Prediction Method of Heavy Oil Horizontal Well Cycle Oil Production Based on PCA and Gradient Boosting Decision Tree

Author(s):  
Hongbo Liu ◽  
Jianwei Gu ◽  
Yaxuan Wang ◽  
Zhiyong Wei
2013 ◽  
Author(s):  
Jun Lin ◽  
Cheng Jiang ◽  
Fugang Lu ◽  
Yong Zhang ◽  
Yongheng Chen ◽  
...  

SPE Journal ◽  
2011 ◽  
Vol 16 (03) ◽  
pp. 494-502 ◽  
Author(s):  
Z.. Wu ◽  
S.. Vasantharajan ◽  
M.. El-Mandouh ◽  
P.V.. V. Suryanarayana

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.


Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8161
Author(s):  
Zehao Xie ◽  
Qihong Feng ◽  
Jiyuan Zhang ◽  
Xiaoxuan Shao ◽  
Xianmin Zhang ◽  
...  

Conformance control is an effective method to enhance heavy oil recovery for cyclic-steam-stimulated horizontal wells. The numerical simulation technique is frequently used prior to field applications to evaluate the incremental oil production with conformance control in order to ensure cost-efficiency. However, conventional numerical simulations require the use of specific thermal numerical simulators that are usually expensive and computationally inefficient. This paper proposed the use of the extreme gradient boosting (XGBoost) trees to estimate the incremental oil production of conformance control with N2-foam and gel for cyclic-steam-stimulated horizontal wells. A database consisting of 1000 data points was constructed using numerical simulations based on the geological and fluid properties of the heavy oil reservoir in the Chunfeng Oilfield, which was then used for training and validating the XGBoost model. Results show that the XGBoost model is capable of estimating the incremental oil production with relatively high accuracy. The mean absolute errors (MAEs), mean relative errors (MRE) and correlation coefficients are 12.37/80.89 t, 0.09%/0.059% and 0.99/0.98 for the training/validation sets, respectively. The validity of the prediction model was further confirmed by comparison with numerical simulations for six real production wells in the Chunfeng Oilfield. The permutation indices (PI) based on the XGBoost model indicate that net to gross ratio (NTG) and the cumulative injection of the plugging agent exerts the most significant effects on the enhanced oil production. The proposed method can be easily transferred to other heavy oil reservoirs, provided efficient training data are available.


2012 ◽  
Author(s):  
Jaime Cuadros ◽  
Andrey Salamanca ◽  
Nestor Raul Amado ◽  
Guillermo Cuadros ◽  
Esteban Rojas ◽  
...  

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