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Petroleum Operations
Published in Jay Gohil, Manan Shah, Application of Big Data in Petroleum Streams, 2022
Reservoir studies, also known as reservoir engineering, refers to a branch of study/working under petroleum industry that deals with the scientific principles and applications of hydrocarbon flow through porous subsurface medium during the drilling (development) and production operations from petroleum reservoirs in order to obtain a high economic recovery. The study consists of (but not limited to) subsurface geology, applied mathematics, technological integration processes and basic laws of physics & chemistry.
Prediction of oil well production based on the time series model of optimized recursive neural network
Published in Petroleum Science and Technology, 2021
The traditional oil well production prediction methods are mainly based on the Arps decline curve (Asps 1945). This method is used as a typical representative method to predict and analyze oil production dynamics in reservoir engineering. The feasibility of this method has been proved by numerous works (Camacho-Velazquez, Fuentes-Cruz, and Vasquez-Cruz 2008; Abbaszadeh et al. 2014; Lu, Ma, and Azimi 2020). However, the Arps decline model predicts the future production based on historical data of changes in production and the judgment of the law of production decline. It only relies on the empirical equation, which has its own limitations such as no consideration of other factors affecting well production.
Modeling natural gas compressibility factor using a hybrid group method of data handling
Published in Engineering Applications of Computational Fluid Mechanics, 2020
Abdolhossein Hemmati-Sarapardeh, Sassan Hajirezaie, Mohamad Reza Soltanian, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Kwok-Wing Chau
The application of artificial intelligence and soft computing for building intelligent methods in many industries has recently attracted much attention (Anitescu, Atroshchenko, Alajlan, & Rabczuk, 2019; Chuntian & Chau, 2002; Fotovatikhah et al., 2018; Guo, Zhuang, & Rabczuk, 2019; Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018; Taherei Ghazvinei et al., 2018; Yaseen, Sulaiman, Deo, & Chau, 2019). In petroleum and gas industries, intelligent models have been used to determine, oil and gas thermodynamic properties, reservoir formation properties and miscibility conditions required for gas injection processes (Dargahi-Zarandi, Hemmati-Sarapardeh, Hajirezaie, Dabir, & Atashrouz, 2017; Dashtian, Bakhshian, Hajirezaie, Nicot, & Hosseini, 2019; Hajirezaie, Hemmati, & Ayatollahi, 2014; Hajirezaie, Hemmati-Sarapardeh, Mohammadi, Pournik, & Kamari, 2015; Hajirezaie, Pajouhandeh, Hemmati-Sarapardeh, Pournik, & Dabir, 2017; Hajirezaie, Wu, Soltanian, & Sakha, 2019; Hemmati-Sarapardeh, Tashakkori, Hosseinzadeh, Mozafari, & Hajirezaie, 2016; Kamari, Pournik, Rostami, Amirlatifi, & Mohammadi, 2017; Kamari, Safiri, & Mohammadi, 2015; Rostami, Kamari, Panacharoensawad, & Hashemi, 2018). These models take both input and output values to get trained and later can make predictions. Even though the original, intelligent models were considered black-box models, there have been numerous modifications to these models to make them transparent and usable methods, and their performance has significantly improved over the past few years. Intelligent models have been used in many reservoir engineering calculations. There are also some intelligent models that were developed specifically for predicting natural gas properties (Dargahi-Zarandi et al., 2017; Hajirezaie et al., 2015, 2017). We have already developed two intelligent models for predicting natural gas compressibility factor using the same data bank (Kamari et al., 2013; Shateri et al., 2015). However, they are a black box, and their usage generally needs software. Literature still suffers from the lack of a comprehensive, accurate, and simple model for natural gas compressibility estimation.