Abstract
Multiphase flow meters (MFMs) which measure three-phase oil-gas-water flow rates, are being utilized to make available, quick and accurate well test data in different oil production applications, like in remote or unmanned locations, topside and subsea applications. Data acquisition and production monitoring of the wells are done discretely by conventional MFMs, due to radioactive sources and unmanned location due to wells far distance. This study presents the development of committee machine based soft sensor (CMSS), an alternative way to the conventional MFMs. The proposed CMSS combines the results of linear and nonlinear auto-regression exogenous input (ARX/NARX), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for overall oil flow rate prediction of the wells, on the basis of available temperature and pressure measurements of lines. Each ensemble member has a weight factor which is derived in two ways including simple averaging and weighted averaging. In the weighted averaging method, the optimal combination of the weights is obtained by a novel optimization based method called imperialist competitive algorithm (ICA). Experiments on data set of 31 wells in one of the northern Persian Gulf oil fields of Iran proved the effectiveness of the proposed ICA optimized CMSS with an improved accuracy over the individual experts.