This paper proposes a cost-effective technique to determine geomechanical properties and in-situ stress from borehole deformation data. In this approach, an artificial neural network (ANN) is applied to map the relationship among in-situ stress, borehole size, geomechanical properties, and borehole displacements. The genetic algorithm (GA) searches for the set of unknown stresses and geomechanical properties that matches the objective borehole deformation function. Probabilistic recapitulation (PR) analysis is conducted after each ANN-GA modeling cycle and will be repeated with a reduced number of unknowns for the next ANN-GA modeling cycle until unequivocal results are achieved. The PR-GA-ANN method has been demonstrated by a field case study to estimate borehole size, Young’s modulus, Poisson’s ratio, and the two horizontal stresses using borehole deformation information reported from four-arm caliper log of a vertical borehole. The methodology effectively solves the issue of the multiple solutions (various rock mechanical properties and in-situ stresses combinations) for a certain borehole deformation. The case study also indicated that the calculated horizontal stresses are in reasonable agreement with the filed hydraulic fracture treatment observations and the reported regional stress study of the area.
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