정훈영 교수

In this study, the severity of slugging is assessed by predicting maximum slug lengths (MSL) quickly using the random forest (RF) algorithm based on the geometric features of well trajectories for a shale gas field. Severe slugging is one of the critical issues production engineering-wise because it causes operation shut-down. Thus it should be predicted for proactive measurements. A total of 5033 well trajectories were acquired from the northeastern area of British Columbia, Canada. The well tr

Abstract Reservoir characterization is critical in order to estimate reserves and optimize oil and gas production. Ensemble Kalman filter characterizes the spatial distribution of reservoir parameters using covariances between static and dynamic data. Ensemble Kalman filter can rapidly provide results reflecting its overall tendency of parameter distribution, but may not characterize them in detail because ensemble Kalman filter does not minimize an objective function. Gradual deformation method

Liquid surge refers to an excessive liquid inflow to a slug catcher or a separator and is one of the main issues in flow assurance. The wellhead choke valves of gas wells must be adjusted to maintain the target flow rate as the reservoir pressure drops. The wellhead choke opening can be determined by conducting multiphase pipeline transient flow simulations to achieve the target flow rate and avoid liquid surges. However, it is not financially and computationally practical to conduct many multip

During the operation of a geological carbon storage project, a critical question is whether injected CO2 remains within the permitted zone. However, because a large suite of subsurface models are possible given very sparse static data, simulating flow in the entire suite to quantify the uncertainty in CO2 plume migration is impractical. We propose a fast alternative that scans the suite of geologic models and groups them on the basis of static connectivity. Grouping is achieved simply by measuri

Ensemble-based stochastic gradient methods, such as the ensemble optimization (EnOpt) method, the simplex gradient (SG) method, and the stochastic simplex approximate gradient (StoSAG) method, approximate the gradient of an objective function using an ensemble of perturbed control vectors. These methods are increasingly used in solving reservoir optimization problems because they are not only easy to parallelize and couple with any simulator but also computationally more efficient than the conve