Landmark Graphics Corporation

Hasnain A Khan

Gustavo Carvajal

Luigi Saputelli

Amit Kumar

Alvin Stanley Cullick

Priyesh Ranjan

Sheldon Burt Gorell

Hatem N Nasr

Karelis Alejandra Urrutia

Gustavo A Carvajal

Ranjan Priyesh

Ajay Pratap Singh

Marko Maucec

Seyed M Mirzadeh

Luigi A Saputelli

Burt Gorell Sheldon

A hydrocarbon field including thief zones can be modeled based on production history data and supplemental data constraining a thief-zone distribution across the field. In various embodiments, a numerical optimization approach involves creating a plurality of model thief-zone distributions with varying parameter values, parameterizing permeability maps obtained for the thief-zone distributions and aggregating across the thief-zone distributions to obtain distributions of coefficients associated with the parameterization, and then iteratively sampling the coefficient distributions, determining a permeability map of the model based on the selected coefficients, and measuring a deviation between the measured production history data and simulated production history data derived from the computational model until a match is achieved.

History matching of hydrocarbon production from heterogenous reservoirs

Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

Systems and methods for subsurface oil recovery optimization

The present disclosure describes systems and methods for performing multi-level reservoir history matching. At least some illustrative embodiments include a method that includes generating a first history-matched model using at least one updated model parameter derived from one or more existing model parameters, generating a set of second history-matched models by applying a probabilistic inversion to the first history-matched model, and deriving a set of third history-matched models from the set of second history-matched models. The method further includes generating dynamic simulation realization sets using each of the set of third history-matched models, ranking each of the set of third history-matched models based at least in part on the dynamic simulation realization sets, and presenting a production forecast to a user based on the highest ranked third history-matched models.

Multi-level reservoir history matching

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Houston, TX, United States of America

Hasnain A Khan