Combined bottom-hole pressure calculation procedure using multiphase correlations and artificial neural network models, A

Abstract

Artificial neural network (ANN) techniques have been adopted to predict bottom-hole pressures and have proved to have better, or at a minimum equivalent prediction performance than conventional prediction methods such as multiphase correlations and mechanistic modeling. With the applied design, the use of ANN techniques can be more fully investigated to aid in multiphase flow related issues. In this study, different artificial neural network models have been trained to solve two of the major problems of bottom-hole pressure calculations - flow regime recognition and pressure gradient prediction. A support vector machine model was trained for flow regime classification. In order to include inclination angle effects on flow regime transition, the model uses inclination angle and as well as gas and liquid velocity numbers as input variables. Four possible different flow patterns were considered for upward and horizontal multiphase flow, including bubble flow, slug flow, annular mist flow and stratified flow. Some 3-D plots of all the possible flow patterns at all inclination angles (from horizontal to upward vertical) within the studied condition range were generated based on model outputs. Previous back-propagation neural network models in the literature have been modified to fit into piece-wise calculation procedures of multiphase correlations to achieve higher prediction accuracy and broaden the prediction range. The model training requires well-segment-scale data sets, which contain pressure gradients as the model output variable and the model input variables, including inclination angle, liquid superficial velocity, gas superficial velocity, gas-liquid surface tension, liquid density, specific gravity of free gas, liquid viscosity, gas viscosity, average pressure and average temperature. The training data was collected from literature and as well as some piece-wise calculation results of multiphase correlations. Different back-propagation neural network model structures have been tested to find a suitable neuron number on hidden-layer. Two pressure gradient prediction models were trained for slug flow and annular mist flow. Finally, a combined bottom-hole calculation procedure was designed based on multiphase correlations and trained artificial neural network models. The statistical test results using the collected data show that the combined procedure has the best prediction performance than the eleven multiphase correlations studied in this work with the lowest average absolute percent error of 3.1% and standard deviation of 0.034. Some independent field data was used to test the extendability of the combined procedure prediction range. Comparing to the multiphase correlations, the combined procedure gave fairly accurate predictions with an average absolute percent error of 23.0% and a standard deviation of 0.176. To facilitate field application, a multiphase flow bottom-hole pressure calculator with a user graphic interface was developed.