Reservoir Modeling and Production Performance Analysis to Investigate the Impacts of Reservoir Properties on Steam-Assisted Gravity Drainage in Cold Lake Oil Sands, Alberta

2016 ◽  
Author(s):  
Zexuan Wang
Keyword(s):  
Performance Analysis ◽  
Oil Sands ◽  
Production Performance ◽  
Reservoir Modeling ◽  
Gravity Drainage ◽  
Reservoir Properties ◽  
Steam Assisted Gravity Drainage ◽  
Cold Lake

Numerical Simulation of Thermal Solvent Replacing Steam under Steam Assisted Gravity Drainage SAGD Process

2010 ◽  
Author(s):  
Weiqiang Li ◽  
Daulat D. Mamora
Keyword(s):  
High Temperature ◽  
Oil Recovery ◽  
Oil Sands ◽  
Steam Injection ◽  
Thermal Recovery ◽  
Production Performance ◽  
Gravity Drainage ◽  
Steam Assisted Gravity Drainage ◽  
Recovery Technique ◽  
Simulation Results

Abstract Steam Assisted Gravity Drainage (SAGD) is one successful thermal recovery technique applied in the Athabasca oil sands in Canada to produce the very viscous bitumen. Water for SAGD is limited in supply and expensive to treat and to generate steam. Consequently, we conducted a study into injecting high-temperature solvent instead of steam to recover Athabasca oil. In this study, hexane (C6) coinjection at condensing condition is simulated using CMG STARS to analyze the drainage mechanism inside the vapor-solvent chamber. The production performance is compared with an equivalent steam injection case based on the same Athabasca reservoir condition. Simulation results show that C6 is vaporized and transported into the vapor-solvent chamber. At the condensing condition, high temperature C6 reduces the viscosity of the bitumen more efficiently than steam and can displace out all the original oil. The oil production rate with C6 injection is about 1.5 to 2 times that of steam injection with oil recovery factor of about 100% oil initially-in-place. Most of the injected C6 can be recycled from the reservoir and from the produced oil, thus significantly reduce the solvent cost. Results of our study indicate that high-temperature solvent injection appears feasible although further technical and economic evaluation of the process is required.

scholarly journals Identifying Reservoir Features via iSOR Response Behaviour

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 427
Author(s):  
Jingyi Wang ◽  
Ian Gates
Keyword(s):  
Oil Sands ◽  
Gravity Drainage ◽  
Athabasca Oil Sands ◽  
Steam Chamber ◽  
Heterogeneous Reservoirs ◽  
Steam Assisted Gravity Drainage ◽  
Response Behaviour

To extract viscous bitumen from oil sands reservoirs, steam is injected into the formation to lower the bitumen’s viscosity enabling sufficient mobility for its production to the surface. Steam-assisted gravity drainage (SAGD) is the preferred process for Athabasca oil sands reservoirs but its performance suffers in heterogeneous reservoirs leading to an elevated steam-to-oil ratio (SOR) above that which would be observed in a clean oil sands reservoir. This implies that the SOR could be used as a signature to understand the nature of heterogeneities or other features in reservoirs. In the research reported here, the use of the SOR as a signal to provide information on the heterogeneity of the reservoir is explored. The analysis conducted on prototypical reservoirs reveals that the instantaneous SOR (iSOR) can be used to identify reservoir features. The results show that the iSOR profile exhibits specific signatures that can be used to identify when the steam chamber reaches the top of the formation, a lean zone, a top gas zone, and shale layers.

Practical Data Mining and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Production Analysis

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Zhiwei Ma ◽  
Juliana Y. Leung ◽  
Stefan Zanon
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Field Data ◽  
Production Performance ◽  
Gravity Drainage ◽  
Production Forecast ◽  
Steam Assisted Gravity Drainage ◽  
System Variables

Production forecast of steam-assisted gravity drainage (SAGD) in heterogeneous reservoir is important for reservoir management and optimization of development strategies for oil sand operations. In this work, artificial intelligence (AI) approaches are employed as a complementary tool for production forecast and pattern recognition of highly nonlinear relationships between system variables. Field data from more than 2000 wells are extracted from various publicly available sources. It consists of petrophysical log measurements, production and injection profiles. Analysis of a raw dataset of this magnitude for SAGD reservoirs has not been published in the literature, although a previous study presented a much smaller dataset. This paper attempts to discuss and address a number of the challenges encountered. After a detailed exploratory data analysis, a refined dataset encompassing ten different SAGD operating fields with 153 complete well pairs is assembled for prediction model construction. Artificial neural network (ANN) is employed to facilitate the production performance analysis by calibrating the reservoir heterogeneities and operating constraints with production performance. The impact of extrapolation of the petrophysical parameters from the nearby vertical well is assessed. As a result, an additional input attribute is introduced to capture the uncertainty in extrapolation, while a new output attribute is incorporated as a quantitative measure of the process efficiency. Data-mining algorithms including principal components analysis (PCA) and cluster analysis are applied to improve prediction quality and model robustness by removing data correlation and by identifying internal structures among the dataset, which are novel extensions to the previous SAGD analysis study. Finally, statistical analysis is conducted to study the uncertainties in the final ANN predictions. The modeling results are demonstrated to be both reliable and acceptable. This paper demonstrates the combination of AI-based approaches and data-mining analysis can facilitate practical field data analysis, which is often prone to uncertainties, errors, biases, and noises, with high reliability and feasibility. Considering that many important system variables are typically unavailable in the public domain and, hence, are missing in the dataset, this work illustrates how practical AI approaches can be tailored to construct models capable of predicting SAGD recovery performance from only log-derived and operational variables. It also demonstrates the potential of AI models in assisting conventional SAGD analysis.

Approximate Physics-Discrete Simulation of the Steam-Chamber Evolution in Steam-Assisted Gravity Drainage

SPE Journal ◽  
2018 ◽  
Vol 24 (02) ◽  
pp. 477-491 ◽  
Author(s):  
Enrique Gallardo ◽  
Clayton V. Deutsch
Keyword(s):  
Oil Sands ◽  
Recovery Process ◽  
Thermal Recovery ◽  
Porous Media Flow ◽  
Gravity Drainage ◽  
Discrete Simulation ◽  
Flow Equations ◽  
Integrity Assessment ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage

Summary Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from oil sands. In this technology, steam injected in the reservoir creates a constantly evolving steam chamber while heated bitumen drains to a production well. Understanding the geometry and the rate of growth of the steam chamber is necessary to manage an economically successful SAGD project. This work introduces an approximate physics-discrete simulator (APDS) to model the steam-chamber evolution. The algorithm is formulated and implemented using graph theory, simplified porous-media flow equations, heat-transfer concepts, and ideas from discrete simulation. The APDS predicts the steam-chamber evolution in heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations to support decisions in the presence of geological uncertainty. The APDS is expected to be useful for selecting well-pair locations and operational strategies, 4D-seismic integration in SAGD-reservoir characterization, and caprock-integrity assessment.

Evaluation of Induced Thermal Pressurization in Clearwater Shale Caprock in Electromagnetic Steam-Assisted Gravity-Drainage Projects

SPE Journal ◽  
2013 ◽  
Vol 19 (03) ◽  
pp. 443-462 ◽  
Author(s):  
Sahar Ghannadi ◽  
Mazda Irani ◽  
Rick Chalaturnyk
Keyword(s):  
Pore Pressure ◽  
Analytical Solutions ◽  
Oil Sands ◽  
Shear Failure ◽  
Gravity Drainage ◽  
Hydraulic Fractures ◽  
Low Permeability ◽  
Steam Assisted Gravity Drainage ◽  
Thermal Pressurization ◽  
Shale Formation

Summary Inductive methods, such as electromagnetic steam-assisted gravity drainage (EM-SAGD), have been identified as technically and economically feasible recovery methods for shallow oil-sands reservoirs with overburdens of more than 30 m (Koolman et al. 2008). However, in EM-SAGD projects, the caprock overlying oil-sands reservoirs is also electromagnetically heated along with the bitumen reservoir. Because permeability is low in Alberta thermal-project caprock formations (i.e., the Clearwater shale formation in the Athabasca deposit and the Colorado shale formation in the Cold Lake deposit), the pore pressure resulting from the thermal expansion of pore fluids may not be balanced with the fluid loss caused by flow and the fluid-volume changes resulting from pore dilation. In extreme cases, the water boils, and the pore pressure increases dramatically as a result of the phase change in the water, which causes profound effective-stress reduction. After this condition is established, pore pressure increases can lead to shear failure of the caprock, the creation of microcracks and hydraulic fractures, and subsequent caprock integrity failure. It is typically believed that low-permeability caprocks impede the transmission of pore pressure from the reservoir, making them more resistant to shear failure (Collins 2005, 2007). In cases of induced thermal pressurization, low-permeability caprocks are not always more resistant. In this study, analytical solutions are obtained for temperature and pore-pressure rises caused by the constant EM heating rate of the caprock. These analytical solutions show that pore-pressure increases from EM heating depend on the permeability and compressibility of the caprock formation. For stiff or low-compressibility media, thermal pressurization can cause fluid pressures to approach hydrostatic pressure, and shear strength to approach zero for low-cohesive-strength units of the caprock (units of the caprock with high silt and sand percentage) and sections of the caprock with pre-existing fractures with no cohesion.

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