Abstract: Method intended for real-time modelling, by neural networks, of hydrodynamic characteristics of multiphase flows in transient phase in pipes. In order to specifically take account of the possible flow regimes of fluids in pipes, various neural or “expert” models are formed for several flow regimes (or subregimes) in the whole of the variation range of the hydrodynamic characteristics of the flows (preferably for each one of them), as well as a neural model estimating the probability of belonging of the flows to each flow regime or subregime, knowing some of the characteristics thereof. The probabilities obtained are used for weighting the estimations delivered by each neural model, the result of the weighted sum being then the estimation eventually retained. Applications to various industries and notably for modelling of hydrocarbon flows in pipelines.

Abstract: Method intended for real-time modelling, by neural networks, of hydrodynamic characteristics of multiphase flows in transient phase in pipes. In order to specifically take account of the possible flow regimes of fluids in pipes, various neural or “expert” models are formed for several flow regimes (or subregimes) in the whole of the variation range of the hydrodynamic characteristics of the flows (preferably for each one of them), as well as a neural model estimating the probability of belonging of the flows to each flow regime or subregime, knowing some of the characteristics thereof. The probabilities obtained are used for weighting the estimations delivered by each neural model, the result of the weighted sum being then the estimation eventually retained. Applications to various industries and notably for modelling of hydrocarbon flows in pipelines.

Abstract: The invention is a method and system for real-time estimation of the flow mode, at all points of a pipe whose structure is defined by a certain number of structure parameters, of a multiphase fluid stream defined by several physical quantities providing simplified implementation of hydrodynamic modules that can be integrated in modelling tools. A non-linear neural network is formed with an input layer having as many inputs as there are structure parameters and physical quantities, an output layer with as many outputs as there are quantities necessary for estimation of the flow mode and at least one intermediate layer. A learning base is created with predetermined tables connecting various values obtained for the output data to the corresponding values of the input data, with iterative determination of the weighting factors of the activation function allowing to properly connect the values in the input and output data tables.

Abstract: A method for forming a module (hydrodynamic or thermodynamic for example) intended for real-time simulation of the flow mode, at any point of a pipe, of a multiphase fluid stream comprising at least a liquid phase and at least a gas phase. The method comprises using a modelling system based on non-linear neural networks each having inputs for structure parameters and physical quantities, outputs where quantities necessary for estimation of the flow mode are available, and at least one intermediate layer. The neural networks are determined iteratively to adjust to the values of a learning base with predetermined tables connecting various values obtained for the output data to the corresponding values of the input data. A learning base suited to the imposed operating conditions is used and optimized neural networks best adjusted to the imposed operating conditions are generated.

Abstract: Method for forming a module (hydrodynamic or thermodynamic for example) intended for real-time simulation of the flow mode, at any point of a pipe, of a multiphase fluid stream comprising at least a liquid phase and at least a gas phase, so that it is best suited to fixed operating conditions concerning a certain number of determined structure and physical parameters relative to the pipe, and a set of determined physical quantities (hydrodynamic or thermodynamic quantities for example), with fixed variation ranges for the parameters and the physical quantities.

Abstract: The present invention is a system and method for generating an alarm relative to effective longitudinal behavior of a drill bit fastened to the end of a drill string driven in rotation in a well by a driving device situated at the surface, using a physical model of the drilling process based on general mechanics equations. The following steps are carried out: the model is reduced so to retain only pertinent modes, at least two values Rf and Rwob are calculated, Rf being a function of the principal oscillation frequency of weight on hook WOH divided by the average instantaneous rotating speed at the surface, Rwob being a function of the standard deviation of the signal of the weight on bit WOB estimated by the reduced longitudinal model from measurement of the signal of the weight on hook WOH, divided by the average weight on bit WOB0, defined from the weight of the string and the average weight on hook. Any danger from the longitudinal behavior of the drill bit is determined from the values of Rf and Rwob.

Abstract: Method and system intended for real-time estimation of the flow mode, at all points of a pipe whose structure is defined by a certain number of structure parameters, of a multiphase fluid stream defined by several physical quantities.

Abstract: The present invention relates to a system and to a method for automatic detection of the precession of an element of a drill string, in particular of the bottomhole assembly of a drill string. The method comprises a stage of surface acquisition of signals linked with the vibration of the end of the drillpipes, automatic processing on at least one signal in order to detect a jump in average and a significant line in the spectrum of the signal. A precession criterion is calculated and an alarm is set off. The invention further relates to a system for detecting the precession of an element of a drill string.