In modern risk analysis applied to oil and gas fields development the effort is made to take all possible sources of uncertainties into account so that the evaluation of alternative technical and economical exploitation strategies also indicates the potential hazards associated with each of them. In this respect the estimation of the hydrocarbon originally in place (HOIP) plays a crucial role. The determination of the main petrophysical parameters is normally based on the log interpretation process. During this process appropriate petrophysical models taking into account all the available data and information are defined, in the attempt to reliably relate log measurements to mineralogy, porosity and water saturation by using suitable mathematical algorithms. Main sources of uncertainties in well log interpretation are the measurements, the selected petrophysical models, and the input parameters required by the models. The evaluation of the uncertainties associated to the results of the well log interpretation process can be performed only by applying a methodology that couples a robust optimization process to a representative statistical approach. Based on previous studies, the Monte Carlo method was selected as the most rigorous statistical method for assessing the uncertainties, and then coupled to a constrained optimization process, as used to solve the inverse problem of the log interpretation process. As a result, a fully automatic algorithm was developed and implemented to solve for the petrophysical parameters based on different combination of log measurements. The approach was applied to a real case in order to assess the uncertainty associated to the petrophysical characterization of a deep-water exploration well, where the interpretation was complicated by a poor characterization of the reservoir fluids. The reservoir was an oil-bearing, layered sand formation. Based on the available measurements it was possible both to identify the main sources of uncertainty in the log measurements and in the petrophysical model parameters and to determine the final uncertainty affecting porosity and oil saturation. These values reflect on the evaluation of the HOIP and are to be used in reservoir static modelling for the simulation of multiple scenarios, thus influencing the reservoir development strategy

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