08-11 November 2021 Online Event Prediction of photoelectric effect log and facies classification in the Panoma gas field Felipe F. Melo CGG GeoSoftware (Formerly at PUC Rio) Contact: felipe146@hotmail.com 1) AIM Predict the photoelectric effect log on two wells and then perform facies classification. 2) Introduction Departing from seven wells in the training set, I impute missing values in the photoelectric effect log (PE) in one well and predict the PE log from two wells. Then, I aggregate these wells to the training data in order to perform facies classification. Distinct feature augmentation techniques are used in both log prediction and facies classification. I used the random forest algorithm for log prediction and the extreme gradient boosting for facies classification. Focusing on the generalization of the models, the hyperparameters were tuned with the nested and the leave two out cross validation, respectively. The well Churchman Bible is picked as test data, this well is chosen because it has all facies and a large number of samples related to facies of interest. 3) Dataset 4) Method 5) Prediction of photoelectric effect log 6) Facies Classification 7) Conclusions • The feature augmentation was successful in both prediction and classification. • The PE prediction score (72%) in the test well shows the good result of this approach. • The results for facies classification are acceptable because of the samples limitations, accuracy of 58%. • The accuracy of 86% on adjacent facies and 90% on BS, 57% on PS and 6% on D (critical facies) shows the effectiveness of the proposed approach. 9) References Dubois, M. K.; Bohling, G. C.; Chakrabarti, S. 2007. Comparison of four approaches to a rock facies classification problem. Computers & Geosciences, 33 (5), 599–617. Hall, B. 2016a Facies classification using machine learning. The Leading Edge, 35, 906–909. Hall, M.; Hall, B. 2017. Distributed collaborative prediction: Results of the machine learning contest. The Leading Edge, 36, 267–269. 8) Acknowledgments I would like to acknowledge Brendon and Matt Hall, the participants of the SEG contest and the Swung community for push the knowledge and free software. This research has been benefited from knowledge and source codes shared by them. 10) Reproduce The codes used on this work are available at: https://github.com/ffigura/SBGf_ ML_gas/ Figure 1 – Distribution of facies for each well, the well names are shown over each plot. The last panel shows the distribution of all facies. Figure 2 – Cross-plots from the well logs colored with facies classification. Figure 3 – Well logs and facies from the well Nolan. Figure 4 – Feature augmentation for well Nolan. Figure 5 – Prediction results. a) Feature importance for the test set. b) True and predicted logs from the well Nolan, this well is part of the training set. c) True and predicted logs from the test set, well Churchman Bible. Figure 7 – Confusion matrix. The facies as BS, PS and D are more critical in this work. The well Churchman Bible is used as test dataset, i.e., the quality of the regressor and of the classifier will be evaluated on this well. All other nine wells are used as training dataset for facies classification. In the first part, the photoelectric effect (PE) log is imputed for the well Recruit F9 (12 missing samples) and computed for the wells Alexander D and Kimzey A using the Random Forest regressor. Before log prediction, I perform feature augmentation. In this case, it adds non-linearities computing quadratic expansion and by considering all second order interaction terms and applies a low pass filter with the 1D discrete wavelet transform in the logs. Therefore, the training set moves from six to twenty features before estimate the PE logs. The logs NM_N and RELPOS are not used in feature augmentation because it would make no geophysical sense. After prediction of the PE logs into the two missing wells, these wells are grouped with the seven wells used as trai