A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network

Abstract

The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.

Cementing Quality Prediction in the Shunbei Block Based on Genetic Algorithm and Support Vector Regression

“…Pearson correlation, which is a method that measures the degree of correlation between two variables X and Y, was adopted to verify the example. The Pearson correlation coefficient between X and Y is defined as the quotient of the covariance and standard deviation of the two variables, as shown by Equation (7). The Pearson correlation coefficient ranges between −1 and 1, where 1 suggests complete positive correlation of variables, 0 indicates irrelevance, and −1 means complete negative correlation [34].…”

“…Viggen et al proposed an assisted cement log interpretation tool based on supervised ML, and the implemented tool, which can be used for cementing quality evaluation, allows the interpretation of logging results to be automated [6]. Fang Chunfei et al proposed a multi-scale perceptual convolutional neural network with kernels of different sizes, which is suitable for recognizing logging variable density images and evaluating cementing quality [7]. Souza et al combined an experimental setup simulating several well conditions with machine learning as a diagnostic tool to demonstrate that support vectors are more suitable than other vectors for cementing quality evaluation [8].…”

Artificial Intelligence Applications in Petroleum Exploration and Production

“…Fang et al [3] developed a novel cementing quality evaluation method based on the convolutional neural network. The authors proposed a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in variable density logging.…”