Experimental and analytical methods to determine minimum miscibility pressure (MMP) for Bakken formation crude oil

Adekunle, Olawale; Hoffman, B. Todd

doi:10.1016/j.petrol.2016.04.013

Cited by 35 publications

(15 citation statements)

References 6 publications

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“…The precision of model is strongly dependent on adequate data. In this study, 152 experimental values, including 52 pure CO 2 -oil MMPs and 100 impure CO 2 -oil MMPs, were collected from the open literatures (Adekunle and Hoffman, 2016;Bon et al, 2006;Cardenas et al, 1984;Dicharry et al, 1973;Dong et al, 2001;Eakin and Mitch, 1988;Graue and Zana, 1981;Holm and Josendal, 1974;Kanatbayev et al, 2015;Lai et al, 2017;Lashkarbolooki et al, 2017;Li and Luo, 2017;Li et al, 2012;Li et al, 2018;Metcalfe, 1982;Moudi et al, 2009;Harmon and Grigg, 1988;Sebastian et al, 1985;Spence et al, 1980; Thakur et al, 1984;Zuo et al, 1993) to build a robust model for estimating MMP. Eighty percent (122) of the all experimental values are used for training and the remaining 20% (30) are employed for test.…”

Section: Data Sources and Scopementioning

confidence: 99%

Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network

Tian

Wang³

et al. 2020

Energy Exploration & Exploitation

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CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

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Section: Data Sources and Scopementioning

confidence: 99%

Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network

Tian

Wang³

et al. 2020

Energy Exploration & Exploitation

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“…Visual observation of the injected gas bubble behavior is captured by a camera as it rises and moves upward along the oil column. By observing the shape and dissolution behavior of the bubble, the MMP can be determined [16]. A schematic diagram of the RBA experiment is shown in Figure 2.…”

Section: Rising Bubble Experimentsmentioning

confidence: 99%

“…Otherwise, if the injected bubble disperses more rapidly or dissolves instantaneously, the pressure is above the MMP. In the case of the trailing edge of the bubble, noticeably dissolves while the leading edge maintains its spherical shape until it dissolves near the top of the apparatus, the displacement pressure is at or just above the MMP [16]. The criterion used for modeling a rising gas bubble through a medium crude oil from RBA experimental data is due to this technique provides a reliable estimation of the MMP because the gas loses its interfacial tension and forms a single phase after traveling a short distance through the oil.…”

Section: Rising Bubble Experimentsmentioning

confidence: 99%

Modeling an Ascending Nitrogen Gas Bubble in a Medium Crude Oil by Lattice Boltzmann Method

Silva¹,

Miguel²

2018

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The study and modeling of oil biphasic systems, liquid-liquid and liquid-gas, focus mainly on the details of the modifications and application of the numerical methods itself. The correspondence between theoretical and experimental results and the information needed to apply a certain numerical method, usually remain in the background. On the other hand, in the particular case of the prediction of minimum miscibility pressure, extremely important parameter in oil exploration, references that show qualitative and numerical data associated with the characterization of the systems are scarce. The above reasons motivated the realization of this work. We used the Lattice Boltzmann Equation method to model a two-dimensional system of the displacement of a nitrogen gas bubble through a medium crude oil, under different pressure conditions keeping the temperature constant. According to experimental data, the bubble is not miscible by the crude, under a pressure range of 5000 psi to 6500 psi; nevertheless, the bubble is miscible in the range of 7000 psi to 7500 psi. Throughout simulations performed under similar conditions, we showed that it can be inferred the critical pressure range of miscibility of a medium crude oil.

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“…Both theoretical calculation and laboratory experimental methods have been used to obtain the MMP of CO 2 -crude oil (Zendehboudi et al, 2013;Ahmadi et al, 2015;Ahmadi et al, 2017;Ahmadi et al, 2018). Theoretical calculation methods mainly include empirical formulae (Yellig and Metcalfe, 1980), multiple contacts (Adekunle and Hoffman, 2016), numerical simulations (Ekechukwu et al, 2020), and equation of states (Karkevandi-Talkhooncheh et al, 2017), etc., because the application range of different calculation methods is different, and the results are greatly affected by parameters. The experimental measurement methods mainly include the slim tube test (STT) (Flock and Nouar, 1984;Mogensen, 2016), rising bubble apparatus (RBA) (Christiansen and Haines, 1987;Novosad et al, 1990;Czarnota et al, 2017), vanishing interfacial tension technique (VIT) (Rao, 1997;Saeedi Dehaghani and Soleimani, 2020), X-ray computerized scanner (Liu et al, 2015;Ahmad et al, 2016), magnetic resonance imaging technique (MRI) (Liu et al, 2016;Karkevandi-Talkhooncheh et al, 2018), core displacement experiment (Zhang and Gu, 2015;Ennin and Grigg, 2016), and so on.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Formation Water Salinity on the Minimum Miscibility Pressure of CO2-Crude Oil for Y Oilfield

Liu

et al. 2021

Front. Earth Sci.

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CO2 miscible flooding is an important technology for enhancing oil recovery and greenhouse gas storage in the world. As a tertiary recovery technology, it is usually applied after water flooding. Therefore, the actual reservoirs usually contain a lot of injected water in addition to connate water. The salinity of these formation waters varies from place to place. CO2 is an acid gas. After it is injected into the reservoir, it easily reacts with formation water and rock and affects the physical properties of the reservoir. However, no research results have been reported whether this reaction affects the minimum miscibility pressure (MMP) of CO2-crude oil, a key parameter determining miscible flooding in formation water. Based on CO2-formation water–rock interaction experiments, this paper uses the core flooding method to measure the CO2-crude oil MMP under different salinity in formation water. Results show that CO2 causes a formation water pH decrease from 7.4 to 6.5 due to its dissolution in formation water. At the same time, CO2 reacts with formation water, albite, potassium feldspar, and carbonate minerals in the cores to generate silicate and carbonate precipitates, which could migrate to the pore throat together with the released clay particles. Overall, CO2 increased core porosity by 5.63% and reduced core permeability by 7.43%. In addition, when the salinity of formation water in cores was 0, 4,767, and 6,778 mg/L, the MMP of CO2-crude oil was 20.58, 19.85, and 19.32 MPa, respectively. In other words, the MMP of CO2-crude oil decreased with the increase of salinity of formation water.

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Experimental and analytical methods to determine minimum miscibility pressure (MMP) for Bakken formation crude oil

Cited by 35 publications

References 6 publications

Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network

Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network

Modeling an Ascending Nitrogen Gas Bubble in a Medium Crude Oil by Lattice Boltzmann Method

The Effect of Formation Water Salinity on the Minimum Miscibility Pressure of CO2-Crude Oil for Y Oilfield

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Experimental and analytical methods to determine minimum miscibility pressure (MMP) for Bakken formation crude oil

Cited by 35 publications

References 6 publications

Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network

Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network

Modeling an Ascending Nitrogen Gas Bubble in a Medium Crude Oil by Lattice Boltzmann Method

The Effect of Formation Water Salinity on the Minimum Miscibility Pressure of CO2-Crude Oil for Y Oilfield

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Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network

Estimation of minimum miscibility pressure during CO₂ flooding in hydrocarbon reservoirs using an optimized neural network