A simplex optimization technique for real-time, reconfigurable transmitter power amplifiers

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

Download Full-text

Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics

10.2118/205593-ms ◽

2021 ◽

Author(s):

Gaurav Modi ◽

Manu Ujjwal ◽

Srungeer Simha

Keyword(s):

Real Time ◽

Data Analytics ◽

Optimization Technique ◽

Material Balance ◽

Oil Production ◽

Data Driven ◽

Petroleum Engineering ◽

Short Term ◽

Injection Water ◽

Facility Level

Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

Download Full-text