103 citations · 112 across the 5 of their papers we have counts for
5 papers
A Deep Learning-Aided Approach for Estimating Field Permeability Map by Fusing Well Logs, Well Tests, and Seismic Data
Grigoriy Shutov, Viktor Duplyakov, Shadfar Davoodi +11
Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and, therefore, designing effective recovery strateg…
Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements
E. A. Kanin, A. A. Garipova, S. A. Boronin +5
We propose a new method for construction of the absolute permeability map consistent with the interpreted results of well logging and well test measurements in oil reservoirs. Nada…
Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem
Viktor Duplyakov, Anton Morozov, Dmitriy Popkov +5
We describe a stacked model for predicting the cumulative fluid production for an oil well with a multistage-fracture completion based on a combination of Ridge Regression and CatB…
Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast
A. D. Morozov, D. O. Popkov, V. M. Duplyakov +6
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via mach…
A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data
Evgenii Kanin, Andrei Osiptsov, Albert Vainshtein +1
Engineering simulators used for steady-state multiphase pipe flows are commonly utilized to predict pressure drop. Such simulators are typically based on either empirical correlati…