Empirically informed convolutional neural network model for logging curve calibration

Abstract

Environmental calibration of logging curves is critical for petrophysical interpretation and sweet spot characterization. Wellbore failure frequently occurs in clay-rich shale rocks during drilling, leading to biased logging interpretation and uncertainty. To reduce the biased correction or erroneous decision making in the interpreter-dominated logging curve calibration process, we develop an empirically informed convolutional neural network (EiCNN) logging curve correction strategy to calibrate the borehole failure-induced logging curve abnormity more accurately. The EiCNN method, together with high-quality logging curves as labeled samples, provides a nonlinear mapping between input logging curves and calibrations for the distorted curves. The EiCNN method completely alleviates biased correction or decision making by the interpreter-dominated method. It has a strong generalization ability, using many empirically interpreted high-quality data as input samples. The field validation wells demonstrate that the EiCNN model can precisely correct the distorted logging curves of mudstone segments with a correlation coefficient of >0.95. Moreover, the validation and test wells illustrate that the EiCNN method is capable of precisely correcting logging curves of interlayer mudstone, implying that the EiCNN method, to a certain degree, can also accurately perform environmental correction of logging curves from thin mudstone layers.