APPLICATION OF PRE-STACK JOINT INVERSION TO MID-DEEP RESERVOIR PREDICTION IN BOHAI BAY
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摘要:
渤海A油田中深层砂泥纵波阻抗叠置严重，常规叠后反演手段无法有效预测储层，为此提出叠前联合反演技术思路：通过实钻井数据开展储层敏感性分析，采用坐标旋转技术构建储层识别因子并作为目标曲线，以叠前同时反演获得的纵横波阻抗、密度等弹性参数体作为输入，采用多属性神经网络反演获得全区储层识别因子数据体，从而对砂岩储层厚度及空间展布进行准确预测。该技术获得的目标储层预测结果在纵向与已钻井吻合良好，横向展布符合地质沉积规律认识，为后续钻井提供了重要的参考依据，具有广阔的应用前景。
Abstract:The sandstone and shale’s p-impedances are seriously overlapped in the A oilfield of the Bohai Bay and thus conventional ways using post-stack seismic attribute and inversion cannot effectively predict sand reservoirs. Here we propose a pre-stack joint inversion technology for reservoir prediction to solve this problem. Firstly, reservoir sensitivity analysis is carried out based on logging data. Then, we obtain a sand reservoir identification factor using coordinate rotation technique and make it a target curve. Finally, we obtain a reservoir identification factor volume of the whole area to describe sand reservoir thickness and lateral distribution through multiple attribute neural network inversion with p-impedance, s-impedance, density, and ratio of p-wave and s-wave data acquired by fine pre-stack simultaneous inversion. Actual application shows that this technique can acquire a good reservoir prediction result, which corresponds well with drilling wells vertically and conforms to geological rule horizontally. The result can play a very important role in subsequent drilling and has a broad application prospect.
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