A fast inversion method for multicomponent induction log data

Yu, Liming; Kriegshäuser, Berthold; Fanini, Otto; Xiao, Jiaqi

doi:10.1190/1.1816617

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…Various research has been carried out to solve the inversion problem. Yu developed a fast, robust and hands-off inversion method, and the method describes the layered formation using equal thickness layers [19]. Lu used the singular value decomposition method to perform an inversion algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering

Zhu

Gao

Kong

et al. 2019

Sensors

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Logging while drilling (LWD) plays a crucial role in geo-steering, which can determine the formation boundary and resistivity in real time. In this study, an efficient inversion, which can accurately invert formation information in real time on the basis of fast-forward modeling, is presented. In forward modeling, the Gauss–Legendre quadrature combined with the continued fraction method is used to calculate the response of the LWD instrument in a layered formation. In inversion modeling, the Levenberg–Marquardt (LM) algorithm, combined with the line search method of the Armijo criterion, are used to minimize the cost function, and a constraint algorithm is added to ensure the stability of the inversion. A positive and negative sign is added to the distance parameter to determine whether the LWD instrument is located above or below the formation boundary. We have carried out a series of experiments to verify the accuracy of the inversion. The experimental results suggest that the forward algorithm can make the infinite integral of the Bessel function rapidly converge, and accurately obtain the response of the LWD instrument in a layered formation. The inversion can accurately determine the formation resistivity and boundary in real time. This is significant for geological exploration.

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Section: Introductionmentioning

confidence: 99%

Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering

Zhu

Gao

Kong

et al. 2019

Sensors

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show abstract

“…Nowadays, most inversion algorithms are based on one-dimensional (1-D) modeling for the best efficiency since the inversion process requires carrying out the forward modeling repeatedly and thus is usually time consuming [3]. Yu et al developed an 1-D inversion algorithm based on turbo boosting proposed by Hakvoort [4]. This method describes layered formation using equally thick thin layers with known relative dipping angle and azimuthal angle.…”

Section: Introductionmentioning

confidence: 99%

1-D Inversion of Triaxial Induction Logging in Layered Anisotropic Formation

Zhang¹,

Yuan²,

Liu³

2012

PIER B

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Abstract-In this paper, we present a one-dimensional (1-D) inversion algorithm for triaxial induction logging tools in multi-layered transverse isotropic (TI) formation. A non-linear least-square model based on Gauss-Newton algorithm is used in the inversion. Cholesky factorization is implemented to improve the stability and the reliability of the inversion. Zero-D inversion is conducted at the center of each layer to provide a reasonable initial guess for the best efficiency of the inversion procedure. Cross components are used to provide sufficient information for determining the boundaries in the initial guess. It will be illustrated that using all the nine components of the conductivity/resistivity yield more reliable inversion results and even faster convergence than using only the diagonal components. The resultant algorithm can be used to obtain various geophysical parameters such as layer boundaries, horizontal and vertical resistivity, dipping angle and rotation angle etc. from triaxial logging data automatically without any priori information. Several synthetic examples are presented to demonstrate the capability and reliability of the inversion algorithm.

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“…With the exception of [1], development of inversion algorithms for triaxial induction logging measurements has been focused to predominantly one-or, to a lesser extent, two-dimensional models [3,5,13,15,20,22,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Data-Adaptive Resolution Method for the Parametric Three-Dimensional Inversion of Triaxial Borehole Electromagnetic Measurements

Alpak

Torres‐Verdín²

2010

PIER B

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We develop a new adaptive inversion procedure: Dataadaptive Resolution Inversion (DRI) method, which eliminates the need of selecting a parameterization prior to inversion. Instead, one performs a hierarchical search for the correct parameterization while solving a sequence of inverse problems with an increasing dimension of parameterization. A parsimonious approach to inverse problems usually involves the application of the same refinement consistently over the complete spatial domain. Such an approach may lead to over-parameterization, subsequently, to unrealistic conductivity estimates and excessive computational work. With DRI, the new parameterization at an arbitrary stage of inversion sequence is allocated such that new degrees of freedom are not necessarily introduced all over the spatial domain of the problem. The aim is to allocate new degrees of freedom only where it is warranted by the available data. Inversion results confirm that DRI is robust and efficient for multiparameter inversion of multicomponent borehole electromagnetic measurements.

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A fast inversion method for multicomponent induction log data

Cited by 11 publications

References 2 publications

Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering

Application of Logging While Drilling Tool in Formation Boundary Detection and Geo-steering

1-D Inversion of Triaxial Induction Logging in Layered Anisotropic Formation

Data-Adaptive Resolution Method for the Parametric Three-Dimensional Inversion of Triaxial Borehole Electromagnetic Measurements

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