The Development of Artificial-neural-network-based Universal Proxies to Study Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) Processes

2015 ◽  
Author(s):  
Qian Sun ◽  
Turgay Ertekin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gravity Drainage ◽  
Steam Assisted Gravity Drainage ◽  
Cyclic Steam Stimulation ◽  
Artificial Neural ◽  
Steam Stimulation

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.

scholarly journals Optimization of Fast-steam-assisted gravity drainage for the energy-efficient operations at a heterogeneous oil-sands reservoir

2017 ◽  
Vol 36 (5) ◽  
pp. 1040-1060
Author(s):  
Jiyeon Choi ◽  
Changhyup Park ◽  
Soonhyeong Jeong
Keyword(s):  
Energy Efficient ◽  
Early Stage ◽  
Injection Pressure ◽  
Gravity Drainage ◽  
Fluid Mixture ◽  
Efficient Operation ◽  
Steam Assisted Gravity Drainage ◽  
Positive Effects ◽  
Cyclic Steam Stimulation ◽  
Steam Stimulation

This paper discusses the energy-efficient operation of Fast-steam-assisted gravity drainage wellpad system in the presence of reservoir heterogeneity, different well constraints, and lateral flux communication between adjacent steam chambers. Fast-steam-assisted gravity drainage incorporates cyclic steam stimulation in an unrecovered area between steam-assisted gravity drainage wellpairs, and the well constraints of the wellpad system (including the injection pressure and steam injection rate at the injectors, bottom hole pressure, surface liquid rate, and steam rate at the producers) are simultaneously optimized to accomplish the minimum cumulative steam-to-oil ratio for a given bitumen recovery constraint. The higher injection pressures of the cyclic steam stimulation can result in greater efficiency by pushing the diluted fluid mixture to the steam-assisted gravity drainage producers through the cross-over zone between the steam chambers. At an early stage, a greater amount of steam should be injected through the cyclic steam stimulation work, and at the late stage, a lower injection pressure is needed to use the latent heat. The positive effects of the cyclic steam stimulation at the edges of the steam-assisted gravity drainage steam chambers are concentrated at localized flow paths where the lateral flux transport occurs due to spatial heterogeneity. A sensitivity analysis shows that the injection pressure and the steam rate produced for the steam-assisted gravity drainage wellpairs influence the energy efficiency of the entire thermal operation when compared to other configurations.

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