Statistical Upscaling Workflow for Warm Solvent Injection Processes
Creators: Andriianova, Elena
Publisher: name: University of Alberta Library
Year: 2024

<p>Hybrid solvent- and thermal-based methods offer an important potential for reducing the environmental impact and enhancing recovery efficiency in heavy oil production processes. Time-consuming compositional simulations of fine-scale models challenge the optimization of operational conditions.  This research aims to develop a novel flow-based upscaling workflow for simulating the Warm Solvent Injection (WSI) process in heterogeneous reservoirs. The method takes into account the pattern of spatial heterogeneity at different scales. <br>
This study explores the scaling up of various static parameters (facies, porosity, permeability) and dynamic or transport parameters (dispersivities and thermal diffusivity). Homogenizing the reservoir&#39;s heterogeneity is achieved through model coarsening, leading to a decrease in local velocity fluctuations. Consequently, heterogeneity-dependant parameters, such as thermal conductivity and dispersivity, require careful consideration during the coarsening process. The workflow offers a new approach to upscaling scale-dependent transport parameters that help consider additional uncertainties in the flow behaviour predictions. The method is flexible, as no explicit assumption regarding the multivariate distribution of the heterogeneity is required.<br><br>
Firstly, to build a workflow for statistical upscaling, 2D synthetic models were designed with properties of Canadian FortMcurray formation to apply the WSI technique. Next, a two-step workflow is proposed: (1) effective dispersivity is estimated based on cell size increase for the equivalent homogeneous model. (2) In a calibration step, several realizations of sub-scale models of the modeling-scale cell size are generated in order to depict the spatial heterogeneity below the modeling scale. The difference in the flow simulation responses of a heterogeneous realization and an equivalent average model are minimized to estimate scale-dependant transport parameters essential for the thermal-solvent processes, such as effective longitudinal and transverse dispersivities and thermal conductivity. Probability distributions of effective dispersivities and thermal conductivity are constructed by aggregating results from all realizations. Finally, the workflow is tested for a 3D model with datasets assembled from actual field data.<br>
This research focuses on developing a statistical upscaling workflow for warm solvent injection processes that incorporates both longitudinal and transverse dispersivity effects, implementing a cloud transform to sample from the conditional probability considering porosity and net-to-gross ratio. The workflow was applied to a case study, where oil saturation, solvent chamber propagation and temperature maps were compared before and after statistical upscaling was applied.<br>
This research would make it easier and faster to test oil production processes to assist heavy oil production companies identify and improve more environmentally friendly and economical ways to produce heavy oil and bitumen. The results of this work demonstrate the workflow&#39;s capability to estimate effective dispersivity and improve the scaled-up model&#39;s performance.</p>