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- 저자 : Javad Zahiri (검색결과 2 건)
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Determination of safe mud weight window based on well logging data using artificial intelligence
Zahiri, Javad,Abdideh, Mohammad,Golab, Elias Ghaleh 한국자원공학회 2019 Geosystem engineering Vol.22 No.4
Identification of different stresses applied to the environment surrounding wellbore via different processes, and combining these data with mechanical parameters of common formations in hydrocarbon reservoirs comprise a key for addressing a wide range of costly problems and issues in the oil industry. In the present research, first, an attempt was made to construct mechanical earth model based on well logging data, elastic moduli of rock, and appropriate failure criteria for the final purpose of calculating and determining safe mud weight window (SMWW). Finally, appropriate artificial intelligence and machine-learning algorithms were used to establish a relationship between well logging data and SMWW, which could be used to calculate and predict SMWW without using associated relationships with the mechanical earth model. This might end up with a decreased number of required parameters for calculating SMWW, including uniaxial compressive strength. In the present research, the learning process was conducted using datasets from three wells, two of which provided training data, with the other one used as testing data. The prepared model was finally used to predict corresponding pressures to SMWW and baseline pressures for hydraulic fracturing operation. The model gave a coefficient of determination of 0.93 when applied to the testing data using support vector regression algorithm with radial basis function kernel, indicating large capabilities of this algorithm in predicting non-foreseen data.
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Abbasali Emamjomeh,Elham Saboori Robat,Javad Zahiri,Mahmood Solouki,Pegah Khosravi 한국식물생명공학회 2017 Plant biotechnology reports Vol.11 No.2
Reconstruction of gene co-expression networks is a powerful tool for better understanding of gene function, biological processes, and complex disease mechanisms. In essence, co-expression network analysis has been widely used for understanding which genes are highly co-expressed through special biological processes or differentially expressed in various conditions. Development of high-throughput experiments has provided a large amount of genomic and transcriptomic data for model and nonmodel organisms. The availability of genome-wide expression data has led to the development of in silico procedures for reconstruction of gene co-expression networks. Gene co-expression networks predict unknown genes’ functions; moreover, it has been successfully applied to understand important biological processes of living organisms such as plants. In this survey, we have reviewed the algorithms, databases, and tools of gene coexpression network reconstruction, which can lead to new landscapes for further research activities. Furthermore, we explain an application of some algorithms, databases, and tools that can significantly boost our current understanding of co-expression networks in Arabidopsis thaliana as a model plant using publicly available data. The presented example shows that using co-expression networks is an efficient way to detect genes, which may involve in various critical biological processes such as defense response.
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