2025-06-25
Shinagam Rajshekar, Lalit Chandra Saikia, Lavanya Nandyala
Recent research highlights a significant challenge in maintaining power quality in grid-connected wind energy systems. This challenge is further complicated by issues such as the lack of reactive power support and the high installation costs associated with power quality upkeep. To address these challenges, this study proposes an innovative solution in the form of a Genetic Improvised Fuzzy Logic PID controller based DSTATCOM. This controller not only effectively reduces overshooting but also demonstrates reduced voltage variation during dynamic conditions when compared to conventional Fuzzy Logic Control (FLC) methods. Furthermore, the Genetic Algorithm optimizes switching pulses for the Voltage Source Converter (VSC) in the Distribution Static Synchronous Compensator (DSTATCOM). The proposed model demonstrates its effectiveness by achieving a 2.06% reduction in Total Harmoni Distortion (THD). Rigorous simulations conducted in MATLAB/Simulink, and the results obtained from the proposed model highlight its potential to significantly enhance power quality in grid-connected wind energy systems.
2025-06-25
Adil Raad Saadallah Ogaidi, Xiao Wang, Mohammed A. Samba
The Water Alternating Gas (WAG) process is a cyclic method of injecting alternating cycles of gas followed by water, repeating this process over multiple cycles. However, decision-making during the WAG process often involves critical choices that, at times, fall short of providing accurate and effective results. In recent years, the advent of Artificial Neural Networks (ANNs) has opened up promising opportunities to revamp Enhanced Oil Recovery (EOR) processes. This comprehensive review examines ANN-based methodologies for predicting performance of WAG injection projects. It starts with an overview of the WAG injection process, outlining its relevance in enhanced oil recovery. Subsequently, the study explores the architecture and complexity of ANNs, providing foundational insights into their functionality. The application of ANNs in developing predictive models for WAG injection is then analyzed, emphasizing their potential to enhance accuracy in forecasting WAG performance. Key aspects discussed include ANN-based WAG injection across various gas and rock types, the functions used in ANN WAG modeling, and the algorithms employed in ANN-based WAG simulations. Furthermore, the review highlights the challenges associated to implementing ANN-based predictive models in WAG projects. By synthesizing existing research, this study intends to provide valuable insights for petroleum engineers, particularly in understanding and applying ANN models for optimizing WAG injection strategies.
2025-06-25
Eduardo López Ramos, Helber Cubillos Gutierrez, Felipe Medellín, Alcide Thebault
There are numerous industrial processes and energy generators that emit CO 2 into the atmosphere, which are still in an incipient state of technological transition for their reduction. A global expanding alternative involves storing CO 2 for long periods (> 1,000 years), in geological environments such as saline aquifers. Thus, studies on a semi-regional scale are fundamental to reduce the uncertainty regarding location of sites with the best storage capacities and high Chance of Successful. The use of Forward Stratigraphic Modeling (FSM) workflows to populate static models with properties is a time-versatile tool to evaluate the prospectivity of subsurface resources over large areas, as required for CO2 storage. These workflows, coupled to Common Risk Segment Analysis, have been applied in a basin with a long history of O&G exploration and production, the Middle Magdalena Valley basin in Colombia, proving their effectiveness in the selection of areas along prospective corridors to store CO 2 , in Mesozoic and Cenozoic formations. Preliminary estimates suggest that the Meso-Cenozoic formations in this part of the basin may reach a Theoretical CO 2 Storage Capacity close to 830 GTon CO 2 and an Effective CO 2 Storage Capacity of 293 GTon CO 2 .