Using geophysical log data to predict the fracture density in a claystone host rock for storing high-level nuclear waste

Tóth, Emese; Hrabovszki, Ervin; Tóth, Tivadar M.

doi:10.1007/s40328-023-00407-w

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Due to its resolution and the nature of the operation that uses a transmitter and receiver, resistivity is one of the more valuable tools used to detect fracture zones, especially in tight formations and low-permeability reservoirs such as crystalline rock bodies [13,16,26]. During drilling, fractured rocks surrounding the well will be filled with mud or mud filtrate, usually recorded by the resistivity logs [1,33].…”

Section: Well-log Responses Towards Fracture Characteristicsmentioning

confidence: 99%

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Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

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Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

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Section: Well-log Responses Towards Fracture Characteristicsmentioning

confidence: 99%

“…For instance, ref. [26] implemented multiple linear regression to predict fracture density (FD) on a claystone formation. Another example is wavelet transform, which is one of the methods that have been studied extensively in the literature.…”

Section: Introductionmentioning

confidence: 99%

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

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Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks

Hasan,

M. Tóth

2024

J Petrol Explor Prod Technol

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Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data; for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms.

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Quantitative estimation of three-dimensional fracture density: Insights from the stereological relationship between borehole and universal elliptical DFN

Xiao,

Zhang,

Ren

et al. 2025

Engineering Geology

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Using geophysical log data to predict the fracture density in a claystone host rock for storing high-level nuclear waste

Cited by 5 publications

References 34 publications

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks

Quantitative estimation of three-dimensional fracture density: Insights from the stereological relationship between borehole and universal elliptical DFN

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