No abstract

Piezocone Penetration Test (CPTu) is widely used in offshore projects to obtain soil parameters, such as the undrained shear strength. Due to depth limitations to perform this test, it is common to obtain data until around 40m when the conductor installation process would require at least double the depth. The present work uses extrapolation techniques based on analytic and heuristic approaches to estimate data beyond the depth domain of CPTu tests. Design of the conductor casing is highly dependent on soil properties, since it serves as a foundation element of the well. Estimation of the soil parameters is based on deepwater CPTu data from Brazilian offshore basins. Three analytical approaches are used in this study (linear and non-linear regressions: second and third degrees). Moreover, Artificial Neural Networks (ANN) (dense, convolutional and recurrent networks) are also employed to predict the soil behavior. Methodologies were applied, validated and compared to evaluate their capability to accurately estimate the undrained shear strength. Python subroutines were developed and applied to sets of homogeneous and heterogeneous data from CPTu tests. The undrained shear strength was estimated beyond the test domain until the depth of interest to the conductor casing design around 80m. For this purpose, both groups of techniques were validated analyzing the efficiency of the fitting process, the associated error and coefficient of determination of each methodology. From that point on, we compared data from analytical methods and the neural networks application, verifying which technique fits better on the datasets. These methods of estimation of soil properties work as an instrument to support the decision-making process in top-hole drilling operations. The datasets analyzed present different levels of soil heterogeneity and performing the extrapolation analysis brings additive information to understand the soil behavior beyond depths reached by CPTu tests. This contributes to the safety and reliability of conductor casing design and installation. To the authors' knowledge, this analysis is rarely performed in the literature.

In top hole designs, geotechnical characterization is fundamental in the study of the axial load capacity of the soil and its implications on the design of the conductor casing. The present work estimates soil parameters from CPTu (piezocone penetration test) data using geostatistical methods, with evaluation of trends, variograms, and kriging. Results suggest that universal and residual kriging are good tools for soil characterization in top hole section design, considering their capacity to support spatiality of the phenomenon and the mapping of the uncertainties involved.

This investigation analyzes the cost-benefit ratio of the Transformation Field Analysis to compute the elastoplastic behavior of periodically perforated metal sheets. Evaluation of accuracy and computational cost are analyzed by implementing a finite element approach coupled with the Transformation Field Analysis technique for different meshes and finite element orders. Numerical studies are employed to compare Transformation Field Analysis accuracy with standard Finite Element Analysis for elastoplastic analysis of periodically perforated metal sheets. Additionally, experimental data is employed to validate the Transformation Field Analysis results. The Transformation Field Analysis requires calculating the strain concentration and influencing tensors employing the finite element method. The numerical results show the technique's capabilities and favorable scenarios, besides the influence of domain discretization and finite element order.

No abstract