Evaluation Techniques for Oil Gas Reservoir Based on Artificial Neural Networks Techniques
By using BP artificial nerve network’s error reversion transmission. Summing up various data of comprehensive logging can solve the problem of low accurate rate for identifying oil,gas,water zones. The software provides nerve network reservoir interpretation model by studying and training the initial data of tested oil. Practice proves the overall coincidence rate of interpretation reaches 97％. It can more efficiently reflects logging technique’s advantage of wellsite quick evaluation oil,gas,water zones. The application in this technique improves the level of logging data interpretation and evaluation.
artificial neural Networks Oil Gas and Water Layer Mud Logging Identification
Hong Yan Pan Hong He
Department of Computer Sciences Tianjin Broadcast and TV University Tianjin,China Tianjin Key Laboratory for Control Theory and Application in Complicated Systems Tianjin University
国际会议
香港
英文
28-31
2010-08-17（万方平台首次上网日期，不代表论文的发表时间）