Steam Chamber Development and Production Performance Prediction of Steam Assisted Gravity Drainage

2014 ◽  
Author(s):  
Shaolei Wei ◽  
Cheng Lin-Song ◽  
Shijun Huang ◽  
Wenjun Huang
Keyword(s):  
Performance Prediction ◽  
Production Performance ◽  
Gravity Drainage ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage

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.

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 A steam rising model of steam-assisted gravity drainage production for heavy oil reservoirs

2019 ◽  
Vol 38 (4) ◽  
pp. 801-818
Author(s):  
Ren-Shi Nie ◽  
Yi-Min Wang ◽  
Yi-Li Kang ◽  
Yong-Lu Jia
Keyword(s):  
Production Rate ◽  
Horizontal Well ◽  
Oil Production ◽  
Steam Injection ◽  
Injection Rate ◽  
Gravity Drainage ◽  
Model Parameters ◽  
Steam Quality ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage

The steam chamber rising process is an essential feature of steam-assisted gravity drainage. The development of a steam chamber and its production capabilities have been the focus of various studies. In this paper, a new analytical model is proposed that mimics the steam chamber development and predicts the oil production rate during the steam chamber rising stage. The steam chamber was assumed to have a circular geometry relative to a plane. The model includes determining the relation between the steam chamber development and the production capability. The daily oil production, steam oil ratio, and rising height of the steam chamber curves influenced by different model parameters were drawn. In addition, the curve sensitivities to different model parameters were thoroughly considered. The findings are as follows: The daily oil production increases with the steam injection rate, the steam quality, and the degree of utilization of a horizontal well. In addition, the steam oil ratio decreases with the steam quality and the degree of utilization of a horizontal well. Finally, the rising height of the steam chamber increases with the steam injection rate and steam quality, but decreases with the horizontal well length. The steam chamber rising rate, the location of the steam chamber interface, the rising time, and the daily oil production at a certain steam injection rate were also predicted. An example application showed that the proposed model is able to predict the oil production rate and describe the steam chamber development during the steam chamber rising stage.

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.

Feasibility of time-lapse gravity and gravity gradiometry monitoring for steam-assisted gravity drainage reservoirs

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. WA99-WA111 ◽  
Author(s):  
Anya Reitz ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Heavy Oil ◽  
Production Efficiency ◽  
Low Cost ◽  
Time Lapse ◽  
Gravity Drainage ◽  
Great Success ◽  
Gravity Gradiometry ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage ◽  
The Cost

There is presently an increased need to monitor production efficiency as heavy oil reservoirs become more economically viable. We present a feasibility study of monitoring steam-assisted gravity drainage (SAGD) reservoirs using time-lapse gravimetry and gravity gradiometry. Even though time-lapse seismic has historically shown great success for SAGD monitoring, the gravimetry and gravity gradiometry methods offer a low-cost interseismic alternative that can complement the seismic method, increase the survey frequency, and decrease the cost of monitoring. In addition, both gravity-based methods are directly sensitive to the density changes that occur as a result of the replacement of heavy oil by steam. Advances in technologies have made both methods viable candidates for consideration in time-lapse reservoir monitoring, and we have numerically evaluated their potential application in monitoring SAGD production. The results indicate that SAGD production should produce a strong anomaly for both methods at typical SAGD reservoir depths. However, the level of detail for steam-chamber geometries and separations that can be recovered from the gravimetry and gravity gradiometry data is site dependent. Gravity gradiometry shows improved monitoring ability, such as better recovery of nonuniform steam movement due to reservoir heterogeneity, at shallower production reservoirs. Gravimetry has the ability to detect SAGD steam-chamber growth to greater depths than does gravity gradiometry, although with decreasing resolution of the expanding steam chambers.

Convection at the Edge of a Steam-Assisted-Gravity-Drainage Steam Chamber

SPE Journal ◽  
2011 ◽  
Vol 16 (03) ◽  
pp. 503-512 ◽  
Author(s):  
Jyotsna Sharma ◽  
Ian D. Gates
Keyword(s):  
Heat Transfer ◽  
Convective Heat Transfer ◽  
Relative Permeability ◽  
Convective Heat ◽  
Injection Well ◽  
Gravity Drainage ◽  
Oil Sand ◽  
Production Well ◽  
Steam Chamber ◽  
Steam Assisted Gravity Drainage

Summary Steam-assisted gravity drainage (SAGD) has become the preferred process to recover bitumen from Athabasca deposits in Alberta. The method consists of a lower horizontal production well, typically located approximately 2 m above the base of the oil zone, and an upper horizontal injection well located roughly 5 to 10 m above the production well. Steam flows from the injection well into a steam chamber that surrounds the wells and releases its latent heat to the cool oil sands at the edge of the chamber. This research re-examines heat transfer at the edge of the steam chamber. Specifically, a new theory is derived to account for convection of warm condensate into the oil sand at the edge of the chamber. The results show that, if the injection pressure is higher than the initial reservoir pressure, convective heat transfer can be larger than conductive heat transfer into the oil sand at the edge of the chamber. However, enhancement of the heat-transfer rate by convection may not necessarily imply higher oil rates; this can be explained by relative permeability effects at the chamber edge. As the condensate invades the oil sand, the oil saturation drops and, consequently, the oil relative permeability falls. This, in turn, results in the reduction of the oil mobility, despite the lowered oil viscosity because of higher temperature arising from convective heat transfer.

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