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New Type Curves for Modeling Productivity of Horizontal Well With Negative Skin Factors
- Ren-Shi Nie (Southwest Petroleum University, China) | Yong-Lu Jia (Southwest Petroleum University, China) | Yingfeng Meng (Southwest Petroleum University, China) | Yongheng Wang (Hua-Bei branch, Sinopec) | Jian-Min Yuan (Hua-Bei branch, Sinopec) | Wei-Feng Xu (Hua-Bei branch, Sinopec)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2012
- Document Type
- Journal Paper
- 486 - 497
- 2012. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.6.4 Drillstem/Well Testing, 4.1.5 Processing Equipment
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- 1,012 since 2007
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This paper first establishes a comprehensive semianalytical model for a horizontal well in a homogeneous, dual- or triple-porosity reservoir. By taking a negative-skin approach, including Laplace transform, separation of variables, and inverse Laplace transform, we obtain the solutions in a unified formula for both constant-rate production and constant-wellbore-pressure production. In the model, we consider a set of rich boundary conditions, including infinite reservoir and closed or constant-pressure boundary conditions for the top, bottom, and side of the bounded reservoir. Consequently, we plot a series of new standard log-log type curves and make a clear observation of different flow periods (e.g., early radial flow, hemiradial flow, linear flow, and late pseudoradial flow) from negative-skin type curves. We make a comparison between the solutions using the negative-skin approach and conventional solutions, which shows that there are some small differences between the two methods. We also analyze the type-curve characteristics for horizontal-well production in dual- and triple-porosity reservoirs, which shows that the derivative curves are V-shaped for dual media and W-shaped for triple media. Finally, we perform well-test interpretations for a horizontal well with positive skin and a horizontal well with negative skin and bottomwater, which shows that our new model performs well against the real field data.
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