欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为有效解决钻井过程中的井壁失稳问题，根据地震和测井信息之间的密切联系，建立了基于地震属性的实时井壁稳定性预测模型。该模型综合利用地震、测井和地质资料，从待钻目标井和已完钻邻井的井旁地震记录中分别提取最优地震属性组合，运用小波神经网络建立已钻井地震属性与测井数据之间的分层映射关系模型，利用当前待钻地层的地震属性并选取相应的映射模型实时预测钻头以下地层的声波和密度测井曲线。基于预测结果结合井壁稳定力学模型计算待钻层段的孔隙压力、坍塌压力和破裂压力，进而预测安全钻井液密度范围。塔里木油田的实际应用表明，该预测模型具有良好的实时操作性能，测井曲线、地应力、孔隙压力、破裂压力和安全钻井液密度范围的预测精度均较高。图5表1参21
英文摘要：
Based on the close relationship between seismic and logging information, a real-time prediction model of borehole stability is established using seismic, logging and geological data to control borehole wall sloughing instability. Firstly, seismic attributes are extracted from borehole-side seismic traces of target wells and drilled offset wells respectively. Then mapping models of relationships between seismic attributes and logging data of various formation intervals in drilled wells are constructed using wavelet neural network. Using the seismic attributes of formation under bit and the corresponding mapping model, the acoustic and density logging data of the current undrilled formation can be predicted. On the basis of the prediction results, the mechanical model of borehole stability is employed to calculate pore pressure, collapse pressure and fracture pressure, thus predicting the safe drilling fluid density range. Practical application in Tarim Oilfield shows that real-time operation performance of the model is excellent and the prediction accuracy of parameters is satisfactory.
同期刊论文项目
同项目期刊论文
期刊信息
位置：