AI based mechanistic modeling and probabilistic forecasting of hybrid low salinity chemical flooding

Dang, Cuong Thanh Quy; Nghiem, Long X.; Fedutenko, Eugene; Gorucu, Seyhan Emre; Yang, Chaodong; Mirzabozorg, Arash; Nguyen, Ngoc Thi Bich; Chen, Zhangxin

doi:10.1016/j.fuel.2019.116445

Cited by 22 publications

(5 citation statements)

References 20 publications

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“…This method could provide a noticeable wettability alteration and the utilization of HSW has had the lowest wettability changes [37]. Dang et al (2020) proposed mechanistic-hybrid chemical-enhanced oil recovery techniques that were combined with LSW flooding to consider a multilayer artificial network as an artificially intelligent method to investigate the efficiency of geochemistry properties. They considered different chemical agents such as surfactant, polymer and water salinities and their profound impact on the alteration of relative permeability changes.…”

Section: Introductionmentioning

confidence: 99%

“…They considered different chemical agents such as surfactant, polymer and water salinities and their profound impact on the alteration of relative permeability changes. They concluded that surfactant and low-salinity water injectivity would be an optimum hybrid-enhanced oil recovery method [38]. Bakhshian et al (2020) investigated the combined effects of pore geometry and wettability alteration for the displacement of two-phase fluid by the utilization of the lattice Boltzmann model at different contact angles.…”

Section: Introductionmentioning

confidence: 99%

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A Laboratory Approach on the Hybrid-Enhanced Oil Recovery Techniques with Different Saline Brines in Sandstone Reservoirs

Cheng

Yang

et al. 2020

Processes

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As foams are not thermodynamically stable and might be collapsed, foam stability is defined by interfacial properties and bulk solution. In this paper, we investigated foam injection and different salinity brines such as NaCl, CaCl2, KCl, and MgCl2 to measure cumulative oil production. According to the results of this experiment, it is concluded that sequential low-salinity water injections with KCl and foam flooding have provided the highest cumulative oil production in sandstone reservoirs. This issue is related to high wettability changes that had been caused by the KCl. As K+ is a monovalent cation, KCl has the highest wettability changes compared to other saline brines and formation water at 1000 ppm, which is due to the higher wettability changes of potassium (K+) over other saline ions. The interfacial tension for KCl at the lowest value is 1000 ppm and, for MgCl2, has the highest value in this concentration. Moreover, the formation brine, regarding its high value of salty components, had provided lower cumulative oil production before and after foam injection as it had mobilized more in the high permeable zones and, therefore, large volumes of oil would be trapped in the small permeable zones. This was caused by the low wettability alteration of the formation brine. Thereby, formation water flowed in large pores and the oil phase remained in small pores and channels. On the other hand, as foams played a significant role in the mobility control and sweep efficiency, at 2 pore volume, foam increased the pressure drop dramatically after brine injection. Consequently, foam injection after KCl brine injection had the maximum oil recovery factor of 63.14%. MgCl2 and formation brine had 41.21% and 36.51% oil recovery factor.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Laboratory Approach on the Hybrid-Enhanced Oil Recovery Techniques with Different Saline Brines in Sandstone Reservoirs

Cheng

Yang

et al. 2020

Processes

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“…In this paper, the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), which are commonly used evaluation methods for regression models, are applied to assess the model performance with different training dataset [ [27] , [28] , [29] , [30] ]. In addition, Peak-relative Error (PRE) a is used to evaluate the peak value accuracy, which are of most interest in actual flood forecasting [ [31] , [32] , [33] ].…”

Section: Methodsmentioning

confidence: 99%

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

Li,

Wu,

Zhang

et al. 2024

Heliyon

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“…This is where tertiary methods known as enhanced oil recovery (EOR) techniques are needed to help. EOR techniques can recover the residual oil saturation left in the reservoir, and they can be referred to as follows: thermal recovery, , microbial recovery, , gas injection, , and chemical flooding. − In an oil reservoir, EOR techniques can make an overall improvement in terms of efficiency of oil displacement, which involves both macroscopic and microscopic parameters. , Due to different rock and fluid types, a suitable EOR technique should be chosen . For example, carbonate reservoirs, which are complex cases because of their low relative permeability, are the best to be treated in a way such that alterations in wettability occur in the rock .…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Modeling of Fluid and Carbonated Rock Interactions with EDTA Chelating Agent during EOR Process

et al. 2023

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The injection of chemical fluids into oil reservoirs is gaining widespread attention in light of the declining conventional oil resources by recovering more hydrocarbons. This study is focused on using a chemical called ethylenediaminetetraacetic acid (EDTA) chelating agent in a carbonate reservoir to shed light on contact angle differences of 625 aged thin sections and rock dissolution under the influence of different pHs, temperatures, chelating times, and various chelating agent concentrations in seawater. According to a rock dissolution test, at least 5 wt % of EDTA chemical is needed to obtain oil recovery. A ζ potential test and scanning electron microscopy (SEM) images revealed that the mechanism of adsorption at low pH values and the expansion of the electrical double layer (EDL) at high pH values were responsible for wettability alteration, and an increase in EDTA concentration intensified each mechanism. Interfacial tension (IFT) measurements also showed that adding 1 and 10 wt % of the EDTA to the seawater solution reduced the IFT by 67.75% and 76.08%, respectively. The contact angle experiments demonstrated an increase in the mechanism that leads rock to behave more hydrophilically as pH, solution temperature, and chelating agent concentration in saltwater increased. Artificial neural network (ANN) methods also led to the introduction of a model to predict the contact angle employing multilayer perceptron neural networks (MPNN) and cascade feedforward neural networks (CFFNN). The CFFNN with two hidden neurons and trained by the Levenberg–Marquardt backpropagation algorithm is the most accurate model when comparing the accuracy of models for predicting contact angle values. The CFFNN model indicated that the weight percentage of the chelating chemical, which has a share of about 90%, had the greatest influence on the contact angle, and chelating time, with a share of less than 10%, had the least.

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AI based mechanistic modeling and probabilistic forecasting of hybrid low salinity chemical flooding

Cited by 22 publications

References 20 publications

A Laboratory Approach on the Hybrid-Enhanced Oil Recovery Techniques with Different Saline Brines in Sandstone Reservoirs

A Laboratory Approach on the Hybrid-Enhanced Oil Recovery Techniques with Different Saline Brines in Sandstone Reservoirs

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

Experimental Investigation and Modeling of Fluid and Carbonated Rock Interactions with EDTA Chelating Agent during EOR Process

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