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Petroleum Operations
Published in Jay Gohil, Manan Shah, Application of Big Data in Petroleum Streams, 2022
In a simplified manner (as mentioned previously), a petroleum reservoir refers to a formation or subsurface pool of rocks (porous in nature) where the hydrocarbons have accumulated over the years due to the process of diagenesis (a process of rock compaction which changes the physical and chemical properties of the rock) of buried organic materials (like remains of dead organisms) under extreme high temperature and pressure [29].
Brief Overview of Partial Differential Equations
Published in Jichun Li, Yi-Tung Chen, Computational Partial Differential Equations Using MATLAB®, 2019
A petroleum reservoir is a porous medium which contains hydrocarbons. Simulation of petroleum reservoirs helps us in predicting the performance of a reservoir and optimizing the oil recovery. The so-called two-phase immiscible flow is used to model the simultaneous flow of two fluid phases such as water and oil in a porous medium. We assume that the two phases are immiscible and there is no mass transfer between them. Hence we have the mass conservation for each phase, which is given by ∂(ϕραSα)∂t+∇⋅(ραuα)=qα,α=w,o, where w and o denote the water phase and oil phase, respectively. Here we denote ϕ the porosity of the porous medium, ρα the density of each phase, qα the mass flow rate, and Sα the saturation. The Darcy velocity uα is described by Darcy’s law uα=−1μαkα(∇pα−ραg∇z),α=w,o, where kα is the effective permeability, pα is the pressure, μα is the viscosity for each phase α, g is the magnitude of the gravitation acceleration, z is the depth of the reservoir, and ∇z=(0,0,1)′. Note that both phases occupy the whole void space, hence we have Sw + So = 1.
Microbial characteristics and potential mechanisms of souring control for a hypersaline oil reservoir
Published in Petroleum Science and Technology, 2023
Bo Wang, Shuyuan Deng, Sanbao Su, Shanshan Sun, Chao Chen, Hao Xu, Hongfei Ma, Ibrahim M. Banat, Yuehui She, Fan Zhang
In this work, we explored microbial souring control under high salinity conditions through NO3- and NO2- injection in anaerobic bottles and simulated up-flow sand-packed bioreactor in the lab. Underlying mechanisms were revealed by examining chemical characteristic and microbial community composition at different stages. The results indicated that souring control were feasible. Halophilic nitrate-reducing, sulfur-oxidizing bacteria were the stimulated target groups. The results indicated that the activity of SRBs was inhibited by bacteria related to Halomonas and Arcobacter, and Sulfurimonas, Thiomicrospira and Acrobacter could control the production of H2S when SRBs could not be suppressed. The salinity was an important factor for the efficiency of souring control in the oil reservoir studied. Therefore, the reinjection of high salinity production water for the studied petroleum reservoir could be recommended.
Application of “oil-phase” microbes to enhance oil recovery in extra heavy oil reservoir with high water-cut: A proof-of-concept study
Published in Petroleum Science and Technology, 2023
Li-Hui Hao, Chang-Qiao Chi, Na Luo, Yong Nie, Yue-Qin Tang, Xiao-Lei Wu
The petroleum reservoir under study is located in a Xinjiang oilfield, northwestern China. The average porosity and permeability of this sandstone reservoir are 25.2% and 9,306 mD, respectively, and oil strata are located at depths between 470 and 625 m, at 33 °C and 4.95 MPa. The average viscosity of degassed oil is 19,683 mPa·s at 33 °C, which reduces to 4 ∼ 7 Pa·s at 50 °C. The salinity of the formation water is 20 ∼ 50 g/L. The reservoir is categorized as three different zones: oil zone, transition zone and bottom/edge aquifer zone, according to various degrees of water-cut (Figure 1). Before MEOR, this reservoir has been exploited with steam huff-puff; however, most of wells had been shut in, due to high water-cut caused by bottom/edge water conning. Fluid samples composed of both crude oil and water mixture were collected from wellheads in the target reservoir with the methods previously described (Cai et al. 2015), for further researches.
Accelerated Bayesian inference-based history matching of petroleum reservoirs using polynomial chaos expansions
Published in Inverse Problems in Science and Engineering, 2021
Sufia Khatoon, Jyoti Phirani, Supreet Singh Bahga
The ever-increasing demand for petroleum necessitates efficient management of existing oil and gas fields and development of new fields. The forecast of oil production from an oil reservoir is made with the aid of reservoir simulations, which predict the flow of oil, gas, and water through porous media [1]. Accurate production forecasts using reservoir simulations require precise knowledge of the properties of the reservoirs [2]. The subsurface of a petroleum reservoir is invariably complex and heterogeneous [3]. Because measurements are limited to discrete locations, geological properties and initial conditions are usually determined by solving inverse problems using the historical production data. Solving inverse problems to estimate reservoir properties by matching the predictions of reservoir models to the production data is called history matching.