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The flow of hydrocarbons through deep sea pipelines is a challenging issue for the petroleum industry. Typical operating conditions involve high hydrostatic pressures and low sea bed temperatures, which can favor the formation of solid deposits and result in pipeline blockages and, consequently, incur in large financial losses. Heat transfer analysis plays a fundamental role in the design of deep sea pipelines. Thermal insulation is designed to avoid the formation of solid deposits during regular operating conditions. On the other hand, dur- ing shutdown periods heat losses from the produced fluid to the surrounding environment can result on fluid temperatures sufficiently low that the formation of deposits becomes inevitable, unless other techniques are used, such as injection of chemical inhibitors or active heating of the pipeline. In this work, we solve the inverse problem of estimating the transient heat trans- fer coefficient from the pipeline surface to the surrounding sea water, in a pipe-in-pipe sys- tem. The transient external heat transfer coefficient is estimated with the Markov Chain Mon- te Carlo method, implemented via the Metropolis-Hastings algorithm. Simulated temperature measurements of one single sensor, located at the external surface of the inner pipe, are used in the inverse analysis. A smooth prior is used for the transient heat transfer coefficient, while the measurement errors are assumed to be Gaussian, additive, with zero mean and known covariance matrix.

ESTIMATION OF THE CONVECTIVE HEAT TRANSFER COEFFICIENT IN PIPELINES WITH THE MARKOV CHAIN MONTE CARLO METHOD