Inflow Performance of a Cyclic-Steam-Stimulated Horizontal Well Under the Influence of Gravity Drainage

Summary In this paper, we present a new, semianalytical gravity-drainage model to predict the oil production of a cyclic-steam-stimulated horizontal well. The underlying assumption is that the cyclic steam injection creates a cylindrical steam chamber in the upper area of the well. Condensed water and heated oil in the chamber are driven by gravity and pressure drawdown toward the well. The heat loss during the soak period and during oil production is estimated under the assumption of vertical and radial conduction. The average temperature change in the chamber during the cycle is calculated using a semianalytical expression. Nonlinear, second-order ordinary differential equations are derived to describe the pressure distribution caused by the two-phase flow in the wellbore. A simple iteration scheme is proposed to solve these equations. The influx of heated oil and condensed water into the horizontal wellbore is calculated under the assumption of steady-state radial flow. The solution from the semianalytical formulation is compared against the results from a commercial thermal simulator for an example problem. It is shown that the model results are in good agreement with those obtained from reservoir simulation. Sensitivity studies for optimization of wellbore length, gravity drainage, bottomhole pressure, and steam-injection rate are conducted with the model. Results indicate that the proposed model can be used in the optimization of individual-well performance in cyclic-steam-injection heavy-oil development. The semianalytical thermal model presented in this work can offer an attractive alternative to numerical simulation for planning heavy-oil field development.

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

Energy Exploration & Exploitation ◽

10.1177/0144598719897178 ◽

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.

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Expansion of Data Analytics for Optimizing Steamflood In Mukhaizna Heavy Oil Field

10.2118/207680-ms ◽

2021 ◽

Author(s):

Mohammed Al Asimi ◽

Nasar Al Qasabi ◽

Duc Le ◽

Yuchen Zhang ◽

Di Zhu ◽

...

Keyword(s):

Heavy Oil ◽

Data Analytics ◽

Oil Production ◽

Steam Injection ◽

Lessons Learned ◽

Oil Field ◽

Production Performance ◽

Optimization Process ◽

Successful Implementation ◽

Full Field

Abstract After successful implementation of data analytics for steamflood optimization at the Mukhaizna heavy oil field in Oman late 2018, Occidental expanded the project to two additional areas with a total of 626 wells in 2019, followed by full field coverage of more than 3,200 wells in 2020. In 2019, two separate low-fidelity proxy models were built to model the two pilot areas. The models were updated with more features to account for additional reservoir phenomena and a larger scope. On the proxy engine side, speed and robustness were improved, resulting in reduced CPU processing time and lower cost. Because of advancements in software programing and the pilots’ encouraging production performance, full-field coverage was accelerated so the model could support the efforts in optimizing steam injection during the 2020 OPEC+ production cut, not only to comply with allotted quotas, but also to allocate the resources optimally, especially the costly steam. Good improvements have been observed in overall steamflood performance, the models’ capabilities, and the optimization workflow. The steam/oil ratio has been reduced through the increase in oil production in both expanded study areas while keeping the total steam injection volume constant. Overall field steam utilization was improved both during the 2020 OPEC+ production cuts and during the production ramp-up stage afterward. With the continuous improvement in supporting tools and scripts, most of the steam optimization process steps were automated, from preparing, checking, and formatting input data to analyzing, validating, and visualizing the model outputs. Another result of these improvements was the development of a user-friendly web application to manage the model workflow efficiently. This web app greatly improved the process of case submittals, including data preparation and QC, running models (history matching and forecasting), as well as visualization of the entire workflow. In terms of optimization workflow, these improvements resulted in less time spent by the field optimization engineer in updating, refreshing, and generating new model recommendations. It also helped reduce the time spent by the reservoir management team (RMT) to test and validate the new ideas before field implementation. This paper will describe the improvements in the proxy model and the overall optimization process, show the observed oil production increases, and discuss the challenges faced and the lessons learned.

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