Application of Mean Square Deviation Method to the Target Selection of Acid Fracturing for Fissure-Cavern Carbonate Reservoirs

Abstract:

The Ordovician fissure-cavern carbonate reservoirs of Tarim Basin have relatively large production thickness, long completion intervals and serious heterogeneity problems, former studies about physical parameters for fractured intervals of acid fracturing cannot be well presented through mean algorithm method. This paper uses mean square deviation method to acquire the values of the main influencing factors of acid fracturing, then optimizes the physical characteristics of the fractured intervals. The BP neural network simulation has been adopted to get the optimal BP neural network structure model. In Tarim, this study has simulated 20 wells before acid fracturing using the decision software which compiled by the optimum neural network construction. Comparative analysis has been made through application examples, the coincident rate of mean algorithm is 75%, compared with the 90% of mean square deviation method. Therefore, during the target selection for acid fracturing of heterogeneous carbonate reservoirs with long open hole intervals, it’s significant to use mean square deviation method to optimize physical parameters of reservoirs.

• Periodical: Advanced Materials Research (Volumes 1092-1093)
• Pages: 1356-1360
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1092-1093.1356
• Online since: March 2015
• Authors: Ya Hong Wu*, Li Wang, Hao Qing Fang, Xi Shan Tian, Qiang Bo Mi, Chun Yue Li, Bo Yuan Li
• Keywords: Acid Fracturing, BP Neural Network, Carbonate Reservoir, Mean Square Deviation Method, Physical Parameter

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