A Comprehensive Review of Recent Advances in the Estimation of Natural Gas Compressibility Factor

2021 ◽  
Author(s):  
Oluwasegun Cornelious Omobolanle ◽  
Oluwatoyin Olakunle Akinsete
Keyword(s):  
Artificial Intelligence ◽  
Equation Of State ◽  
Compressibility Factor ◽  
Operating Conditions ◽  
Measurement Equation ◽  
Comprehensive Review ◽  
Holistic Understanding ◽  
Deviation Factor ◽  
Gas Compressibility ◽  
Limited Accuracy

Abstract Accurate prediction of gas compressibility factor is essential for the evaluation of gas reserves, custody transfer and design of surface equipment. Gas compressibility factor (Z) also known as gas deviation factor can be evaluated by experimental measurement, equation of state and empirical correlation. However, these methods have been known to be expensive, complex and of limited accuracy owing to the varying operating conditions and the presence of non-hydrocarbon components in the gas stream. Recently, newer correlations with extensive application over wider range of operating conditions and crude mixtures have been developed. Also, artificial intelligence is now being deployed in the evaluation of gas compressibility factor. There is therefore a need for a holistic understanding of gas compressibility factor vis-a-vis the cause-effect relations of deviation. This paper presents a critical review of current understanding and recent efforts in the estimation of gas deviation factor.

Calculation Method of Natural Gas Compressibility Factor and its Application in Pipeline Trade

2014 ◽  
Author(s):  
Xiaocui Tian ◽  
Xiaokai Xing ◽  
Rui Chen ◽  
Shubao Pang ◽  
Liu Yang
Keyword(s):  
Natural Gas ◽  
Compressibility Factor ◽  
Economic Benefits ◽  
Ideal Gas ◽  
Operating Conditions ◽  
Pipeline System ◽  
Natural Gas Pipeline ◽  
Gas Volume ◽  
Gas Compressibility ◽  
Simplified Formula

In the custody transfer metering of natural gas, it’s necessary to transform gas volume from metering state into standard state. Natural gas is non-ideal gas, and its compressibility factor varies with different components, temperature and pressure. So the accuracy of its calculation has direct impact on that of natural gas metering, and then affects the economic benefits of the enterprise [1]. According to related standard of China, in the custody transfer metering of natural gas, the formula stipulated by AGA NO.8 should be adopted to calculate compressibility factor. But the components of natural gas must be monitored at all times when this method is used, and the calculation process is complicated. In practical operation of natural gas trade, compressibility factor changes because of frequent adjustment of pipeline operating conditions. In order to simplify the calculation, simplified formula is applied to calculate compressibility factor generally, but it’s difficult to guarantee the accuracy at the same time. In this paper, the simplified formula, which is used for calculating natural gas compressibility factor of a joint-stock natural gas pipeline of CNPC, is modified with the standard formula stipulated by AGA NO.8. After the modification, an empirical formula of compressibility factor calculation applicable to this pipeline system is proposed, whereby the accuracy of compressibility factor calculation is improved. When the modified one is applied to natural gas trade, the accuracy of metering is improved likewise.

scholarly journals A Novel Simplification for the Prediction of Natural Gas Compressibility Factor

2021 ◽  
pp. 63-76
Author(s):  
OMOBOLANLE Oluwasegun Cornelious ◽  
AKINSETE Oluwatoyin Olakunle ◽  
AROMOKEYE Niyi
Keyword(s):  
Natural Gas ◽  
Compressibility Factor ◽  
Reservoir Properties ◽  
Predictive Tool ◽  
Deviation Factor ◽  
Temperature And Pressure ◽  
Explicit Correlation ◽  
Compressibility Factors ◽  
Gas Compressibility ◽  
Reduced Temperature

The need for a simpler, effective and less expensive predictive tool for the estimation of natural gas compressibility factor cannot be exaggerated. An accurate prediction of gas compressibility factor is essential because it plays a definitive role in evaluating gas reservoir properties used in the estimation of gas reserves, custody transfer and design of surface equipment. In this present work, a novel explicit correlation and a highly sophisticated computer program were developed to accurately predict natural gas deviation factor. The research also aims to effectively capture the relationship between Pseudo-reduced temperature and pressure in relations to the Z-factor. In this study, 3972 digitized data points extracted from Standing and Katz’s Chart were regressed and analyzed using Microsoft Excel Spreadsheet, the extraction of this data was done using WebPlotDigitizer developed by Ankit Rohatgi of GitHub, Pacifica, CA, USA. The correlation was developed as a function of Pseudo-reduced temperature and pressure with tuned parameters distributed across 1.05 ≤ Tpr ≤ 3.0 and 0 < Ppr ≤ 8.0. Subsequently, the input (Tpr and Ppr values) of the feed data was used to validate the correlation and compare it with other known and published correlations. Statistical analysis of the results showed that a 99.8% agreement exists between the predicted and actual compressibility factors for the various test scenarios and case studies involving both sweet and sour gases. Also, the correlation was observed to outperform other models. Finally, the results were observed to perfectly mimic the Standing and Katz charts with an overall correlation coefficient of 99.76% and an adjusted R2 of 99.75%. The proposed correlation was subsequently used to develop a software using JavaScript. Undoubtedly, the proposed correlation and software are suitable for rapid and accurate simplification and prediction of natural gas compressibility factor.

scholarly journals Applications of Artificial Intelligence in COVID-19 Pandemic: A Comprehensive Review

2021 ◽  
pp. 115695
Author(s):  
Muzammil Khan ◽  
Muhammad Taqi Mehran ◽  
Zeeshan Ul Haq ◽  
Zahid Ullah ◽  
Salman Raza Naqvi
Keyword(s):  
Artificial Intelligence ◽  
Comprehensive Review

scholarly journals COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

2021 ◽  
Author(s):  
Jawad Rasheed ◽  
Akhtar Jamil ◽  
Alaa Ali Hameed ◽  
Fadi Al-Turjman ◽  
Ahmad Rasheed
Keyword(s):  
Artificial Intelligence ◽  
Comprehensive Review

Artificial intelligence research in agriculture: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amit Sood ◽  
Rajendra Kumar Sharma ◽  
Amit Kumar Bhardwaj
Keyword(s):  
Artificial Intelligence ◽  
Peer Review ◽  
Diffusion Of Innovation ◽  
Comprehensive Review ◽  
Content Type ◽  
Design Elements ◽  
Extant Literature ◽  
Use Of Technology ◽  
Challenges And Opportunities ◽  
Market Factors

PurposeThe purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.Design/methodology/approachThe authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.FindingsFour factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.Research limitations/implicationsThe proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.Practical implicationsPractitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.Social implicationsThis paper brings new developed methods and practices in agriculture for betterment of society.Originality/valueThis paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

Transfer of consciousness. Considering its possibility or fantasy from the religious and scientific perspectives

DIALOGO ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 189-200
Author(s):  
Tudor-Cosmin Ciocan ◽  
Any Docu Axelerad ◽  
Maria CIOCAN ◽  
Alina Zorina Stroe ◽  
Silviu Docu Axelerad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Eternal Life ◽  
Human Consciousness ◽  
Religious Believer ◽  
Actual Knowledge ◽  
Holistic Understanding ◽  
The World ◽  
The Subject ◽  
Human Feature ◽  
The Brain

Ancient beliefs such as astral projection, human possession, abduction and other similar are not only universal, taught by all religions, but also used as premises for core believes/expectations, such as after-life, eternal damnation, reincarnation, and many others. Transferring Consciousness to a Synthetic Body is also a feature of interest in our actual knowledge, both religious as for science. If immortality were an option, would you take it into consideration more seriously? Most people would probably dismiss the question since immortality isn’t a real deal to contract. But what if having eternal life was a possibility in today’s world? The possibility of the transfer of human consciousness to a synthetic body can soon become a reality, and it could help the world for the better. Thus, until recently, the subject was mostly proposed by religion(s) and saw as a spiritual [thus, not ‘materially real’ or ‘forthwith accomplishable’] proposal therefore not really fully engaged or trust if not a religious believer. Now, technology is evolving, and so are we. The world has come to a point where artificial intelligence is breaking the boundaries of our perception of human consciousness and intelligence. And with this so is our understanding about the ancient question ‘who are we?’ concerning consciousness and how this human feature sticks to our body or it can become an entity beyond the material flesh. Without being exhaustive with the theme's development [leaving enough room for further investigations], we would like to take it for a spin and see how and where the religious and neuroscience realms intersect with it for a global, perhaps holistic understanding. Developments in neurotechnology favor the brain to broaden its physical control further the restraints of the human body. Accordingly, it is achievable to both acquire and provide information from and to the brain and also to organize feedback processes in which a person's thoughts can influence the activity of a computer or reversely.

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