基于神经网络算法的人工蜂群算法在地层岩性识别中的应用OA
Application of Artificial Bee Colony Algorithm Based on Neural Network in Lithologic Identification
合理确定地层岩性对合理选择钻头类型、快速建立岩性剖面、及时发现油气层和卡准取心层位有着重要意义。以录井资料为基础，结合已钻井的测井资料，根据BP神经网络和人工蜂群算法，建立基于BP神经网络算法的人工蜂群算法模型。应用该模型在青海油田某区块进行地层岩性随钻识别试验，试验结果与测井资料解释结果相比，符合率可达91．35％。
During the drilling process, it is very important to identify formation lithology in order to select bit types, quickly establish the lithology section, discover the oil and gas layers in time, and locate the coring layer exactly. Based on the mud logging data and combining with the logging data of the drilled wells, an artificial bee colony algorithm based on BP neural network is established for lithology identification. This model was verified in Qinghai o…查看全部>>
张庆丰;周子杰;杨贵康;张密华
青海油田公司边远油田开发公司,敦煌816400青海油田公司边远油田开发公司,敦煌816400西南石油大学,成都610500青海油田公司边远油田开发公司,敦煌816400
石油、天然气工程
BP神经网络人工蜂群算法岩性测井资料
BP neural networkartificial bee colony algorithmlithologylogging data
《重庆科技学院学报：自然科学版》 2011 (5)
46-48,3
青海油田公司边远油田开发公司项目（QHYTBY2006-001）
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