Use of active learning method to determine the presence and estimate the magnitude of abnormally pressured fluid zones: A case study from the anadarko basin, Oklahoma

Abstract

We discuss active learning method (ALM) as an artificial intelligent approach for predicting a missing log (DT or sonic log) when only two other logs (GR and REID) are present. Applying ALMapproach involves three steps: (1) supervised training of the model, using available GR, REID, and DT logs; (2) confirmation and validation of the model by blind-testing the results in a well containing both the predictors (GR, REID) and the target (DT) values; and (3) applying the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT values. Our modeling approach indicates that the performance of the algorithm is satisfactory, while the time performance is significant. The quality of our simulation procedure was assessed by three parameters, namely mean square error (MSE), mean relative error (MRE), and Pearson product’ momentum correlation coefficient (R). The values obtained for these three quality-control parameters appear congruent, with the exception of MRE, regardless of the training set used (reduced vs. complete). ALM performance was measured also by the time required to attain the desirable outcomes: five depth levels of investigation took a little more than one minute of computing time during which MSE dropped significantly. We performed twice the regression analysis: with and without normalization of input data sets (training well and validation well) using the procedure indicated by previous works. The results show minimum differences in quality assessment parameters (MSE, MRE, and R), suggesting thatdata normalization is not a necessary step in all regression algorithms. We employed both the measured and simulated sonic logs DT to predict the presence and estimate the depth intervals where overpressured fluid zone may develop in the Anadarko Basin, Oklahoma. Based on our interpretation of the sonic log trends, we inferred that overpressure regions are developing betweenȼ depth and the overpressured intervals have thicknesses varying betweenȼ700 and 1000m. These resultsmatch very well our previous results reported in the Anadarko Basin, using the same wells, but different artificial intelligent approaches.