Intelligent evaluation method for cementing quality based on MLPCNN

The quality of cementing is crucial for the production efficiency and lifespan of oil and gas wells. Currently, the most widely used method is acoustic amplitude variable density logging for evaluation. However, the interpretation process is complex, and decisions related to major risks need to be made based on the results of cementing interpretation. Therefore, the evaluation of cementing quality must be undertaken by experienced experts, which is time-consuming and labor-intensive. In order to improve the efficiency of cementing interpretation, we used convolutional neural networks such as VGG and ResNet to automatically interpret cementing quality, but the accuracy was insufficient. Therefore, we proposes a method of parallel connection between multi-layer perceptions and convolutional neural networks (MLP-CNN), where acoustic amplitude data is input into multi-layer perceptions and variable density logging images are input into convolutional neural networks; We modifies the structure of convolutional neural networks by setting convolutional kernels of different sizes to extract information at different scales for features with varying density maps, such as the thickness, brightness, and shape of stripes. We used 9000 data from the Fuyuan block of the Tarim Oilfield for training and validation. The results showed that compared to traditional convolutional networks such as VGG and ResNet, the MLP and CNN parallel networks effectively improved the accuracy of cementing quality recognition, with an evaluation accuracy of 90%. Furthermore, compared to a single scale convolutional kernel, the convolutional neural network algorithm with multiple convolutional kernels of different sizes is more suitable for extracting features from variable density cementing images. We modified the structure of the convolutional neural network and established an MLP-CNN neural network with three convolutional kernels of different sizes, which improved the accuracy by 5% compared to the MLPCNN model with a single convolutional kernel; meanwhile, we compared the time complexity and spatial complexity of seven networks. The findings revealed that the MLP-CNN parallel network efficiently mitigates a substantial number of ineffective convolutions, thereby reducing model computational costs and enhancing computational efficiency. Finally, in order to test the transferability of the model, we used 60000 data from the Manshen and Yueman blocks of the Tarim Oilfield for testing, and the evaluation accuracy reached 89%, indicating a satisfactory migration effect and robust performance of the model.