scholarly journals The research of relative permeability curve for microbial flooding in Baolige oilfield

Author(s):  
Shasha Liu ◽  
Yaxiu Fu ◽  
Yi Gu ◽  
Rui Wang ◽  
Guan Wang ◽  
...  
2015 ◽  
Vol 109 (3) ◽  
pp. 527-540 ◽  
Author(s):  
Wei Hu ◽  
Shenglai Yang ◽  
Guangfeng Liu ◽  
Zhilin Wang ◽  
Ping Wang ◽  
...  

1975 ◽  
Vol 15 (01) ◽  
pp. 39-49 ◽  
Author(s):  
George J. Hirasaki

Abstract An improved estimate of the reservoir parameters is made during the history-matching phase of a reservoir simulation study by determining the set of parameters that result in the best match of the simulated performance with the observed performance. Often, the process of determining which parameters are to be adjusted is a trial-and-error process. Graphs of the sensitivity coefficients for comparing the cumulative oil recovery with the reservoir parameters are presented to determine the relative significance of parameters are presented to determine the relative significance of the parameters and to provide guidelines for the magnitude of change to the parameters. The sensitivity coefficients are based on a one-dimensional system with dip, incompressible fluids, and polynomial relative-permeability curves. The recovery efficiency can be expressed as a function of the dimensionless cumulative injection with the gravity number (gravity/viscous-forces ratio), mobility ratio, and relative-permeability exponent as parameters. The sensitivity of the cumulative oil recovery (at a given value of cumulative injection) to the movable pore volume, mobility ratio, permeability, and the exponent of the relative-permeability curve permeability, and the exponent of the relative-permeability curve can be calculated from the expression for the recovery efficiency. The graphs of the sensitivity coefficients can be used to determine the relative significance of the parameters, if a unique set of parameters can be determined, and how much they should be adjusted. parameters can be determined, and how much they should be adjusted Introduction When the simulated oil-recovery performance differs from the observed performance history, the engineer must determineif the history match is satisfactory, orif not, which reservoir parameters are to be adjusted and how much. The purpose of this parameters are to be adjusted and how much. The purpose of this discussion is to provide guidelines for the engineer in choosing the parameter(s) to be adjusted and to determine the magnitude and direction of the change. This will be accomplished by first illustrating the sensitivity of water or gas displacement performance to the reservoir parameters so that the critical parameter(s) can be identified, and then graphically presenting the magnitude of the sensitivity coefficients to determine the magnitude of change in the parameter value necessary to achieve a match. The following guidelines will be limited in scope to two-phase displacement processes with negligible interfacial mass transfer (e.g., waterflood, natural water drive, gas injection, or gas-cap expansion). Processes such as solution gas drive or vaporizing gas drive will not be presented. The results will be expressed in terms of the gross fluids produced or injected rather than time. The analysis and results are based on a one-dimensional system. Although the recovery performance of a multidimensional system will be different from that of a one-dimensional system, the relative sensitivity of the recovery performance to the parameters should not differ significantly for most recovery processes. Examples of exceptions that cannot be represented with the one-dimensional system are where well coning is significant or where permeability barriers exist between the injection well and production well. production well. The reservoir parameters that are investigated arethe movable pore volume of the displacement process, SVp;the mobility ratio of the displacing fluid to the displaced fluid, M, where the mobilities are evaluated at the maximum saturation of each phase;the permeability, which is represented as a factor in the gravity number, N(G) if the formation is dipping; andthe shape of the relative permeability curve, expressed in terms of a single parameter, n. ASSUMPTIONS AND MODEL OF THE DISPLACEMENT PROCESS The following assumptions are made about the displacement process. process.The saturations, relative permeabilities, porosity, and permeability are averaged over the reservoir thickness. permeability are averaged over the reservoir thickness.The areal displacement is modeled with a linear system. SPEJ P. 39


SPE Journal ◽  
2018 ◽  
Vol 23 (05) ◽  
pp. 1929-1943 ◽  
Author(s):  
Yongge Liu ◽  
Jian Hou ◽  
Lingling Liu ◽  
Kang Zhou ◽  
Yanhui Zhang ◽  
...  

Summary Reliable relative permeability curves of polymer flooding are of great importance to the history matching, production prediction, and design of the injection and production plan. Currently, the relative permeability curves of polymer flooding are obtained mainly by the steady-state, nonsteady-state, and pore-network methods. However, the steady-state method is extremely time-consuming and sometimes produces huge errors, while the nonsteady-state method suffers from its excessive assumptions and is incapable of capturing the effects of diffusion and adsorption. As for the pore-network method, its scale is very small, which leads to great size differences with the real core sample or the field. In this paper, an inversion method of relative permeability curves in polymer flooding is proposed by combining the polymer-flooding numerical-simulation model and the Levenberg-Marquardt (LM) algorithm. Because the polymer-flooding numerical-simulation model by far offers the most-complete characterization of the flowing mechanisms of polymer, the proposed method is able to capture the effects of polymer viscosity, residual resistance, diffusion, and adsorption on the relative permeability. The inversion method was then validated and applied to calculate the relative permeability curve from the experimental data of polymer flooding. Finally, the effects of the influencing factors on the inversion error were analyzed, through which the inversion-error-prediction model of the relative permeability curve was built by means of multivariable nonlinear regression. The results show that the water relative permeability in polymer flooding is still far less than that in waterflooding, although the residual resistance of the polymer has been considered in the numerical-simulation model. Moreover, the accuracy of the polymer parameters has great effect on that of the inversed relative permeability curve, and errors do occur in the inversed water relative permeability curve—the measurements of the polymer solution viscosity, residual resistance factor, inaccessible pore-volume (PV) fraction, or maximum adsorption concentration have errors.


Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yuhang Guo ◽  
Baozhi Pan ◽  
Lihua Zhang

Relative permeability and transverse relaxation time are both important physical parameters of rock physics. In this paper, a new transformation model between the transverse relaxation time and the wetting phase’s relative permeability is established. The data shows that the cores in the northwest of China have continuous fractal dimension characteristics, and great differences existed in the different pore size scales. Therefore, a piece-wise method is used to calculate the fractal dimension in our transformation model. The transformation results are found to be quite consistent with the relative permeability curve of the laboratory measurements. Based on this new model, we put forward a new method to identify reservoir in tight sandstone reservoir. We focus on the Well M in the northwestern China. Nuclear magnetic resonance (NMR) logging is used to obtain the point-by-point relative permeability curve. In addition, we identify the gas and water layers based on new T2-Kr model and the results showed our new method is feasible. In the case of the price of crude oil being low, this method can save time and reduce the cost.


2021 ◽  
Author(s):  
Mohd Ghazali Abd Karim ◽  
Wahyu Hidayat ◽  
Alzahrani Abdulelah

Abstract The objective of this paper is to investigate the effects of interfacial tension dependent relative permeability (Kr_IFT) on oil displacement and recovery under different gas injection compositions utilizing a compositional simulation model. Oil production under miscible gas injection will result in variations of interfacial tension (IFT) due to changes in oil and gas compositions and other reservoir properties, such as pressure and temperature. Laboratory experiments show that changes in IFT will affect the two-phase relative permeability curve (Kr), especially for oil-gas system. Using a single relative permeability curve during the process from immiscible to miscible conditions will result in inaccurate gas mobility against water, which may lead to poor estimation of sweep efficiency and oil recovery. A synthetic sector compositional model was built to evaluate the effects of this phenomenon. Several simulation cases were investigated over different gas injection compositions (lean, rich and CO2), fluid properties and reservoir characterizations to demonstrate the impact of these parameters. Simulation model results show that the application of Kr_IFT on gas injection simulation modelling has captured different displacement behavior to provide better estimation of oil recovery and identify any upside potential.


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