地震测井和VSP测定的速度精度较高，但剖面上的井测定的速度资料很少，不足以控制速度的横向变化。
Although the seismic log and VSP velocity are accuracy, but determined velocity data of Wells on sections are little and insufficient to control the horizontal change of velocity.
资料处理的关键在于根据地震测井资料建立的速度模型，求出校正量。
The key to data processing lies in accurate time corrections which are computed from velocity model made by well shooting data.
岩石孔隙度和地震波速度之间存在着内在联系，利用地震和测井数据反演井间孔隙度值是储层预测的主要内容之一。
Interal relationship exists between porosity and seismic-wave velocity, and inter-well porosity inversion with seismic and logging data is one of the key elements of reservoir prediction.
讨论了利用测井声波时差和地震层速度预测地层压力的方法及其应用。
Logging sonic differential time and seismic interval velocity are used to estimate formation pressure in this paper.
同时，提出了在少量微测井控制下，由电阻率剖面转换地震波速度剖面的非线性高阶多项式拟合算法。
A non-linear higher order polynomial fitting method is also proposed to transform the resistivity profile into seismic velocity profile under the control of a micro-well logging velocities.
通过直接测量井中每一地层的声速度，测井记录和井中地震数据可以更容易地和地表地震数据联系起来。
By directly measuring the acoustic velocity of each formation encountered in a well, the well logs and borehole seismic data can be correlated to surface seismic data more easily.
同时给出了利用地震层速度预测地应力的BP神经网络模型，以在钻前预测井壁稳定性。
Meanwhile, the predicting method of stress is proposed based on BP's neural network by using the seismic interval velocity, in order to predict borehole wall stability.
同时给出了利用地震层速度预测地应力的BP神经网络模型，以在钻前预测井壁稳定性。
Meanwhile, the predicting method of stress is proposed based on BP's neural network by using the seismic interval velocity, in order to predict borehole wall stability.
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