APPLICATION OF SUPPORT VECTOR MACHINE AND ORDINARY KRIGING (SVM-OK) TO SPATIAL INTERPOLATION OF POROSITY ATTRIBUTE OF AGBABU BITUMEN DEPOSIT

The lack of geostatistical modelling of volumetric characteristics of Nigeria's long-discovered heavy oil and bitumen deposits in Agbabu was accountable for the discrepancies in the hydrocarbon-in-place calculations. The absence of spatial variability evaluation in such efforts is responsible for the inconsistency and divergence that define the impacts of the existing attempts to estimate the porosity attribute. Due to its optimum features and simplicity of estimate, Kriging is reported as the most generally used approach to evaluate the connection between variables (inputs and output). However, Kriging reveals that the relationship between variables requires a linear form, which is frequently not the case. Thus, the necessity for a procedure with no fixed shape is built based on information acquired directly from the data. Consequently, support vector regression, a form of the support vector machine, is employed as a choice in contrast to kriging. This research is a comparative application of support vector machine and kriging (SVM-OK) to the spatial interpolation of the porosity property of the Agbabu bitumen field. The porosity values from existing data are trained, cross-validated, and verified with the help of the support vector machine, and they are interpolated by ordinary kriging. The geographical patterns of the predictions have been checked visually using graphs based on these three diagnostics: variance of the error terms and correlation of the error terms. The findings of this study illustrate the performance of the combination of the Support vector machine and conventional kriging in the spatial interpolation of volumetric characteristics in the Agbabu bitumen deposit. The approach exhibited great predictive performance. SVM-kriging will be employed in reservoir characterisation with the least mean absolute error (MAE) and mean squared error (MSE).