基于二次规划的双感应测井反演方法OA北大核心CSTPCD

Based on signal convolutional measurement,true resistivity of zones is acquired through deconvo-lution and regularization,which is very important for the calculation of water saturation of reservoirs by dual induction resistivity inversion.It is not feasible to accurately calculate the deconvolution factor by traditional deconvolution methods,and it is diffcult to select regularization parameters in Tikhonov regularization.The inversion problem was converted into an optimization problem of quadratic programming to be solved,and equation constraints were imposed to guarantee the smoothness of the inversed resistivity curves.Objectives For solving the quadratic programming(QP)problem with equation constraints,Methods a recursive artifi-cial neural network model of QP was constructed in this paper based on neurocomputing theory and a time-dependent nonlinear dynamical system.The improved Euler method was adopted for numerical simulation to obtain the optimal solution and dual solution of the model simultaneously.Results Factual applications on-site showed that the formation resistivity curves were very helpful in determining oil or water zones accu-rately and were more coincident with well testing results compared with raw measured resistivity curves.Conclusions The formation resistivity inversion based on quadratic programming effectively reconstructed the original formation resistivity and had a strong capability to discover and identify oil and gas reservoirs.This method provided significant technical support for increasing oil reserves and production.