Role of molecular diffusion in heterogeneous, naturally fractured shale reservoirs during CO2 huff-n-puff

Jia, Bao; Tsau, Jyun‐Syung; Barati, Reza

doi:10.1016/j.petrol.2018.01.032

Cited by 86 publications

(29 citation statements)

References 11 publications

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“…During the production period, molecular diffusion can mitigate the reservoir pressure decline. The hydrophilic nature of the reservoir accelerated the diffusion process of water and water imbibition and displaced oil into the larger pores [51].…”

Section: Study On Oil Displacement Effect and Residual Oil Distributionmentioning

confidence: 99%

Waterflooding Huff-n-puff in Tight Oil Cores Using Online Nuclear Magnetic Resonance

et al. 2018

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Given the difficulty in developing waterflooding in tight oil reservoirs, using waterflooding huff-n-puff is an effective method to improve oil recovery. Online nuclear magnetic resonance (NMR) can detect the change in internal oil and water during the core displacement process, and magnetic resonance imaging (MRI) in real time. To improve the tight oil reservoir development effectiveness, cores with different permeability were selected for a waterflooding huff-n-puff experiment. Combined with online NMR equipment, the fluid saturation, recovery rate, and residual oil distribution were studied. The experiments showed that, for tight oil cores, more than 80% of the pores were sub-micro-and micro-nanopores. More than 77.8% of crude oil existed in the sub-micro-and micropores, and movable fluids mainly existed in the micropores with a radius larger than 1 µm. The NMR data and the MRI images both demonstrated that the recovery ratio of waterflooding after waterflooding huff-n-puff was higher than that of conventional waterflooding, and, therefore, residual oil was lower. Choosing two cycles' of waterflooding, huff-n-puff was more suitable for tight oil reservoir development. The production of crude oil increased by 22.2% in the field pilot test, which preliminarily proved that waterflooding huff-n-puff was suitable for tight oil reservoirs.

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Section: Study On Oil Displacement Effect and Residual Oil Distributionmentioning

confidence: 99%

Waterflooding Huff-n-puff in Tight Oil Cores Using Online Nuclear Magnetic Resonance

et al. 2018

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“…In this paper, a combined correlation coefficient (cCC) parameter is introduced to indicate the combined CC of T, API, and GG to Pb and Rs. cCC is the arithmetic mean of the CCs of the three input parameters calculated by Equation (2). cCC is estimated for Pb and Rs to determine which output should be estimated first.…”

Section: Data Analysis and Acquisitionmentioning

confidence: 99%

“…No additional or special software is required to run the developed technique.Energies 2018, 11, 3490 2 of 14 function during the primary production period. Meanwhile, the fracture length plays a key role in oil production [2].Conventionally, PVT properties are determined by laboratory measurements. However, these experiments are costly, time-consuming, and highly dependent on the quality and quantity of collected samples [3][4][5].…”

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confidence: 99%

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A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

et al. 2018

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Reservoir fluid properties such as bubble point pressure (Pb) and gas solubility (Rs) play a vital role in reservoir management and reservoir simulation. In addition, they affect the design of the production system. Pb and Rs can be obtained from laboratory experiments by taking a sample at the wellhead or from the reservoir under downhole conditions. However, this process is time-consuming and very costly. To overcome these challenges, empirical correlations and artificial intelligence (AI) models can be applied to obtain these properties. The objective of this paper is to introduce new empirical correlations to estimate Pb and Rs based on three input parameters—reservoir temperature and oil and gas gravities. 760 data points were collected from different sources to build new AI models for Pb and Rs. The new empirical correlations were developed by integrating artificial neural network (ANN) with a modified self-adaptive differential evolution algorithm to introduce a hybrid self-adaptive artificial neural network (SaDE-ANN) model. The results obtained confirmed the accuracy of the developed SaDE-ANN models to predict the Pb and Rs of crude oils. This is the first technique that can be used to predict Rs and Pb based on three input parameters only. The developed empirical correlation for Pb predicts the Pb with a correlation coefficient (CC) of 0.99 and an average absolute percentage error (AAPE) of 6%. The same results were obtained for Rs, where the new empirical correlation predicts the Rs with a coefficient of determination (R2) of 0.99 and an AAPE of less than 6%. The developed technique will help reservoir and production engineers to better understand and manage reservoirs. No additional or special software is required to run the developed technique.

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“…The boom of Bakken formation in North America is limited by the effective recovery method even with the recent advancement of hydraulic fracturing and horizontal drilling technology utilization [1]. Since then, the CO 2 flooding method has been proposed as the potential way to increase the recovery factor for the unconventional shale reservoir in the Bakken formation, especially combined with the advanced water-alternating-gas (CO 2 -WAG) and CO 2 huff and puff technologies [2][3][4][5][6][7]. Meanwhile, the injection of the supercritical CO 2 into the shale and coal bed methane reservoir would assist the emission reduction of greenhouse gases [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Properties Alternation during Super-Critical CO2 Injection in Shale

et al. 2019

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The low recovery of oil from tight liquid-rich formations is still a major challenge for a tight reservoir. Thus, supercritical CO2 flooding was proposed as an immense potential recovery method for production improvement. While up to date, there have been few studies to account for the formation properties’ variation during the CO2 Enhanced Oil Recovery (EOR) process, especially investigation at the micro-scale. This work conducted a series of measurements to evaluate the rock mechanical change, mineral alteration and the pore structure properties’ variation through the supercritical CO2 (Sc-CO2) injection process. Corresponding to the time variation (0 days, 10 days, 20 days, 30 days and 40 days), the rock mechanical properties were analyzed properly through the nano-indentation test, and the mineralogical alterations were quantified through X-ray diffraction (XRD). In addition, pore structures of the samples were measured through the low-temperature N2 adsorption tests. The results showed that, after Sc-CO2 injection, Young’s modulus of the samples decreases. The nitrogen adsorption results demonstrated that, after the CO2 injection, the mesopore volume of the sample would change as well as the specific Brunauer–Emmett–Teller (BET) surface area which could be aroused from the chemical reactions between the CO2 and some authigenic minerals. XRD analysis results also indicated that mesopore were altered due to the chemical reaction between the injected Sc-CO2 and the minerals.

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Role of molecular diffusion in heterogeneous, naturally fractured shale reservoirs during CO2 huff-n-puff

Cited by 86 publications

References 11 publications

Waterflooding Huff-n-puff in Tight Oil Cores Using Online Nuclear Magnetic Resonance

Waterflooding Huff-n-puff in Tight Oil Cores Using Online Nuclear Magnetic Resonance

A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

Investigation of Properties Alternation during Super-Critical CO2 Injection in Shale

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