钻井岩性实时识别方法研究

地层岩性的实时识别对及时调整钻井参数、有效控制井眼轨迹具有十分重要的作用。以录井资料为基础识别地层岩性,必须综合考虑钻井操作参数、水力参数以及钻头磨损状态的影响,而随钻过程中,就目前的技术还不能够实时测量钻头磨损状态。根据BP神经网络原理,建立了岩性识别双重神经网络模型。第1个神经网络用来在已知钻头磨损状态条件下,识别所钻地层岩性;第2个神经网络用来在已知地层岩性条件下,预测钻头磨损状态。2个神经网络通过钻头磨损状态参数连接起来,选取样本数据分别对2个神经网络进行训练,并结合随钻录井数据,根据岩性识别流程图对岩性进行实时识别。应用该模型在新疆油田进行了岩性实时识别试验,识别结果与测井解释结果相比,符合率达85%。应用结果表明该模型具有一定的合理性和实用性。

Method for Real-Time Lithology Identifying While Drilling

It is very important for the drilling engineer and geologist to real-timely identify formation lithology while drilling. Based on the mud logging data to identify lithology, the effect of drilling operational parameters, hydraulic factors and bit wear should be comprehensively considered. It is not practical to real-timely measure bit wear while drilling. According to BP neural network theory, dual neural network model for lithology identification was established. The first neural network was used to identify lithology under the condition of known bit wear. The second neural network was used to predict bit wear under the condition of given lithology. The two neural networks were linked by the state of bit wear. Sample data were selected to train the two neural networks respectively. Combined with mud logging data, lithology was predicted according to the process of lithology identification. This model was verified in xin jiang oilfield. Compared with geological explanation of logging data, the prediction result is better and the coincidence rate can reach about 85%. Field application result shows that this model is effective and feasible.