An iterative inversion of Dual Induction Tool logs from thin-bedded sandy–shaly formations of the Carpathian Foredeep using a modified simulated annealing method

Wilkosz, Michał; Wawrzyniak-Guz, K.

doi:10.1007/s11600-019-00343-w

Cited by 2 publications

(1 citation statement)

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“…To overcome such problems of the linearized inversion methods, optimization techniques utilizing random search have been developed in the past decades. Some of the most commonly used global optimization methods in geophysics are simulated annealing and the genetic algorithm (Sen and Stoffa 2013;Wilkosz and Wawrzyniak-Guz 2019). Particle swarm optimization used in this study can also eliminate the starting model dependence of the inverse problem.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty reduction of interval inversion estimation results using a factor analysis approach

Abordán

Szabó

2020

Int J Geomath

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This paper aims to investigate the impact of the overdetermination (data-tounknowns) ratio on the global inversion of wireline logging data. In the course of the so-called interval inversion method, geophysical data measured in a borehole over a longer depth range is jointly inverted and the depth variation of the investigated petrophysical parameters are expanded into series using Legendre polynomials as basis functions resulting in a highly overdetermined inverse problem. A metaheuristic Particle Swarm Optimization (PSO) approach is applied as a first phase of inversion for decreasing the starting model dependence of the interval inversion procedure. In the subsequent linear inversion steps, by using the measurement error of logging tools and the covariance matrix of the estimated petrophysical parameters, we can quantify the accuracy of the model parameters.The dataset used in this study consists of nuclear, resistivity and sonic logs which are inverted to compute porosity, shale volume and water saturation along the investigated interval. For increasing the data-to-unknowns ratio of the inverse problem, shale volume is estimated separately by a PSO-based factor analysis and fixed as known parameter for the interval inversion process. Since the shale volume has been described as high degree Legendre polynomial, a significant increase of the overdetermination ratio considerably decreases the uncertainty of the remaining model parameters allowing for a more reliable calculation of hydrocarbon content.

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Section: Discussionmentioning

confidence: 99%

Uncertainty reduction of interval inversion estimation results using a factor analysis approach

Abordán

Szabó

2020

Int J Geomath

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Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

Yang

Zhang

et al. 2022

Energy Science & Engineering

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Sulige gas field is the largest natural gas producing area in China. Due to the "water blocking" effect, the gas-water layer significantly influences the development of gas reservoirs. The existence of low-resistivity gas layers in the He 8 member of the Shihezi Formation in the southwestern Sulige gas field makes it challenging to distinguish the gas layers from the gas-water layers using conventional identification methods. To effectively identify the low-resistivity gas layers, their genetic mechanisms are analyzed by studying their lithology and pore structural characteristics based on the well logging and core experimental data. The low-resistivity gas layers are main affected by three causes: (1) electrical conductivity of argillaceous laminae in sandstone,(2) additional conductivity of clay minerals, and (3) high bound water saturation caused by the development of micropores and clay minerals in sandstone. Herein, to effectively distinguish the low-resistivity gas layer from the gas-water layer based on the genetic mechanism of the low-resistivity gas layer, first, a gas-bearing index was constructed to characterize the gas-bearing properties of the reservoir using the neutron logging and density logging curves after eliminating the influence of the dispersed shale. Second, a model to calculate the bound water saturation was constructed by selecting sensitive parameters with respect to the causes of bound water. Then, two plots, namely the gas-bearing index-natural gamma relative value cross-plot and the bound water content-porosity cross-plots, were constructed using the gas testing data, and the identification standard of the low-resistivity gas and gas-water layers was established. The interactive identification of the two cross-plots effectively distinguished the low-resistivity gas layer from the gaswater layer, thereby providing a basis for understanding the distribution of gas and water in the southwestern Sulige gas field, which may guide further exploration and the deployment of the development well pattern.

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An iterative inversion of Dual Induction Tool logs from thin-bedded sandy–shaly formations of the Carpathian Foredeep using a modified simulated annealing method

Cited by 2 publications

References 10 publications

Uncertainty reduction of interval inversion estimation results using a factor analysis approach

Uncertainty reduction of interval inversion estimation results using a factor analysis approach

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

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