scholarly journals Correlating Stochastically Distributed Reservoir Heterogeneities with Steam-Assisted Gravity Drainage Production

2018 ◽  
Vol 73 ◽  
pp. 9 ◽  
Author(s):  
Cui Wang ◽  
Zhiwei Ma ◽  
Juliana Y. Leung ◽  
Stefan D. Zanon
Keyword(s):  
Performance Measures ◽  
Field Data ◽  
Scale Up ◽  
Operating Conditions ◽  
Production Performance ◽  
Data Driven ◽  
Gravity Drainage ◽  
Heterogeneous Distribution ◽  
Correlation Lengths ◽  
Steam Assisted Gravity Drainage

Application of big data analytics in reservoir engineering has gained wide attention in recent years. However, designing practical data-driven models for correlating petrophysical measurements and Steam-Assisted Gravity Drainage (SAGD) production profiles using actual field data remains difficult. Parameterization of the complex reservoir heterogeneities in these reservoirs is not trivial. In this study, a set of attributes pertinent to characterizing stochastic distributions of shales and lean zones is formulated and used for correlating against a number of production performance measures. A comprehensive investigation of the heterogeneous distribution (continuity, size, proportions, permeability, location, orientation and saturation) of shale barriers and lean zones is presented. First, a series of two-dimensional SAGD models based on typical Athabasca oil reservoir properties and operating conditions are constructed. Geostatistical techniques are applied to stochastically model shale barriers, which are imbedded in a region of degraded rock properties referred to as Low-Quality Sand or LQS, among a background of clean sand. Parameters including correlation lengths, orientation, proportions and permeability anisotropy of the different rock facies are varied. Within each facies, spatial variations in water saturation are modeled probabilistically. In contrast to many previous simulation studies, representative multiphase flow functions and capillarity models are assigned in accordance to individual facies. A set of input attributes based on facies proportions and dimensionless correlation lengths are formulated. Next, to facilitate the assessment of different scenarios, production performance is quantified by numerous dimensionless output attributes defined from recovery factor and steam-to-oil ratio profiles. An additional dimensionless indicator is implemented to capture the production time during which the instantaneous steam-to-oil ratio has exceeded a particular economic threshold. Finally, results of the sensitivity analysis are employed as training and testing datasets in a series of neural network models to correlate the pertinent system attributes and the production performance measures. These models are also used to assess the consequences of ignoring lateral variation of heterogeneities when extracting petrophysical (log) data from vertical delineation wells alone. An important contribution of this work is that it proposes a set of input attributes for correlating reservoir heterogeneity introduced by shale barriers and lean zones to SAGD production performance. It demonstrates that these input attributes, which can be extracted from petrophysical logs, are highly correlated with the ensuing recovery response and heat loss. This work also exemplifies the feasibility and utility of data-driven models in correlating SAGD performance. Furthermore, the proposed set of system variables and modeling approach can be applied directly in field-data analysis and scale-up study of experimental models to assist field-operation design and evaluation.

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.

Phase Analysis of Scale Deposition in Boiler Tubes Utilizing Steam-Assisted Gravity Drainage Produced Water

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Raymond Kuriger ◽  
David Young ◽  
Malcolm Mackenzie ◽  
Hamid Sarv ◽  
Jason Trembly
Keyword(s):  
Heat Transfer ◽  
Produced Water ◽  
Magnesium Silicate ◽  
Solid Content ◽  
Operating Conditions ◽  
Gravity Drainage ◽  
Dissolved Solids ◽  
X Ray ◽  
Steam Assisted Gravity Drainage ◽  
Boiler Tubes

Scale buildup on water-side heat transfer surfaces poses a potential operating challenge for steam-assisted gravity drainage (SAGD) boilers used in the production of bitumen since produced water, which has a high dissolved solid content, is recycled. Scale from deposition of dissolved solids on boiler tubes acts as a thermal insulating layer, decreasing heat transfer and lowering boiler efficiency. Understanding scale deposit composition on heat transfer surfaces is beneficial in the determination of adequate boiler maintenance practices and operating parameters. This research determined the effect of feedwater pH (7.5, 9.0, and 10.0) on scale composition resulting from deposition of dissolved solids under commercially relevant boiler operating conditions at 8.96 MPa (1300 psig) and 37.86 kW/m2 (12,000 Btu/h ft2). Scale deposits were analytically investigated using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM/EDS), powder X-ray diffraction (XRD), and Raman spectroscopy. At feedwater pH values of 7.5 and 9.0, anhydrite (CaSO4), xonotlite (Ca6Si6O17(OH)2), and pectolite (NaCa2Si3O8(OH)) were detected. At the pH of 10.0, xonotlite and pectolite were identified in the absence of anhydrite. Furthermore, the magnesium silicate phase, serpentine (Mg3Si2O5(OH)4), was also postulated to be present.

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 Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

ACS Omega ◽  
2021 ◽  
Vol 6 (17) ◽  
pp. 11497-11509
Author(s):  
Yang Yu ◽  
Shangqi Liu ◽  
Yang Liu ◽  
Yu Bao ◽  
Lixia Zhang ◽  
...  
Keyword(s):  
Oil Production ◽  
Data Driven ◽  
Gravity Drainage ◽  
Steam Assisted Gravity Drainage ◽  
Proxy Model

The Role of Emulsions in Steam-Assisted-Gravity-Drainage (SAGD) Oil-Production Process: A Review

SPE Journal ◽  
2019 ◽  
Vol 25 (02) ◽  
pp. 969-989 ◽  
Author(s):  
Shadi Ansari ◽  
Reza Sabbagh ◽  
Yishak Yusuf ◽  
David S. Nobes
Keyword(s):  
Relative Permeability ◽  
Single Phase ◽  
Operating Conditions ◽  
Qualitative Description ◽  
Gravity Drainage ◽  
Steam Assisted Gravity Drainage ◽  
Well Completion ◽  
Sand Control ◽  
Reservoir Conditions ◽  
Emulsification Process

Summary Studies that investigate and attempt to model the process of steam-assisted gravity drainage (SAGD) for heavy-oil extraction often adopt the single-phase-flow assumption or relative permeability of the moving phases as a continuous phase in their analyses. Looking at the emulsification process and the likelihood of its prevalence in SAGD, however, indicates that it forms an important part of the entire physics of the process. To explore the validity of this assumption, a review of prior publications that are related to the SAGD process and the modeling approaches used, as well as works that studied the emulsification process at reservoir conditions, is presented. Reservoir conditions are assessed to identify whether the effect of the emulsion is strong enough to encourage using a multiphase instead of a single-phase assumption for the modeling of the process. The effect of operating conditions on the stability of emulsions in the formation is discussed. The review also covers the nature and extent of effects from emulsions on the flow mechanics through pore spaces and other flow passages that result from the well completion and downhole tubing, such as sand/flow-control devices. The primary outcome of this review strengthens the idea that a multiphase-flow scenario needs to be considered when studying all flow-related phenomena in enhanced-oil-recovery processes and, hence, in SAGD. The presence of emulsions significantly affects the bulk properties of the porous media, such as relative permeability, and properties that are related to the flow, such as viscosity, density, and ultimately pressure drop. It is asserted that the flow of emulsions strongly contributed to the transport of fines that might cause plugging of either the pore space or the screen on the sand-control device. The qualitative description of these influences and their extents found from the review of this large area of research is expected to guide activities during the conception stages of research questions and other investigations.

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