基于随机粒子群的Newton-SVD方法的电测井反演

Inversion of Electrical Logging by Newton-SVD with Stochastic Particle Swarm

提出一种基于随机粒子群的Newton-SVD方法对双侧向测井进行双参数反演(原状地层电阻率,侵入带半径),运用随机粒子群方法得到双侧向测井反演的一个较为合理的初始值,结合Newton-SVD方法进行双侧向测井反演,提高测井反演的稳定性、可靠性,给出一种设定测井反演初始值的方法,通过对加入噪声的理想模型和实际资料进行处理表明,该方法可有效、准确地消除复杂测井环境的影响,得到真实的地层参数.

We propose a method to invert two parameters （ virgin zone resistivity, invaded zone） of dual lateral logging which is based on Newton-SVD of particle swarm. Reasonable initial values are selected by random particle swarm method. Newton-SVD is applied to invert dual lateral logging data. It improves stability and reliability of logging inversion greatly, and offers a radical way for setting initial values of inversion. Ideal model with noise and practical logging data are compared. It shows that the method eliminates effects of complex logging environments effectively and offer true formation parameters.