scholarly journals Algorithm for inversion of resistivity logging-while-drilling data in 2D pixel-based model

2021 ◽  
Vol 2099 (1) ◽  
pp. 012040
Author(s):  
A V Bondarenko ◽  
D Yu Kushnir ◽  
N N Velker ◽  
G V Dyatlov

Abstract Multi-frequency and multi-component extra-deep azimuthal resistivity measurements with depth of investigation of a few tens of meters provide advanced possibilities for mapping of complex reservoir structures. Inversion of the induction measurements set becomes an important technical problem. We present a regularized Levenberg–Marquardt algorithm for inversion of resistivity measurements in a 2D environment model with pixel-based resistivity distribution. The cornerstone of the approach is an efficient parallel algorithm for computation of resistivity tool signals and its derivatives with respect to the pixel conductivities using volume integral equation method. Numerical tests of the suggested approach demonstrate its feasibility for near real time inversion.

2021 ◽  
Author(s):  
Danil Andreevich Nemushchenko ◽  
Pavel Vladimirovich Shpakov ◽  
Petr Valerievich Bybin ◽  
Kirill Viktorovich Ronzhin ◽  
Mikhail Vladimirovich Sviridov

Abstract The article describes the application of a new stochastic inversion of the deep-azimuthal resistivity data, independent from the tool vendor. The new model was performed on the data from several wells of the PAO «Novatek», that were drilled using deep-azimuthal resistivity tools of two service companies represented in the global oilfield services market. This technology allows to respond in a timely manner when the well approaches the boundaries with contrasting resistivity properties and to avoid exit to unproductive zones. Nowadays, the azimuthal resistivity data is the method with the highest penetration depth for the geosteering in real time. Stochastic inversion is a special mathematical algorithm based on the statistical Monte Carlo method to process the readings of resistivity while drilling in real time and provide a geoelectrical model for making informed decisions when placing horizontal and deviated wells. Until recently, there was no unified approach to calculate stochastic inversion, which allows to perform calculations for various tools. Deep-azimuthal resistivity logging tool vendors have developed their own approaches. This article presents a method for calculating stochastic inversion. This approach was never applied for this kind of azimuthal resistivity data. Additionally, it does not depend on the tool vendor, therefore, allows to compare the data from various tools using a single approach.


2019 ◽  
Vol 16 (06) ◽  
pp. 1840025
Author(s):  
Jungki Lee ◽  
Hogwan Jeong

The parallel volume integral equation method (PVIEM) is applied for the analysis of two-dimensional elastic wave scattering problems in an unbounded isotropic solid containing various types of multiple multilayered anisotropic inclusions. It should be noted that the volume integral equation method (VIEM) does not require the use of the Green’s function for the anisotropic inclusion — only the Green’s function for the unbounded isotropic matrix is needed. A detailed analysis of the SH wave scattering problem is presented for various types of multiple multilayered orthotropic inclusions. Numerical results are presented for the elastic fields at the interfaces for square and hexagonal packing arrays of various types of multilayered orthotropic inclusions in a broad frequency range of practical interest. Standard parallel programming was used to speed up computation in the VIEM. The PVIEM enables us to investigate the effects of single/multiple scattering, fiber packing type, fiber volume fraction, single/multiple layer(s), multilayer’s shapes and geometry, isotropy/anisotropy, and softness/hardness of various types of multiple multilayered anisotropic inclusions on displacements and stresses at the interfaces of the inclusions and far-field scattering patterns. Also, powerful capabilities of the PVIEM for the analysis of general two-dimensional multiple scattering problems are investigated.


2019 ◽  
Vol 68 (2) ◽  
pp. 709-720 ◽  
Author(s):  
Decheng Hong ◽  
Hu Li ◽  
Wei‐Feng Huang ◽  
Hongmei Liu

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