Carbon Dioxide Plume Evolution Following Injection into a Depleted Natural Gas Reservoir: Modeling of Conformance Uncertainty Reduction Over Time

AbstractQuantifying natural gas production risk can help guide natural gas exploration and development in Carboniferous gas reservoirs. In this study, the Monte Carlo probability method is used to obtain the probability distribution and growth curve of each production risk factor and production in a Carboniferous gas reservoir in eastern Sichuan. In addition, the fuzzy comprehensive evaluation method is used to conduct the sensitivity analysis of the risk factors, and the natural gas production and realization probability under different risk factors are obtained. The research results show that: (1) the risk factor–production growth curve and probability distribution are calculated by the Monte Carlo probability method. The average annual production under the stable production stage under different realization probabilities is obtained. The maximum probability range of annual production is $$\left( {43.43 - 126.35} \right) \times 10^{8} {\text{m}}^{3} /{\text{year}}$$ 43.43 - 126.35 × 10 8 m 3 / year , and the probability range is 14.59–92.88%. (2) The risk factor sensitivity analysis is significantly affected by the probability interval. In the entire probability interval, the more sensitive risk factors are the average production of the kilometer-deep well (D) and the production rate in the stable production stage (A). During the exploration and development of natural gas, these two risk factors can be adjusted to increase production.

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Thermodynamic Studies of Iron Carbonate Solubility in Aqueous Monoethylene Glycol Mixtures and CO2 Atmosphere

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.830.134 ◽

2016 ◽

Vol 830 ◽

pp. 134-138 ◽

Cited By ~ 1

Author(s):

Camila Senna Figueiredo ◽

Jailton Ferreira do Nascimento ◽

Rony Oliveira de Sant'ana ◽

Deborah Cordeiro de Andrade ◽

Zaniel Souto Dantas Procópio ◽

...

Keyword(s):

Carbon Dioxide ◽

Natural Gas ◽

High Capacity ◽

Gas Production ◽

Aqueous Mixtures ◽

Iron Carbonate ◽

Low Viscosity ◽

Carbon Dioxide Atmosphere ◽

Equilibrium Data ◽

Monoethylene Glycol

Monoethylene glycol (MEG) is being widely applied as thermodynamic inhibitor to avoid formation of natural gas hydrates. High hydrophilicity, low toxicity, low viscosity, low solubility in liquid hydrocarbons and high capacity of dissolving salts are advantageous for the use of MEG in the natural gas production. In addition, MEG recovery can be easily achieved considering its low volatility in relation to water, which makes the process economical and environmentally feasible. The reuse of MEG is being theme of research and phase equilibrium data for the involved species are required. In this work, a experimental procedure to synthetize iron carbonate and, afterwards, determine its solubility in aqueous mixtures of MEG in the presence of carbon dioxide atmosphere have been developed. Furthermore, a series of solubility data has been measured. This work presents a worthy contribution to the description of iron carbonate aqueous solubilities in the presence of MEG and carbon dioxide, regarding the instability of the salt to respect of oxidation. Subsequently, the knowledge of the behavior of the iron carbonate solubilities is useful for the industrial unities of production of natural gas and recovery of MEG.

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Energy and Exergy Analyses of a Power Plant With Carbon Dioxide Capture Using Multistage Chemical Looping Combustion

Journal of Energy Resources Technology ◽

10.1115/1.4035057 ◽

2016 ◽

Vol 139 (3) ◽

Cited By ~ 14

Author(s):

Bilal Hassan ◽

Oghare Victor Ogidiama ◽

Mohammed N. Khan ◽

Tariq Shamim

Keyword(s):

Carbon Dioxide ◽

Power Plant ◽

Natural Gas ◽

Co2 Capture ◽

Power Plants ◽

Waste Heat ◽

Chemical Looping Combustion ◽

Chemical Looping ◽

Operating Pressure ◽

Efficiency Gain

A thermodynamic model and parametric analysis of a natural gas-fired power plant with carbon dioxide (CO2) capture using multistage chemical looping combustion (CLC) are presented. CLC is an innovative concept and an attractive option to capture CO2 with a significantly lower energy penalty than other carbon-capture technologies. The principal idea behind CLC is to split the combustion process into two separate steps (redox reactions) carried out in two separate reactors: an oxidation reaction and a reduction reaction, by introducing a suitable metal oxide which acts as an oxygen carrier (OC) that circulates between the two reactors. In this study, an Aspen Plus model was developed by employing the conservation of mass and energy for all components of the CLC system. In the analysis, equilibrium-based thermodynamic reactions with no OC deactivation were considered. The model was employed to investigate the effect of various key operating parameters such as air, fuel, and OC mass flow rates, operating pressure, and waste heat recovery on the performance of a natural gas-fired power plant with multistage CLC. The results of these parameters on the plant's thermal and exergetic efficiencies are presented. Based on the lower heating value, the analysis shows a thermal efficiency gain of more than 6 percentage points for CLC-integrated natural gas power plants compared to similar power plants with pre- or post-combustion CO2 capture technologies.

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Belief Movement, Uncertainty Reduction, and Rational Updating*

The Quarterly Journal of Economics ◽

10.1093/qje/qjaa043 ◽

2021 ◽

Author(s):

Ned Augenblick ◽

Matthew Rabin

Keyword(s):

Statistical Tests ◽

Uncertainty Reduction ◽

The State ◽

Data Sets ◽

New Information ◽

The World ◽

Core Concepts ◽

Market Beliefs ◽

Over Time

Abstract When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

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