Artificial Intelligence Method of Flow Unit Division Based on Waveform Clustering: A Case Study on Zhetybay Oil Field, South Mangyshalk Basin, Kazakhstan

The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.