Study On The Recognition Method Of Water-Flooded Zone In Thick Oil Reservoirs During High Water Cut Stage

Since long-term water-flooding changes reservoir property in middle or late period of oil field development, the amplitude and shape of logs will show some changes correspondingly. A pattern recognition technology describing the amplitude and shapes of logs is used to improve recognition accuracy for the grades of watered out zones. Based on experiment of polymer-flooding and alkali-surfactant-polymer flooding reservoir samples, we can get experimental data of electrical properties, interval travel time and NMR of the samples with different oil saturation. Accordingly we can study electrical and acoustic properties of polymer-flooding and alkali-surfactant-polymer flooding reservoirs. In accordance with analysis of reservoir property and character of log response for water-flooded zones, parameters describing shapes of logs from thick zones are extracted to keep completeness of shape description in thick zone subdivision. Finally, log-shape parameters, logs, and log interpretation result parameters are picked out, and a new pattern recognition technology is used to determine the grades of water-flooded zones automatically based on process neural network. Mode base for five grades of water-flooded zones is established with 176 samples from seven coring wells to train network. The interpretation result for two wells in Daqing Oilfield indicates that the average interpretation accuracy is 81.3%, and this method can improve evaluation precision of grades of water-flooded zones.