Empirically informed convolutional neural network model for logging curve calibration

Environmental calibration of logging curves is critical for petrophysical interpretation and sweet spot characterization. Well-bore 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.