Effect of solvent type on emulsion formation in steam and solvent-steam flooding processes for heavy oil recovery

Kar, Taniya; Hasçakir, Berna

doi:10.1016/j.colsurfa.2020.125783

Cited by 24 publications

(2 citation statements)

References 25 publications

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“…In steam-flooded reservoirs, the strong heterogeneity of the reservoir and the presence of steam overloads facilitate steam canals, rapidly increase water content, and limit the effects of steam flooding A reservoir assessment based on reservoir capacity and a complete understanding of well-towell connectivity prevents steam channelling, achieves balanced reservoir displacement, and produces crude oil [1,5]. It helps improve the recovery.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Prioritization on Steam Flood Injection in Oil Field Using Random Forest Regression Method

Rafi

Anggoro

Rochman

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Steam flood injection is a method based on thermal recovery that injects steam, or especially water, at particular temperatures through the special injection wells. In this research, the oil field injected in steam flood is a heavy oil maturation with high viscosity, while the preliminary prioritization in steam flood injection in an oil field must be done at certain stages in a particular area in this oil field. This area consists of 8 sub-areas, 1,2,3 north section of the sub-area and 1,2,3,4,5,6,7,8 south section of the sub-area, each of which has a different number of wells and subsurface conditions. The provision to prioritize the injection in certain areas consists of monthly well production data, well location data, and reservoir data. On reservoir data is used a machine learning method that is random forest regression, which aims to generate variable importance, which will be made by grading reservoir properties variables on which are the most important or relevant for use as a parameter to determine the preliminary prioritization. The use of this Random Forest Regression method was combined to get an accuracy score for high-level modelling and correlate the process and results with the original state of on-field development and reservoir properties. The flow of this method is data cleansing, fitting models to data, assessing the quality of fit, generating decision trees, and identifying key variables. The tools used for this method include a programming language, which will do processing and build the variable importance model in the Random Forest Regression method. This can be done in a systematic and structured way. The result of this method shows the variable importance of reservoir properties such as porosity, saturation, and permeability in the form of OOB value calculation. While the final result of all corresponding prioritizations indicates that the 4,5,6 south section sub-areas should be prioritized for steam flood injection first.

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Section: Introductionmentioning

confidence: 99%

Preliminary Prioritization on Steam Flood Injection in Oil Field Using Random Forest Regression Method

Rafi

Anggoro

Rochman

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…Heavy oil is abundant but difficult to exploit. To improve the production of heavy oil reservoirs, some enhanced oil recovery (EOR) methods [1,2] have been adopted, such as steam flooding [3], high-pressure nitrogen flooding [4], CO 2 flooding [5], fire flooding [6], microbial technology [7], and so on. Among them, taking the technical advantages of efficient oil displacement, low unit thermal cost, and wide applicability, the fire flooding technology is one of the most effective and promising ways to improve the thermal recovery of heavy-oil resources [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prediction Models for the Physical Properties in Fire-Flooding Exhaust Reinjection Process

et al. 2022

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The reinjection of the fire-flooding exhaust is a novel disposal process for handling the exhaust produced by the in-situ combustion technology. For reasonable process design and safe operation, it is of great significance to select an optimum property calculation method for the fire-flooding exhaust. However, due to the compositional particularity and the wide range of operating parameters during reinjection, the state equations in predicting the exhaust properties over the wide range of operating parameters have not been studied clearly yet. Hence, this paper investigates the applicability of several commonly-used equations of state, including the Soave–Redlich–Kwong equation, Peng–Robinson equation, Lee–Kesler–Plocker equation, Benedict–Webb–Rubin–Starling equation, and GERG-2008 equations. Employing Aspen Plus software, the gas densities, compressibility factors, volumetric coefficients, and dew points for five exhaust compositions are calculated. In comparison with the experimental data comprehensively, the result indicates that the Soave–Redlich–Kwong equation shows the highest precision over a wide range of temperature and pressure. The mean absolute percentage error for the above four parameters is 3.84%, 5.17%, 5.53%, and 4.33%, respectively. This study provides a reference for the accurate calculation of the physical properties of fire-flooding exhausts when designing and managing a reinjection system of fire-flooding exhaust.

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Hybrid thermal-solvent process

Divandari¹,

Zabihi²

2023

Thermal Methods

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Effect of solvent type on emulsion formation in steam and solvent-steam flooding processes for heavy oil recovery

Cited by 24 publications

References 25 publications

Preliminary Prioritization on Steam Flood Injection in Oil Field Using Random Forest Regression Method

Preliminary Prioritization on Steam Flood Injection in Oil Field Using Random Forest Regression Method

Evaluation of Prediction Models for the Physical Properties in Fire-Flooding Exhaust Reinjection Process

Hybrid thermal-solvent process

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