Assisted Cement Log Interpretation Using Machine Learning

Abstract

Summary The Assisted Cement Log Interpretation Project has used machine learning (ML) to create a tool that interprets cement logs by predicting a predefined set of annular condition codes used in the cement log interpretation process. The development of a cement log interpretation tool speeds up the log interpretation process and enables expert knowledge to be efficiently shared when training new professionals. By using high-quality and consistent training data sets, the project has trained a model that will support unbiased and consistent interpretations over time. The tool consists of a training and a prediction tool integrated with cased-hole logging interpretation software. By containerizing the code using an “API First” design principle (API: application programming interface), the applicability of this add-on tool is broad. The ML model is trained using selected and engineered features from cement logs, and the tool predicts an annular condition code according to the cement classification system for each depth segment in the log. The interpreters can easily fetch a complete cement log interpretation prediction for the log and use that as a template for their final interpretation. The ML model can easily be retrained with new data sets to improve accuracy even further. To improve cement log interpretation consistency in the industry, the code will be made available as open source.

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

“…Nezhad et al proposed a convolutional neural network (CNN) approach to evaluate the performance of neural networks in automatic interpretation and combined it with fuzzy systems [5]. 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].…”