Further Investigation of CO Based Vapex For the Recovery of Heavy Oils And Bitumen

Talbi, K.; Maini, Brij B.

doi:10.2118/2004-270

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…Ineffectual inputs may complicate the training procedure and result in imprecise predictions. Based on a comprehensive literature review on the available experimental studies and the observations obtained during the experiments in this study five parameters were considered as the inputs for training the ANN model . These five input parameters include drainage height, heavy oil viscosity, solvent type, permeability, and porosity.…”

Section: Soft Computing‐based Approachmentioning

confidence: 99%

“…To successfully model the complex relationships between the input and output parameters, a large number of datasets was required to train and test the ANN model. In order to gather an appropriate number of datasets the experimental results from this study were combined by the available experimental results in the literature, and a total of 223 datasets were collected to develop the ANN model. These datasets were divided into two categories: one category included 155 datasets for training and validating the network and the second category consisted of 68 datasets for testing the trained network.…”

Section: Soft Computing‐based Approachmentioning

confidence: 99%

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A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

Mohammadpoor

Torabi

2018

Can J Chem Eng

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There are vast resources of heavy oil and bitumen reservoirs in the Western Canadian Basin. For many of them up to 95 % of reserves still remain in place, and by considering the increase in future energy demand these abundant resources can be considered as potential sources for future years. Recently, solvent‐based heavy oil recovery methods such as vapour extraction (VAPEX) have gained attention due to the potential environmental and economic assets over thermal processes. Due to the complexity of the mechanisms associated with the solvent injection process (i.e. diffusion and gravity drainage processes), such models are incapable of accurately predicting the production rate during the VAPEX process. In this study, the artificial neural networks (ANN) technique is utilized to tackle the limitations that analytical methods encounter while predicting the complex relationships, where there is uncertainty, imprecision, and partial truth. Hence, in the first phase of the research a comprehensive experimental study in two large‐scale, visual rectangular VAPEX models was carried out by utilizing various injection solvents. Based on an extensive literature review and experimental results, the drainage height, solvent type, permeability, porosity, and heavy oil viscosity were considered as the inputs of the model to predict the heavy oil production rate as the output of the model. After trying different training scenarios, it was found that the back‐propagation learning algorithm can be successfully used to predict the ultimate recovery factor after implementing the VAPEX method in the heavy oil system of interest.

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Section: Soft Computing‐based Approachmentioning

confidence: 99%

Section: Soft Computing‐based Approachmentioning

confidence: 99%

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

Mohammadpoor

Torabi

2018

Can J Chem Eng

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“…In the ES-SAGD process, the solvent is injected with steam in a vapor phase. These include solvent type and composition (Badamchizadeh et al, 2008;Das and Butler, 1994b;Derakhshanfar et al, 2009;Gul and Trivedi;Singhal et al, 2002;Talbi and Maini, 2004), Upscaling (Yazdani and Maini, 2005), reservoir parameters effects (Jiang and Butler, 1996;Thimm, 2007), Asphaltene precipitation (Ardali et al, 2009;Das and Butler, 1994a;Haghighat and Maini, 2008), etc. Later, the experiments were carried out in a 2D rectangular model (Ayodele et al, 2008;Ayodele et al, 2009;Ayodele et al, 2010;Ivory et al, 2008).…”

Section: Experimental Studiesmentioning

confidence: 99%

A Critical Review of Hybrid Steam-Solvent Processes to Recover Heavy Oil

Ardali

Barrufet

Mamora

et al. 2012

SPE Annual Technical Conference and Exhibition

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Steam-Assisted Gravity Drainage (SAGD) is the main commercial technology used for in-situ recovery of Canadian heavy oil and Bitumen. It is commercially proven and delivers high oil rates and high ultimate recoveries. One of the long-term concerns with the SAGD process is high energy intensity and related environmental impacts. Hybrid processes have been developed to take partial advantage of steam and solvent processes while introducing a more efficient and more economically viable recovery methods. Several processes such as Propane-SAGD, Expanding Solvent-SAGD (ES-SAGD), Solvent-Aided Process (SAP), Liquid Addition to Steam to Enhance Recovery (LASER) and Steam-Alternating-Solvent (SAS) were proposed; some of them currently under pilot test. Hybrid steam-solvent processes aim to accelerate oil production rate with lower cost than SAGD and also increase the ultimate oil recovery. Despite remarkable amount of laboratory and computational studies on these processes, there was no extensive critical review of the knowledge obtained for more than a decade. The current level of understanding of the hybrid processes and knowledge around the fundamental physics and mechanisms involved are not fully satisfactory. We believe that a critical review of the status of the hybrid processes will fill the gap by shedding the light on the deficiencies and the limitations of the process, further development areas, and new research topics. Analytical, numerical simulations, laboratory modeling efforts along with pilot test results are summarized. In addition, the main technical challenges of different aspects of hybrid steam-solvent processes are analyzed at different levels. In this paper, special attention is given to a) The effect of reservoir and operational parameters, b) solvent injection strategies, c) The inconsistency between laboratory, simulation and field results and d) problems faced in numerical modeling (capturing the physics of heat and mass transfer). It is believed that a good compilation of the records produced over one decade will constitute a useful reference for the industry and academics. Analytical, simulation, laboratory studies and reported field data strongly support hybrid steamsolvent processes. However, the results are mixed at different level levels and there exists some inconsistencies. The cost of the solvent retained in the reservoir is the major concern and the economics of selected hybrid steam-solvent process for a specific reservoir has to be verified using available tools. The main challenges are verifying effective mixing of the solvent with the in-situ bitumen, managing the solvent placement and distribution in the reservoir, reliably determining the incremental benefit of solvent-addition and ensuring economic solvent recovery.

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“…It is beneficial both environmentally and in terms of recovery, since it is more soluble than methane in heavy oil. The saturation pressure of CO 2 , its solubility in heavy oils, its price compared to other solvents, and the environmental effects to reduce greenhouse gas emissions can favor the use of CO 2 in the Vapex process, known as CO 2 -based Vapex [6] . In an experiment done by Dunn et al [7] , they used lowtemperature soluble gases to recover bitumen from a glass bead packed cell.…”

Section: Petroleum Societymentioning

confidence: 99%

Modelling of Mass Transfer Boundary Layer Instability in the CO-Vapex Process

Javaheri¹,

Abedi²

2008

Canadian International Petroleum Conference

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Vapex (vapor extraction) is a promising technique for the recovery of heavy oil and bitumen reservoirs, especially for cases where steam-assisted gravity drainage and other thermal recovery methods are not economical. In the Vapex process, a solvent is injected into the reservoir to reduce the oil viscosity and mobilize it towards the production well. CO 2-based Vapex is an attractive option from both economical and environmental perspectives. In CO 2-based Vapex, unlike other hydrocarbon solvents, the dissolution of CO 2 in oil can result in a density increase of the diluted oil. As a consequence, the diluted oil has a higher density than the immobile oil beneath and a gravitationally unstable diffusive boundary layer is induced, which may lead to natural convection. In this paper, a mathematical model for the diffusive boundary layer in the CO 2-oil contact region is developed; and, the possibility of convective mixing is examined using linear stability analysis, based on the amplification of the initial perturbations. It is found that in most experimental cases, depending on the Rayleigh number of the porous medium, convective mixing occurs, which results in higher dissolution of CO 2 in oil and thus a higher oil production rate than what is expected from theoretical analysis. This would explain the unexpected higher oil production rate of some experiments in Vapex when CO 2 was used as a solvent. In field-scale operations, the results are different. In field cases, since it is almost impossible for the Rayleigh number to exceed the critical Rayleigh number (Ra c), convection does not happen.

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Further Investigation of CO Based Vapex For the Recovery of Heavy Oils And Bitumen

Cited by 9 publications

References 7 publications

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

A Critical Review of Hybrid Steam-Solvent Processes to Recover Heavy Oil

Modelling of Mass Transfer Boundary Layer Instability in the CO-Vapex Process

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