Отправлено: 02.02.11 13:57. Заголовок: Methods and models for rapidly identifying and finely evaluating the ultra-low permeability oil laye

Methods and models for rapidly identifying and finely evaluating the ultra-low permeability oil layer
Xiong-yan Li1,2, Hong-qi Li1,2, Yu-jiang Shi3, Jin-yu Zhou3 and Hong-yan Yu1,2

Affiliations
1 State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing 102249, People's Republic of China
2 Key Laboratory of Earth Prospecting and Information Technology, China University of Petroleum, Beijing 102249, People's Republic of China
3 Exploration and Development Institute, Changqing Petroleum Company, Xi'an, Shanxi 710021, People's Republic of China
E-mail wangliaoziji@126.com
Journal of Geophysics and Engineering Create an alert RSS this journal
Issue
Volume 8, Number 1

Xiong-yan Li et al 2011 J. Geophys. Eng. 8 13
doi: 10.1088/1742-2132/8/1/003


Due to the low signal-to-noise ratios of logging information, the complexities of the petrophysical property and percolation mechanism as well as the sensitivities of fracture projects, it is difficult to rapidly identify the ultra-low permeability oil layer and predict its productivity after fracturing.
Therefore, the ideas of combining the petrophysical mechanism with the statistical analysis of log response characteristics have emerged. First of all, with the help of the neuron nonlinear function Sigmoid, the PRI is constructed and the water productivity is fitted.
The accuracy of identification chart is 95.95% based on the PRI and the water productivity. The PRI index and differential analysis method are applied to identifying the ultra-low permeability oil layer, with an accuracy of 83.33%. Then the productivity index method is utilized to predict the productivity of the ultra-low permeability oil layer with non-Darcy flow after fracturing. A series of sensitive factors is built up.
The sensitive feature subsets are selected respectively from the parameter sets of high productivity layers and low-to-moderate productivity layers. The productivity indices are fitted by the sensitive feature subsets. The method is employed to predict the productive capacity of 20 key wells in the region of interest.
The relevance of the predictive productivity and the actual productivity is about 0.98, and the average absolute error is 1.95 tons.
The identification method and productivity model can meet the actual production demand and achieve the objective of taking advantage of logging information to rapidly evaluate the ultra-low permeability oil layer.


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