and dynamic field production data to improve model accuracy.

Model parameter reductions and model parameter selection to optimize execution time of reservoir management workflows

An apparatus used to generate forecasts from a high-dimensional parameter data space. The apparatus comprising a reservoir model and a flow simulator module. The reservoir model comprising a plurality input variables, output variables, and at least one algorithmic model. The input variables and output variables are generated by the flow simulator module and variables from a formation and reservoir properties database and a field production database. The flow simulator module generates the at least one algorithmic model and the output variables using at least one selected from a group comprising a full-physics flow simulator, proxy flow simulator for assisted history matching, and a proxy flow simulator for field development optimization. The full-physics flow simulator and the two proxy flow simulators generate the at least one algorithmic model using at least one selected from a group comprising the reservoir model, history matching input variables, and optimization input variables.

Flow simulator for generating reservoir management workflows and forecasts based on analysis of high-dimensional parameter data space

A method includes collecting a first set of borehole gravity data at a first time step along a length of a first wellbore and collecting a second set of borehole gravity data at the first time step along a length of a second wellbore. The method also includes interpolating a third set of borehole gravity data at the first time step in an area between the first wellbore and the second wellbore using the first and the second sets of borehole gravity data. Further, the method includes determining a first fluid saturation and a fluid saturation change over time in a reservoir containing the first wellbore and the second wellbore using the first set, the second set, and the third set. Moreover, the method includes controlling wellbore production operations or wellbore injection operations at the first wellbore based on the fluid saturation change.

Gas saturation distribution monitoring in hydrocarbon reservoir

Using production data and a production flow record based on the production data, a deep neural network (DNN) is trained to model a proxy flow simulation of a reservoir. The proxy flow simulation of the reservoir is performed, using an ensemble Kalman filter (EnKF), based on the trained DNN. The EnKF assimilates new data through updating a current ensemble to obtain history matching by minimizing a difference between a predicted production output from the proxy flow simulation and measured production data from a field. Using the updated current ensemble, a second proxy flow simulation of the reservoir is performed based on the trained DNN. The assimilating and the performing are repeated while new data is available for assimilating. Predicted behavior of the reservoir is determined based on the proxy flow simulation of the reservoir. An indication of the predicted behavior is provided to facilitate production of fluids from the reservoir.

Petroleum reservoir behavior prediction using a proxy flow model

A method for history matching a reservoir model based on actual production data from the reservoir over time generates an ensemble of reservoir models using geological data representing petrophysical properties of a subterranean reservoir. Production data corresponding to a particular time instance is acquired from the subterranean reservoir. Normal score transformation is performed on the ensemble and on the acquired production data to transform respective original distributions into normal distributions. The generated ensemble is updated based on the transformed acquired production data using an ensemble Kalman filter (EnKF). The updated generated ensemble and the transformed acquired production data are transformed back to respective original distributions. Future reservoir behavior is predicted based on the updated ensemble.

Optimized methodology for automatic history matching of a petroleum reservoir model with Ensemble Kalman Filter (EnKF)

A method for identifying a flow parameter in a wellbore may comprise identifying a state vector at a moment t, performing a flow simulation using a flow model, predicting the state vector and a covariance matrix at the moment t, updating the state vector with an EnKF algorithm, correcting the state vector at the moment t, and updating the flow simulation model. A system for identifying a flow parameter in a wellbore may comprise a distributed acoustic system into a wellbore and an information handling system. The distributed acoustic system may comprise a fiber optic cable and at least one measurement device.

Application of the ensemble Kalman filter to dynamic history matching in wellbore production

A method for creating a seamless scalable geological model may comprise identifying one or more geological scales, establishing a geological tied system, identifying one or more graphical resolution levels for each of the one or more geological scales, constructing the seamless scalable geological model, and producing a post-process model. A system for creating a seamless scalable geological model may comprise an information handling system, which may comprise a random access memory, a graphics module, a main memory, a secondary memory, and one or more processors configured to run a seamless scalable geological model software.

Seamless scaling geomodeling

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Houston, TX, United States of America

Yevgeniy Zagayevskiy