Global Sensitivity Analysis for Covering Reservoir Geological and Flow Uncertainty
| dc.contributor.advisor | Chen, Zhangxing (John) | |
| dc.contributor.advisor | Costa Sousa, Mario | |
| dc.contributor.author | Karami Moghadam, Ali | |
| dc.contributor.committeemember | Mackay, Eric J | |
| dc.contributor.committeemember | Dimitrov, Vassil Simeonov | |
| dc.contributor.committeemember | Alim, Usman | |
| dc.date | 2018-02 | |
| dc.date.accessioned | 2018-01-13T00:33:36Z | |
| dc.date.available | 2018-01-13T00:33:36Z | |
| dc.date.issued | 2017-12-21 | |
| dc.description.abstract | Understanding sources of uncertainty has a major impact in the reservoir management design and significantly influences the operations decision-making. Integrating all sources of flow and geological uncertainty is very important to quantify the production uncertainty and to make optimal decisions in reservoir development. However, this task is computationally very intensive and the current methods used by the industry are not robust enough to capture the full complexity of the problem. To address this, this research focuses on identifying sources of input uncertainty that significantly influence reservoir response and decision making. Some sources of input uncertainty are significant by themselves. Others are significant through their interactions. Yet others are not significant at all. This information offers great insight as well as computational gains that reservoir engineers can exploit towards better utilizing their knowledge when making reservoir decisions for the company assets. For this purpose, this research provides promising sensitivity analysis frameworks that are suitable for tackling complex multi-dimensional models within the reservoir modelling workflow and overcome the drawbacks of the commonly used approaches. The first section of this research study introduces a screening method that can successfully categorize the uncertain parameters in terms of their significance to the reservoir response with a low computational cost. This method is followed by a more sophisticated approach that is able to quantify the contribution of each input parameter to the variability of the model responses as well as the existing interactions among the parameters. The relation between the accuracy of the results and the choice of experiment design is discussed in this section. In order to overcome the high cost of computation intrinsic to this method, Single-Layer and Multi-Layer Neural Network surrogate models are successfully employed and integrated with the method. In the next chapter, another approach based on classifying the response/decision variables into a limited set of discrete classes is discussed. This approach quantifies the sensitivity to parameters and parameter interactions, and incorporates the possibility that the interactions can be asymmetric for complex reservoir modeling. The discussed approaches are demonstrated and validated with multiple well known sensitivity analysis test functions and real field case studies. | en_US |
| dc.identifier.citation | Karami Moghadam, A. (2017). Global Sensitivity Analysis for Covering Reservoir Geological and Flow Uncertainty (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/5254 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/5254 | |
| dc.identifier.uri | http://hdl.handle.net/1880/106256 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.faculty | Schulich School of Engineering | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Petroleum Engineering | |
| dc.subject | Reservoir Simulation | |
| dc.subject | Sensitivity Analysis | |
| dc.subject | Uncertainty Quantification | |
| dc.subject.classification | Engineering--Petroleum | en_US |
| dc.title | Global Sensitivity Analysis for Covering Reservoir Geological and Flow Uncertainty | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Chemical and Petroleum Engineering | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | |
| ucalgary.thesis.checklist | I confirm that I have submitted all of the required forms to Faculty of Graduate Studies. | en_US |