Numerical Simulation of SAGD Recovery Process in Presence of Shale Barriers, Thief Zones, and Fracture System

Dang, Tiffany; Chen, Zhangxin; Nguyen, Tung; Bae, Wisup; Mai, C. L.

doi:10.1080/10916466.2010.545792

Cited by 26 publications

(7 citation statements)

References 12 publications

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“…They observed that the drainage and flow of hot fluid in the near-well region is highly sensitive to shale distribution, while the expansion of steam chamber away from the well pair is compromised by the presence of long, continuous shale or high shale proportion. Similar observations can also be found in Dang et al (2013). Amirian et al (2015) and Wang & Leung (2015) demonstrated that as the distance between a shale barrier and the horizontal well pair decreases, or as the volume or continuity of a shale barrier increases, SAGD recovery efficiency would decrease.…”

Section: Introductionsupporting

confidence: 73%

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Leung

Zanon

2016

Day 1 Tue, June 07, 2016

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SAGD recovery is strongly impacted by distributions of heterogeneous shale barriers, which impede the vertical growth and lateral spread of a steam chamber and potentially reduce oil production. Conventional reservoir heterogeneities characterization workflows that entail updating static reservoir models with dynamic flow data are quite time-consuming. Furthermore, numerical flow simulation could provide only approximate solutions to the recovery responses, as numerous simplifications and assumptions must be invoked. This study proposes a workflow integrating artificial intelligence (AI) in a model selection framework that aims to identify associated shale heterogeneities in SAGD reservoir based on observed decline patterns from production time-series data.A series of SAGD models based on typical Athabasca oil reservoir properties and operating conditions is constructed. Shale heterogeneities are modeled stochastically by sampling the location, lateral extent and thickness from probabilistic distributions inferred from field data. Each model is subjected to numerical flow simulation and the corresponding production profiles are recorded. First, sensitivity analysis is carried out to identify and parameterize particular patterns observable in the production response that are related to shale characteristics: whenever the steam chamber encounters a shale barrier, a drop in the production is observed; this drop continues until the steam chamber has advanced past the shale barrier, and the production would rise again. The positions and shapes of these decline patterns are retrieved. Next, artificial neural network (ANN) is constructed to calibrate a relationship between the retrieved production pattern parameters (inputs) and the corresponding geologic parameters describing shale heterogeneities (outputs), which include some variables capturing the location, size and orientation of a particular shale barrier encountered by the steam chamber.Shale barriers located close to the well pair have much more pronounced impacts on the production profiles. Shale barriers that are located far away do not exact a noticeable production pattern. The ANN models are validated using numerous synthetic reservoirs, where the exact shale distributions are known. The trained ANN models can reliably estimate the relevant shale parameters and the associated uncertainties, while accurately predicting the corresponding production responses. It is intended to extend the proposed method to construct the ANN models directly from well logs and production data.This work presents a preliminary attempt in correlating stochastic shale parameters with observable features in production time-series data using AI techniques. The proposed method facilitates the selection of an ensemble of reservoir models that are consistent with the production history; these models can be subjected to further history matching for a precise final match. The proposed methodology does not intend to replace traditional simulation and history-matching workflows, but it rathe...

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Section: Introductionsupporting

confidence: 73%

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Leung

Zanon

2016

Day 1 Tue, June 07, 2016

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“…They also proposed hydraulic fracturing and a high-pressure cycling method as solutions to increase the reservoir permeability and minimize the effect of barriers. − Some researchers have investigated the position and length of the barriers. They observed that shale barriers located distant from the producer and injector wells had a lower impact on production, whereas barriers bigger than 50 m act as an extended flow barrier, reducing oil production significantly. − However, the contribution of FCDs in SAGD injector and producer wells to deal with the shale barriers near wells to enhance oil production and lower the cSOR has not been widely investigated.…”

Section: Introductionmentioning

confidence: 99%

Impact of Inflow and Outflow Rate Control to Minimize Freshwater Usage: Historical Canadian Steam-Assisted Gravity Drainage Operations versus Numerical Simulations

Izadi,

Leung,

Soroush

et al. 2024

Energy Fuels

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This work delves into the effects of utilizing flow control devices (FCDs) to manage inflow and outflow rates on the performance of steam-assisted gravity drainage (SAGD) wells. The focus is on the impact of FCDs on enhancing oil production and reducing the cumulative steam oil ratio (cSOR). A retrospective analysis is conducted using historical data from Canadian SAGD operations to assess the impact of different flow control strategies. Additionally, numerical simulations are performed for various reservoir types, including homogeneous, simple with shale barriers, and heterogeneous reservoirs. FCDs are simulated based on findings from published flow-loop experiments. The primary benefit of incorporating flow-loop experiment data into the simulation lies in creating a mechanistic model grounded in physics as opposed to relying on empirical correlations. By comparison of the outcomes of both real-world data and numerical simulations, this study examines the influence of different flow rate control strategies on SAGD performance. Analyzing historical data extracted from a database encompassing seven major SAGD projects in Western Canada revealed that the optimal approach to enhance oil production and reduce cSOR involves the joint utilization of liner-deployed inflow control devices (LDICDs) and linerdeployed outflow control devices (LDOCDs). Given the limited availability of public information concerning the technical intricacies of flow rate control strategies and their implications on SAGD well performance, a series of simulations across diverse reservoir scenarios were conducted to investigate the mechanisms underlying the impact of FCDs on SAGD well performance. The numerical simulation findings revealed that the combined deployment of LDICDs and LDOCDs effectively managed hot-spot zones, where the inflow rate exceeded that of other sections along the producer well, leading to improved steam distribution. These results showed a potential increase in oil production of up to 26% and a reduction in the cSOR of up to 17%. This research endeavors to enhance our comprehension of how flow rate control through FCDs influences the performance of SAGD wells. The primary objective is to pave the way for more efficient well designs that contribute to reduced greenhouse gas (GHG) emissions, aligned with climate change mitigation goals.

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“…Shale barriers obstruct the bitumen drainage path along with excessive heat loss due to their high-water saturation. Various studies have been done to demonstrate the impact of shale barriers on SAGD performance. − Kumar and Hassanzadeh have presented a detailed literature review on the impact of shale barriers on SAGD and ES-SAGD . However, very few studies are available regarding the impact of shales on ES-SAGD performance. − A major industry standard to assess the performance of SAGD and ES-SAGD is the steam oil ratio (SOR).…”

Section: Introductionmentioning

confidence: 99%

Bitumen Recovery Performance of SAGD and Butane- and Hexane-Aided SAGD in the Presence of Shale Barriers

Kumar

2022

ACS Omega

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Oil and gas formations are commonly found to be heterogeneous, and one of the most common occurrences of reservoir heterogeneity is the presence of shale barriers. Shale barriers typically have very low permeability and high initial water saturation. Due to low permeability, these barriers obstruct the oil drainage path, specifically in thermal recovery methods such as steam-assisted gravity drainage (SAGD). In addition to flow assistance, they also lead to heat losses due to absorption by the high initial water saturation. Expanding solvent steam-assisted gravity drainage (ES-SAGD) is a hybrid technique comprising solvent co-injection along with steam. Solvent being in the vapor phase can potentially overcome the restricted path due to the presence of shale barriers. This paper presents a numerical simulation study on comparison between SAGD and ES-SAGD in the presence of shale barriers. SAGD and ES-SAGD with hexane and butane are numerically simulated for 240 lognormally generated shale realizations. First, both recovery processes are analyzed over the whole simulation period. Additionally, they have also been evaluated at multiple cumulative steam oil ratio cut-offs at 2, 2.5, 3, and 3.5. Transition points are defined and explained to cluster the shale density/fractions based on similar behaviors. It was shown that the oil that cannot be mobilized and produced by SAGD because shale barriers can be reached by the vaporized solvent through tortuous paths and recovered. Also, thermal losses are reduced because of lower steam chamber temperature. This led to efficient results for ES-SAGD over SAGD in heterogeneous formations.

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Numerical Simulation of SAGD Recovery Process in Presence of Shale Barriers, Thief Zones, and Fracture System

Cited by 26 publications

References 12 publications

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs

Impact of Inflow and Outflow Rate Control to Minimize Freshwater Usage: Historical Canadian Steam-Assisted Gravity Drainage Operations versus Numerical Simulations

Bitumen Recovery Performance of SAGD and Butane- and Hexane-Aided SAGD in the Presence of Shale Barriers

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