Monte Carlo Risk Assessment of Formation Damage Caused by Asphaltene Deposition

2021 ◽  
pp. 1-8
Author(s):  
Arley S. Carvalhal ◽  
Gloria M. N. Costa ◽  
Silvio A. B. Vieira de Melo
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Oil Production ◽  
Rock Properties ◽  
Formation Damage ◽  
Permeability Reduction ◽  
Prevention Policy ◽  
Oil Composition ◽  
Hypothetical Scenario ◽  
Asphaltene Deposition

Summary Uncertainties regarding the factors that influence asphaltene deposition in porous media (e.g., those resulting from oil composition, rock properties, and rock/fluid interaction) strongly affect the prediction of important variables, such as oil production. Besides, some aspects of these predictions are stochastic processes, such as the aggregation phenomenon of asphaltene precipitates. For this reason, a well-defined output from an asphaltene-deposition model might not be feasible. Instead of this, obtaining the probability distribution of important outputs (e.g., permeability reduction and oil production) should be the objective of rigorous modeling of this phenomenon. This probability distribution would support the design of a risk-based policy for the prevention and mitigation of asphaltene deposition. In this paper we aim to present a new approach to assessing the risk of formation damage caused by asphaltene deposition using Monte Carlo simulations. Using this approach, the probability-distribution function of the permeability reduction was obtained. To connect this information to a parameter more related to economic concepts, the probability distribution of the damage ratio (DR) was also calculated, which is the fraction of production loss caused by formation damage. A hypothetical scenario involving a decision in the asphaltene-prevention policy is presented as an application of the method. A novel approach to model the prevention of asphaltene aggregation using inhibitors has been proposed and successfully applied in this scenario.

An Integrated Simulation Approach to Predict Permeability Impairment under Simultaneous Aggregation and Deposition of Asphaltene Particles

SPE Journal ◽  
2021 ◽  
pp. 1-14
Author(s):  
Xin Su ◽  
Rouzbeh G. Moghanloo ◽  
Minhui Qi ◽  
Xiang-an Yue
Keyword(s):  
Porous Media ◽  
Particle Aggregation ◽  
Formation Damage ◽  
Permeability Reduction ◽  
Simulation Approach ◽  
Pore Connectivity ◽  
Integrated Simulation ◽  
Asphaltene Deposition ◽  
Simulation Results ◽  
Permeability Impairment

Summary Formation damage mechanisms in general lower the quality of the near wellbore, often manifested in the form of permeability reduction, and thus reducing the productivity of production wells and injectivity of injection wells. Asphaltene deposition, as one of the important causes, can trigger serious formation damage issues and significantly restrict the production capacity of oil wells. Several mechanisms acting simultaneously contribute to the complexity associated with prediction of permeability impairment owing to asphaltene deposition; thus, integration of modeling efforts for asphaltene aggregation and deposition mechanisms seems inevitable for improved predictability. In this work, an integrated simulation approach is proposed to predict permeability impairment in porous medium. The proposed approach is novel because it integrates various mathematical models to study permeability impairment considering porosity reduction, particle aggregation, and pore connectivity loss caused by asphaltene deposition. To improve the accuracy of simulation results, porous media is considered as a bundle (different size) of capillary tubes with dynamic interconnectivity. The total volume change of interconnected tubes will directly represent permeability reduction realized in porous media. The prediction of asphaltene deposition in porous media is improved in this paper via integration of the particle aggregation model into calculation. The simulation results were verified by comparing with existing experimental data sets. After that, a sensitivity analysis was performed to study parameters that affect permeability impairment. The simulation results show that our permeability impairment model—considering asphaltene deposition, aggregation, and pore connectivity loss—can accurately reproduce the experimental results with fewer fitting or empirical parameters needed. The sensitivity analysis shows that longer aggregation time, higher flow velocity, and bigger precipitation concentration will lead to a faster permeability reduction. The findings of this study can help provide better understanding of the permeability impairment caused by asphaltene deposition and pore blockage, which provides useful insights for prediction of production performance of oil wells.

Experimental Investigation of Asphaltene-Induced Formation Damage Caused by Pressure Depletion of Live Reservoir Fluids in Porous Media

SPE Journal ◽  
2018 ◽  
Vol 24 (01) ◽  
pp. 01-20 ◽  
Author(s):  
Omid Mohammadzadeh ◽  
Shawn David Taylor ◽  
Dmitry Eskin ◽  
John Ratulowski
Keyword(s):  
Porous Medium ◽  
Porous Media ◽  
Flow Rate ◽  
Formation Damage ◽  
Bulk Fluid ◽  
Asphaltene Content ◽  
Pressure Depletion ◽  
Asphaltene Deposition ◽  
Permeability Impairment ◽  
Live Oil

Summary One of the complex processes of permeability impairment in porous media, especially in the near-wellbore region, is asphaltene-induced formation damage. During production, asphaltene particles precipitate out of the bulk fluid phase because of pressure drop, which might result in permeability reduction caused by both deposition of asphaltene nanoparticles on porous-medium surfaces and clogging of pore throats by larger asphaltene agglomerates. Experimental data will be used to identify the parameters of an impairment model being developed. As part of a larger effort to identify key mechanisms of asphaltene deposition in porous media and develop a model for asphaltene impairment by pressure depletion, this paper focuses on a systematic design and execution of an experimental study of asphaltene-related permeability damage caused by live-oil depressurization along the length of a flow system. An experiment was performed using a custom-designed 60-ft slimtube-coil assembly packed with silica sands to a permeability of 55 md. The customized design included a number of pressure gauges at regular intervals along the coil length, which enabled real-time measurement of the fluid-pressure profile across the full length of the slimtube coil. The test was performed on a well-characterized recombined live oil from the Gulf of Mexico (GOM) that is a known problematic asphaltenic oil. Under a constant differential pressure, the injection flow rate of the live oil through the slimtube coil decreased over time as the porous medium became impaired. During the impairment stage, samples of the produced oil were collected on a regular basis for asphaltene-content measurement. After more than 1 month, the impairment test was terminated; the live oil was purged from the slimtube coil with helium at a pressure above the asphaltene-onset pressure (AOP); and the entire system was gently depressurized to bring the coil to atmospheric conditions while preserving the asphaltene-damaged zones of the coil. The permeability and porosity of the porous medium changed because of asphaltene impairment that was triggered by pressure depletion. Results indicated that the coil permeability was impaired by approximately 32% because of pressure depletion below the AOP, with most of the damage occurring in the latter section of the tube, which operated entirely below the AOP. Post-analytical studies indicated lower asphaltene content of the produced-oil samples compared with the injecting fluid. The distribution of asphaltene deposits along the length of the coil was determined by cutting the slimtube coil into 2- to 3-ft-long sections and using solvent extraction to collect the asphaltenes in each section. The extraction results confirmed that the observed permeability impairment was indeed caused by asphaltene deposition in the middle and latter sections of the coil, where the pressure was less than the AOP. With the success of this experiment, the same detailed analysis can be extended to a series of experiments to determine the effects of different key parameters on pressure-induced asphaltene impairment, including flow rate, wettability, and permeability.

scholarly journals Prediksi Tingkat Penerimaan Lulusan Siswa Kejuruan dalam Dunia Usaha dan Industri Menggunakan Metode Monte Carlo

2020 ◽  
pp. 84-89
Author(s):  
Hasanatul Iftitah ◽  
Y Yuhandri
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Prediction Accuracy ◽  
Vocational School ◽  
Vocational Schools ◽  
Random Numbers ◽  
Vocational High School ◽  
The Monte Carlo Method ◽  
Academic Year

Vocational High School (SMK) Negeri 4 Kota Jambi is one of the favorite vocational schools in Jambi City which is also the only pure tourism vocational school in Jambi Province. SMK Negeri 4 Kota Jambi has several vocational majors, namely culinary, beauty, fashion and hospitality. In general, students who choose to attend vocational schools have the hope of being able to work immediately after graduating from school, they do not need to continue to study to be able to work. In this study, researchers will predict the level of acceptance of students from SMK Negeri 4 Kota Jambi in the business and industrial world using the Monte Carlo method. Monte Carlo is a method that can find values ​​that are close to the actual value of events that will occur based on the distribution of sampling data. The technique of this method is to select random numbers from the probability distribution to perform the simulation. The data used in this study is the data of students from SMK Negeri 4 Kota Jambi who worked from the 2015/2016 Academic Year to the 2018/2019 Academic Year. Furthermore, the data will be processed using the Monte Carlo method. The simulation will be implemented using PHP programming. The result of this research is the level of prediction accuracy of students of SMK Negeri 4 Kota Jambi who are accepted in the business and industrial world using the Monte Carlo method is 84%.

scholarly journals Uncertainty Analysis of Hydrological Drought due to Record Length, Time Scale, and Probability Distribution Functions Using Monte-Carlo Simulation Method

2021 ◽  
Author(s):  
Faezeh Ghasemnezhad ◽  
Ommolbanin Bazrafshan ◽  
Mehdi Fazeli ◽  
Mohammad Parvinnia ◽  
Vijay Singh
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Confidence Interval ◽  
Time Scale ◽  
Statistical Data ◽  
Distribution Functions ◽  
Hydrological Drought ◽  
Discharge Data ◽  
Probability Distribution Functions ◽  
Record Length

Abstract Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30 and 49 year length from Minab dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3, 6. 9, 12, 24 and 48 month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte-Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to normal and lognormal distributions and the minimum uncertainty to gamma and Weibull distributions. Further, the increase in both time scale and record length led to the decrease in uncertainty.

scholarly journals Precision Annealing Monte Carlo Methods for Statistical Data Assimilation: Metropolis-Hastings Procedures

2019 ◽  
Author(s):  
Adrian S. Wong ◽  
Kangbo Hao ◽  
Zheng Fang ◽  
Henry D. I. Abarbanel
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Data Assimilation ◽  
Conditional Probability ◽  
Information Transfer ◽  
Statistical Data ◽  
Laplace Method ◽  
Conditional Probability Distribution ◽  
Expected Values ◽  
Transfer Of Information

Abstract. Statistical Data Assimilation (SDA) is the transfer of information from field or laboratory observations to a user selected model of the dynamical system producing those observations. The data is noisy and the model has errors; the information transfer addresses properties of the conditional probability distribution of the states of the model conditioned on the observations. The quantities of interest in SDA are the conditional expected values of functions of the model state, and these require the approximate evaluation of high dimensional integrals. We introduce a conditional probability distribution and use the Laplace method with annealing to identify the maxima of the conditional probability distribution. The annealing method slowly increases the precision term of the model as it enters the Laplace method. In this paper, we extend the idea of precision annealing (PA) to Monte Carlo calculations of conditional expected values using Metropolis-Hastings methods.

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