In petroleum industry, the compressional acoustic or sonic log (DT) is commonly used as a predictor because its capabilities respond to changes in porosity or compaction which, in turn, are further used to estimate formation (sonic) porosity, to map abnormal pore-fluid pressure, or to carry out petrophysical studies. Despite its intrinsic capabilities, the sonic log is not routinely recorded in during well logging. We propose using a method belonging to the class of supervised machine learning algorithms — Support Vector Regression (SVR) — to synthesize missing compressional acoustic or sonic (DT) logs when only common logs (e.g., natural gamma ray—GR, or deep resistivity—REID) are available.
Our approach involves three steps: (1) supervised training of the model; (2) confirmation and validation of the model by blind-testing the results in wells containing both the predictor (GR, REID) and the target (DT) values used in the supervised training; and (3) application of the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT log. SVR methodology offers two advantages over traditional deterministic methods: strong nonlinear approximation capabilities and good generalization effectiveness. These result from the use of kernel functions and from the structural risk minimization principle behind SVR. Unlike linear regression techniques, SVR does not overpredict mean values and thereby preserves original data variability. SVR also deals greatly with uncertainty associated with the data, the immense size of the data and the diversity of the data type. A case study from the Anadarko Basin, Oklahoma, about estimating the presence of abnormally pressurized pore-fluid zones by using synthesized DT values, is presented. The results are promising and encouraging.
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