Profile improvement during CO_2 flooding in ultra-low permeability reservoirs

Gas flooding such as CO2 flooding may be effectively applied to ultra-low permeability reservoirs, but gas channeling is inevitable due to low viscosity and high mobility of gas and formation heterogeneity. In order to mitigate or prevent gas channeling, ethylenediamine is chosen for permeability profile control. The reaction mechanism of ethylenediamine with CO2, injection performance, swept volume, and enhanced oil recovery were systematically evaluated. The reaction product of ethylenediamine and CO2 was a white solid or a light yellow viscous liquid, which would mitigate or prevent gas channeling. Also, ethylenediamine could be easily injected into ultra-low permeability cores at high temperature with protective ethanol slugs. The core was swept by injection of 0.3 PV ethylenediamine. Oil displacement tests performed on heterogeneous models with closed fractures, oil recovery was significantly enhanced with injection of ethylenediamine. Experimental results showed that using ethylenediamine to plug high permeability layers would provide a new research idea for the gas injection in fractured, heterogeneous and ultra-low permeability reservoirs. This technology has the potential to be widely applied in oilfields.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

With multiple microgrids(MGs)integrated into power distribution networks in a distributed manner,the penetration of renewable energy like photovoltaic(PV)power generation surges.However,the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security.Accordingly,an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions,especially when the network topology information is absent or inaccurate.In this paper,we propose a novel data-driven voltage profile improvement model,denoted as system-wide composite adaptive network(SCAN),which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs.Unlike existing studies that realize topology identification and decisionmaking optimization in sequence,the proposed end-to-end model determines the optimal voltage control decisions in one shot.More specifically,the proposed model consists of four modules,Pre-training Network and modified interior point methods with adversarial networks(Modified IPMAN)as core modules,and discriminator generative adversarial network(Dis-GAN)and Volt convolutional neural network(Volt-CNN)as ancillary modules.In particular,the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision.Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.