Screening of waterflooding using smart proxy model coupled with deep convolutional neural network

Abstract

The objective of this work is to identify any efficiency and accuracy improvements in smart proxy modeling (SPM) compared to the existing SPMs in the literature. SPM is a novel methodology which include additional steps in the construction process compared to traditional proxy models (TPM). We discuss the advantages of SPM compared to TPM where SPM implements feature engineering techniques which involves generating new static and dynamic parameters. The new extracted parameters help the model to capture hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to TPMs.Based on our literature review, we target our investigation into techniques to improve efficiency and accuracy by focusing on sampling (sequential sampling), feature ranking, and underlying model construction (CNN instead of ANN). In existing SPM literature, only one technique is used during each construction step where there are opportunities to explore novel construction steps to improve overall SPM accuracy and efficiency. Sequential sampling helps to construct the SPM with the lowest number of high-fidelity model execution and it avoids resampling, thereby saving time and making the SPM workflow more efficient. The average feature ranking technique described in this work provides a more confident prioritization of input parameters which eventually helps the overall efficiency in the feature selection step. CNN model as the underlying model provides higher accuracy than implemented ANN models in literature. The SPM with ANN underlying model provides an accuracy of 89–92% compared to the 99% and 94% of the CNN technique for the pressure and oil saturation predictions, respectively.In this paper, we construct a grid-based SPM of a Norwegian offshore field undergoing waterflooding. The designed parameters for this case are the individual liquid rate of the producers, and the outputs are individual grid's oil saturation and pressure. It is shown that the final results for screening purposes generated by SPM can confidently be used to mimic the behavior of the numerical simulator. Also, the results of feature ranking illustrate that some of the extracted data used in the SPM construction steps influence the model's outputs, confirming SPM capability.