Computational fluid dynamic(CFD)simulation of pilot operated intermittent gas lift valve

To design an efficient intermittent gas-lift installation,reliable information is needed in the performance of all process components,from the outer boundary of the reservoir to the surface separators.The gas lift valve is the one critical component that affects the design of the whole system.In intermittent producing system,the pilot gas-lift valve is extremely used to control the point of compressed gas entry into the production tubing and acts as a pressure regulator.A novel approach using computational fluid dynamics simulation was performed in this study to develop a dynamic model for the gas passage performance of a 1-in.,Nitrogen-charged,pilot gas-lift valve.Dynamic performance curves were obtained by using Methane as an injection gas with flow rates reaching up to 4.5 MMscf/day.This study investigates the effect of internal pressure,velocity and temperature distribution within the pilot valve that cannot be predicted in the experiments and mathematical models during the flow-performance studies.A general equation of the nonconstant discharge coefficient has been developed for 1-inch pilot valve to be used for further calculation in the industry without using CFD model.The developed model significantly reduces the complexity of the data required to calculate the discharge coefficient.

Computational fluid dynamic(CFD)modelling of transient flow in the intermittent gas lift

A computational fluid dynamics model(CFD)is developed for intermittent gas lift techniques.The simulation is conducted for a test section of 18 m vertical tube with 0.076 m in diameter using air as injection gas and oil as a formation fluid.The results obtained from the CFD model are validated with the experiment results from the literature.The current study shows that computational modeling is a proven simulation program for predicting intermittent gas lift characteristics and the transient flow parameters that are changing with time and position in the coordinate system.The model can predict the slug velocity behavior for different injection pressure.The slug velocity profile shows three regions;the first region is the rapid acceleration at the initial time of injection,the second region shows the nearly constant velocity until the slug reaches the surface and the third region is again the rapid acceleration when the liquid starts to produce.Also,the results obtained from this model show that as the gas injection pressure increases,the liquid slug velocity increase,and the region of the constant velocity decrease.The effect of the injection time on the liquid production rate has been studied for two different gas injection pressures of 40 psig and 50 psig.The developed model shows that more than 50%of the liquid production is coming from after flow period.

Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques

Flowing bottom-hole pressure(FBHP)is a key metric parameter in the evaluation of performances of oil and gas production wells.An accurate prediction of FBHP is highly required in the petroleum industry for many applications,such the hydrocarbon production optimization,oil lifting cost,and assessment of workover operations.Production and reservoir engineers rely on empirical correlations and mechanistic models exist in open resources to estimate the FBHP.Several empirical models have been developed based on simulation and laboratory results that involved many assumptions that reduce the model's accuracy when they are applied for the field applications.The technologies of machine learning(ML)are one discipline of Artificial Intelligence(AI)techniques provide promising tools that help solving human's complex problems.This study develops machine-learning based models to predict the multiphase FBHP using three machine learning techniques that are Random forest,K-Nearest Neighbors(KNN),and artificial neural network(ANN).Results showed that using an artificial neural network model give error of 2.5%to estimate the FBHP which is less than the random forest and K-nearest neighbor models with error of 3.6%and 4%respectively.The ML models were developed based on a surface production data,which makes the FBHP is predicted using actual field data.The accuracy of the proposed models from ML was evaluated by comparing the results with the actual dataset values to ensure the effectiveness of the work.The results of this study show the potential of artificial intelligence in predicting the most complex parameter in the multiphase petroleum production process.