Reference number : GC-2017-Q3-00534, amendment number : , agreement type : G, recipient legal name : Nouri, Alireza (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Optimizing reservoir development and resource management requires an accurate forecast of recovery performance. Petrophysical properties are essential to the evaluation of the reservoir formation. Porosity and permeability are the most important parameters since they control resource estimation and flow rate, respectively. Over the life of the reservoir, many crucial decisions depend on the accuracy of these predictions. However, the estimation of such properties, especially permeability, is challenging as it is difficult and expensive to acquire representative rock samples for permeability testing in oil sand deposits. This research proposes a methodology to estimate accurate high-resolution permeability (and porosity) for improving reservoir forecasts._x000D_
The aim of this research proposal is to predict permeability and porosity by machine learning techniques. CT-Scans and image logs will be combined with the conventional well logs to build a 3D indicator model and representative porosity-permeability relationships. Next, a high-resolution porosity-permeability relationship will be built. Finally, permeability (horizontal and vertical), facies, porosity and water saturation are modeled for the entire reservoir domain. The calibrated porosity-permeability relationships are applied to construct properties at individual grid block. _x000D_
This research will be primarily beneficial to the Canadian company PetroChina Canada Ltd. (formerly known as Brion Energy Corporation). The results of this project could lead to reduced steam consumption, optimized production design, minimized environmental footprint and improved reservoir forecast by improving numerical reservoir models. This will lead to substantial environmental and economic advantages to Canada by mitigating water resource usage, and natural resource could be extracted with more attention to environment. _x000D_
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_x000D_, program purpose french : Optimizing reservoir development and resource management requires an accurate forecast of recovery performance. Petrophysical properties are essential to the evaluation of the reservoir formation. Porosity and permeability are the most important parameters since they control resource estimation and flow rate, respectively. Over the life of the reservoir, many crucial decisions depend on the accuracy of these predictions. However, the estimation of such properties, especially permeability, is challenging as it is difficult and expensive to acquire representative rock samples for permeability testing in oil sand deposits. This research proposes a methodology to estimate accurate high-resolution permeability (and porosity) for improving reservoir forecasts._x000D_
The aim of this research proposal is to predict permeability and porosity by machine learning techniques. CT-Scans and image logs will be combined with the conventional well logs to build a 3D indicator model and representative porosity-permeability relationships. Next, a high-resolution porosity-permeability relationship will be built. Finally, permeability (horizontal and vertical), facies, porosity and water saturation are modeled for the entire reservoir domain. The calibrated porosity-permeability relationships are applied to construct properties at individual grid block. _x000D_
This research will be primarily beneficial to the Canadian company PetroChina Canada Ltd. (formerly known as Brion Energy Corporation). The results of this project could lead to reduced steam consumption, optimized production design, minimized environmental footprint and improved reservoir forecast by improving numerical reservoir models. This will lead to substantial environmental and economic advantages to Canada by mitigating water resource usage, and natural resource could be extracted with more attention to environment. _x000D_
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_x000D_, agreement title english : Image-based Permeability Assessment for Oil Sands and Application to Reservoir Forecast, agreement title french : Image-based Permeability Assessment for Oil Sands and Application to Reservoir Forecast, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-12-13, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada