Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensembles
This work addresses the problem of predicting gas saturation for resource estimation in the Groningen field, representing an incremental improvement with specific domain application.
The paper tackles spatial prediction of rock gas saturation in the Groningen gas field by using classifier ensembles on augmented well and seismic data, achieving a Matthews correlation coefficient of 0.7689 and an F1-score of 0.7949 for gas reservoir prediction.
This paper presents a proof of concept for spatial prediction of rock saturation probability using classifier ensemble methods on the example of the giant Groningen gas field. The stages of generating 1481 seismic field attributes and selecting 63 significant attributes are described. The effectiveness of the proposed method of augmentation of well and seismic data is shown, which increased the training sample by 9 times. On a test sample of 42 wells (blind well test), the results demonstrate good accuracy in predicting the ensemble of classifiers: the Matthews correlation coefficient is 0.7689, and the F1-score for the "gas reservoir" class is 0.7949. Prediction of gas reservoir thicknesses within the field and adjacent areas is made.