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 ◽  
...  
2019 ◽  
Vol 38 (4) ◽  
pp. 801-818
Author(s):  
Ren-Shi Nie ◽  
Yi-Min Wang ◽  
Yi-Li Kang ◽  
Yong-Lu Jia

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.


2017 ◽  
Vol 12 (5) ◽  
pp. 428-433
Author(s):  
G. M. D. Fernandes ◽  
A. A. R. Diniz ◽  
E. A. Araújo ◽  
M. A. F. Rodrigues ◽  
A. R. Gurgel ◽  
...  

SPE Journal ◽  
2017 ◽  
Vol 23 (01) ◽  
pp. 117-127 ◽  
Author(s):  
Zeinab Zargar ◽  
S. M. Farouq Ali

Summary Steam-assisted gravity drainage (SAGD) is a widely tested method for producing bitumen from oil sands (tar sands). Several analytical treatments of the basic process have been reported. In a typical model, the focus is on bitumen drainage ahead of an advancing heat front. In a few cases, a steady expression for bitumen-drainage rate is obtained. This has been modified by several investigators to include other effects. In all cases, the bitumen rate is obtained with no recourse to the steam-injection rate, which is worked out after the fact. The treatment of time dependence, in a few models, is tenuous, building it in partly by use of experimental data. In this work, the SAGD process is considered to develop during two stages: steam-chamber rise (or unsteady stage) and sideways-expansion (or steady stage). The sideways-expansion phase is modeled by two different approaches: constant volumetric displacement (CVD) and constant heat injection (CHI). In the transient-steam-chamber-rise stage of SAGD, initially there is no heat ahead of the rising front, but as the front rises with time, heat accumulates ahead of the front. In the sideways-spreading stage, there is a dynamic equilibrium situation. The accumulated heat ahead of the front plays a crucial role in this phase of SAGD modeling to find the advancing-front velocity. There is a reciprocal relation between the advancing-front velocity and the amount of stored heat ahead of the front. Higher front velocity leads to lower heat accumulation ahead of the front for mobilizing oil ahead and making it drain. By considering the equilibrium situation for thermal-recovery methods with a dominant-gravity-drainage driving force, the advancing-front velocity is responsible for heat accumulation ahead of the front, and, in turn, this heated oil drains away and is responsible for advancing the front. Therefore, the key point in the modeling is to determine the advancing-front movement that satisfies heat and mass balances over the system under equilibrium. In the CVD model, we postulate that the front movement is such that the steam-chamber growth is constant; that is, the oil-production rate is constant over time. In this work, it is shown that to obtain a constant oil-production rate from a mass balance, the injected heat has to be increased to compensate for the heat loss to the overburden and the growing accumulated heat ahead of the front caused by interface extension and decreasing front velocity. In the CHI model, heat is injected at a constant rate into the system, which provides heat for the growing steam-chamber size, increasing heat loss to the overburden, and heat flow by conduction ahead of the front. In this model, we are computing the front velocity that satisfies heat balance and mass balance for a constant heat-injection rate. Decreasing steam-chamber velocity with time from this model leads to decreasing oil-production rate. The modeling of the SAGD process in this work is different from that in previous works because it is believed that the steam-chamber velocity is the key point in SAGD modeling. In the CVD model, a constant maximum steam-chamber velocity is derived that gives a constant oil-production rate with a better agreement with field data. In the CHI approach, steam-chamber velocity, and hence the oil-production rate, is decreasing with time (strongly affected by increasing heat loss to the overburden).


Author(s):  
Cui Wang ◽  
Zhiwei Ma ◽  
Juliana Y. Leung ◽  
Stefan D. Zanon

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.


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