Ensuring the integrity of wellbore cement is critical for the environmental protection, safety, and economic viability of oil, gas, geothermal, and Carbon Capture and Storage (CCS) operations. Acoustic logging is the most commonly used evaluation method, and interpreting its data requires significant expertise, often informs high-stakes decisions. Machine Learning (ML) has been used to help the data interpretation, but they are heavily reliant on meticulous feature engineering—a labor-intensive and expertise-driven task. Moreover, ML methods cannot tell which portions of input acoustic data are most important in the cement evaluation task. This level of analysis is vital, as it can significantly inform and improve future strategies for acoustic logging data collection and signal processing, ultimately improving the cement evaluation results. To address these challenges, we present a generalized workflow. First, we convert Variable Density Log (VDL) data into images using the Continuous Wavelet Transform (CWT). Then, we apply transfer learning for image classification to enhance the classification of wellbore cement isolation. In transfer learning stage, the images are processed using several pre-trained image classifiers—Xception, VGG16, MobileNetV2, and ResNet50—originally trained on the extensive ImageNet database containing over 14 million images. To adapt these models for our specific task, we added a global average pooling layer to reduce feature map dimensionality, followed by a fully connected layer with a Rectified Linear Unit (ReLU) activation function to introduce non-linearity and enhance learning capability. We replaced the original final layer of each classifier with a new three-neuron layer using softmax activation, tailored for multi-class classification. We preserved the general characteristics learned from the ImageNet dataset by freezing all original layers except for the newly added ones and compiled the models using the Adam optimizer. The performance of these adapted models was evaluated based on accuracy, loss, and F1 score. We applied this workflow to two distinct datasets: the first from a Norwegian wellbore and the second from a hydrocarbon well in China. In the Norwegian case, the Xception model achieved a classification accuracy of 97.4%, which was further refined to 99.0% through the implementation of Gradient-weighted Class Activation Mapping (Grad-CAM), revealing critical frequency ranges that traditional ML methods could not identify. In the Chinese case, employing the same models and workflow, the MobileNetV2 algorithm demonstrated exceptional performance, achieving a 99.6% accuracy in predicting cement isolation. In addition to achieving high accuracy, our results reveal that the VDL frequency content between 20 and 25 kHz is more critical for cement evaluation in the investigated wells than other frequency ranges, a level of analysis unattainable with traditional ML methods. Furthermore, our approach eliminates the need for deep knowledge in feature engineering, typically requisite in traditional ML, and operates effectively without any feature engineering. These results underscore the efficacy of our approach across diverse geological settings and highlight the potential of transfer learning to revolutionize cement evaluation automation, inspiring its broader application in the industry.
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Just Published
Oct 8, 2024
Ben Cox + 3
Open Access