Details

Autor(en) / Beteiligte

• Du, Ruishan
• Chen, Huanyu
• Shang, Fuhua
• Ma, Nan

Titel

A similarity measure recognized by morphological characteristics analysis of well logging curves: application to the knowledge domain of sandstone reservoir

Ist Teil von

• Arabian journal of geosciences, 2020-09, Vol.13 (18), Article 947

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The identification of sandstone contrasting layers is a key step in the depth calibration of sandstone horizons, whose errors can cause large deviations in positioning results, which in turn affect subsequent drilling and production of oil wells. The identification of a sandstone contrast layer is calculated based on distinct response characteristics of well logging curves in the depth range of the layer. The identification of features of a well logging curve is generally made using the axial geological signals centered on the depth, combined with a time series similarity analysis of the data. However, when the local morphological features indicated and described by the curves corresponding to the depth of the sandstone reservoir are distinctly different, there will be differences in the features to obtained. To improve the accuracy in extracting similar features from well logging curves, we propose a similarity measurement method based on morphological characteristics to comprehensively analyze three morphological features of the curve sequence: the segmentation trend, fluctuation amplitude, and depth span. The similarity of the subsequence is measured according to the weighted slope difference of the comparative sequences. The method treats the attributes of similar waveform characteristics as a whole, which can overcome the shortcomings of conventional time series methods in describing the degree of morphological changes of the curves. Results from processing experimental data show that compared with the conventional method, the proposed method can effectively improve the accuracy in extracting the features of well logging curves and identifying the sandstone contrast layer.