Characterization of In Situ Stress State and Joint Properties from Extended Leak-Off Tests in Fractured Reservoirs

AbstractCharacterization of geomechanical parameters in naturally fractured reservoirs remains one of the most challenging tasks in civil, mining, and petroleum engineering. Extended leak-off tests (XLOTs) are generally carried out in new wells to obtain in situ stresses for hydraulic fracture-treatment design and well-trajectory optimization in petroleum engineering. The largest and smallest principal in situ stresses can be calculated by shut-in/closure pressure and breakdown/reopening pressure of XLOTs. However, in situ stresses obtained from XLOTs in the traditional theoretical framework are not completely correct because XLOTs still keep the same test collocations as leak-off tests. In addition, the traditional method cannot be used to simultaneously calculate other parameters beyond in situ stresses. Given these challenges, a hybrid artificial neural network (ANN)–genetic algorithm (GA) method was tested for identification of the principal in situ stresses and joint parameters. First, XLOTs were per...

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