In-situ stress inversion in Liard Basin, Canada, from caliper logs

This paper proposes an integrated method of analytical calculation, artificial intelligence, and probabilistic analysis to cost-effectively determine geomechanical properties and in-situ stresses from borehole deformation via caliper logs. It's also demonstrated in this paper that the actual borehole size can not be simply taken as the bit size by default, and adjusted borehole size has to be used to find the reasonable borehole deformation. In the proposed method, an artificial neural network (ANN) is applied to map the relationship among in-situ stress, adjusted borehole size, geomechanical properties, and borehole displacements. The genetic algorithm (GA) searches for the set of unknown stresses and geomechanical properties that match the objective borehole deformation function. Probabilistic analysis is conducted after ANN-GA modeling to estimate the most possible ranges of the parameters. The hybrid method has been demonstrated by a field case study to estimate the adjusted borehole size, Young's modulus, and the two horizontal in-situ stresses using borehole deformation information reported from four-arm caliper logs of a vertical borehole in Liard Basin in Canada.