Shear wave velocity information is valuable in many aspects of seismic exploration and characterization of reservoirs. However, shear wave logs are not always available in the interval of interest due to cost and time‐saving purposes. In this study, we present a tailored supervised learning approach to estimate shear wave velocity from well‐log measurements in the Lower Cretaceous succession of the Valdemar and Boje fields in the Danish North Sea. Our objective is to investigate the performance of four supervised learning regression models (linear, random forest, support vector and multi‐layer perceptron). A limited well‐log data set from six wells is used for training and testing the supervised learning models. A set of well data containing normalized gamma ray, compressional wave velocity, neutron porosity and medium resistivity logs gave reasonable shear wave velocity estimates in the test wells with root‐mean‐square error scores within the range of other published studies. Based on limited input data and complex geology, the multi‐layer perceptron was the most successful model in predicting the reservoir sections of the test wells. However, all models lacked stability in the overburden zones. Lastly, re‐training the multi‐layer perceptron on the six wells to predict missing shear wave velocity in a nearby well showed promising results for further reservoir characterization. The obtained results can yield useful input into, for example, seismic pre‐stack inversion, amplitude versus offset analysis and rock physics analysis.
A quantitative seismic interpretation study is presented for the Lower Cretaceous Tuxen reservoir in the Valdemar Field, which is associated with heterogeneous and complex geology. Our objective is to better outline the reservoir quality variations of the Tuxen reservoir across the Valdemar Field. Seismic pre-stack data and well logs from two appraisal wells forms the basis of this study. The workflow used includes seismic and rock physics forward modelling, attribute analysis, a coloured inversion and a Bayesian pre-stack inversion for litho-fluid classification. Based on log data, the rock physics properties of the Tuxen interval reveals that the seismic signal is more governed by porosity than water saturation changes at near-offset (or small-angle). The coloured and Bayesian inversion results were generally consistent with well-log observations at the reservoir level and conformed to interpreted horizons. Although the available data has some limitations and the geological setting is complex, the results implied more promising reservoir quality in some areas than others. Hence, the results may offer useful information for delineating the best reservoir zones for further field development and selecting appropriate production strategies.
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