A Data Driven Artificial Intelligence Framework for Hydrogen Production Optimization in Waterflooded Hydrocarbon Reservoir

Hydrogen has become a very promising green energy source that can be easily stored and transported, and it has the potential to be utilized in a variety of applications. Hydrogen, as a power source, has the benefits of being easily transportable and stored over long periods of times, and does not lead to any carbon emissions related to the utilization of the power source. Thermal EOR methods are among the most commonly used recovery methods. They involve the introduction of thermal energy or heat into the reservoir to raise the temperature of the oil and reduce its viscosity. The heat makes the oil mobile and assists in moving it towards the producer wells. The heat can be added externally by injecting a hot fluid such as steam or hot water into the formations, or it can be generated internally through in-situ combustion by burning the oil in depleted gas or waterflooded reservoirs using air or oxygen. This method is an attractive alternative to produce cost-efficiently significant amounts of hydrogen from these depleted or waterflooded reservoirs. A major challenge is to optimize injection of air/oxygen to maximize hydrogen production via ensuring that the in-situ combustion sufficiently supports the breakdown of water into hydrogen molecules. In-situ combustion or fireflood is a method consisting of volumes of air or oxygen injected into a well and ignited. A burning zone is propagated through the reservoir from the injection well to the producing wells. The in-situ combustion creates a bank of steam, gas from the combustion process, and evaporated hydrocarbons that drive the reservoir oil into the producing wells. There are three types of in-situ combustion processes: dry forward, dry reverse and wet forward combustion. In a dry forward process only air is injected and the combustion front moves from the injector to the producer. The wet forward injection is the same process where air and water are injected either simultaneously or alternating. Artificial intelligence (AI) practices have allowed to significantly improve optimization of reservoir production, based on observations in the near wellbore reservoir layers. This work utilizes a data-driven physics-inspired AI model for the optimization of hydrogen recovery via the injection of oxygen, where the injection and production parameters are optimized, minimizing oxygen injection while maximizing hydrogen production and recovery. Multiple physical and data-driven models and their parameters are optimized based on observations with the objective to determine the best sustainable combination. The framework was examined on a synthetic reservoir model with multiple injector and producing wells. Historical injection and production were available for a time period of three years for various oxygen injection and hydrogen production levels. Various time-series deep learning network models were investigated, with random forest time series models incorporating a modified mass balance – reaction kinetics model for in-situ combustion performing most effectively. A robust global optimization approach, based on an artificial intelligence genetic optimization, allows for simultaneously optimization of an injection pattern and uncertainty quantification. Results indicate potential for significant reduction in required oxygen injection volumes, while maximizing hydrogen recovery. This work represents a first and innovative approach to enhance hydrogen recovery from waterflooded reservoirs via oxygen injection. The data-driven physics inspired AI genetic optimization framework allows to optimize oxygen injection while maximizing hydrogen production.

Influence of the Heat Loss on the Performance of a Two-stage Gasification Reactor

This paper investigates the gasification efficiency of a two stage gasifier, described in all detail in previous works, as a function of the heat loss across the reactor walls. The behaviour of the reactor was simulated using a simple mathematical model already reported in previous papers. The examined heat loss ranges from 0% of the heat produced by the exothermic reactions into the reactor, up to 20%. Calculations have been performed by keeping constant both the injected total oxygen and its partition between the two stages, while different feedstocks have been used, such as landfill gas, municipal solid waste (MWS), willow and rice straw. The results of calculation show that the gasification efficiency at fixed oxygen injection is greatly influenced by the feedstock. The elaboration of the obtained data indicates also that the trend of the gasification efficiency vs. heat loss is a function of the high heating value of the feedstock and of the ratio between the oxygen present into the reactor (injected + the one of the feedstock) and the stoichiometric oxygen necessary to transform the feeding into carbon dioxide and steam.

Surface Oxygen Injection in Tin Disulfide Nanosheets for Efficient CO2 Electroreduction to Formate and Syngas

Surface chemistry modification represents a promising strategy to tailor the adsorption and activation of reaction intermediates for enhancing activity. Herein, we designed a surface oxygen-injection strategy to tune the electronic structure of SnS2 nanosheets, which showed effectively enhanced electrocatalytic activity and selectivity of CO2 reduction to formate and syngas (CO and H2). The oxygen-injection SnS2 nanosheets exhibit a remarkable Faradaic efficiency of 91.6% for carbonaceous products with a current density of 24.1 mA cm−2 at −0.9 V vs RHE, including 83.2% for formate production and 16.5% for syngas with the CO/H2 ratio of 1:1. By operando X-ray absorption spectroscopy, we unravel the in situ surface oxygen doping into the matrix during reaction, thereby optimizing the Sn local electronic states. Operando synchrotron radiation infrared spectroscopy along with theoretical calculations further reveals that the surface oxygen doping facilitated the CO2 activation and enhanced the affinity for HCOO* species. This result demonstrates the potential strategy of surface oxygen injection for the rational design of advanced catalysts for CO2 electroreduction.

Tuning the Optical Properties of WO3 Films Exhibiting a Zigzag Columnar Microstructure

Tungsten oxide WO3 thin films are deposited by DC reactive magnetron sputtering. The Reactive Gas Pulsing Process (RGPP) associated with the GLancing Angle Deposition method (GLAD) are implemented to produce zigzag columnar structures. The oxygen injection time (tON time) and the pulsing period are kept constant. Three tilt angles α are used: 75, 80, and 85° and the number of zigzags N is progressively changed from N = 0.5, 1, 2, 4, 8 to 16. For each film, refractive index, extinction coefficient, and absorption coefficient are calculated from optical transmission spectra of the films measured in the visible region from wavelength values only. Absorption and extinction coefficients monotonously drop as the number of zigzags increases. Refractive indices are the lowest for the most grazing tilt angle α = 85°. The highest refractive index is nevertheless obtained for a number of zigzags close to four. This optimized optical property is directly correlated to changes of the microstructure, especially a porous architecture, which is favored for high tilt angles, and tunable as a function of the number of zigzags.