Screening of enhanced oil recovery techniques for Iranian oil reservoirs using TOPSIS algorithm

Khojastehmehr, Mohsen; Madani, Mohammad; Daryasafar, Amin

doi:10.1016/j.egyr.2019.04.011

Cited by 50 publications

(20 citation statements)

References 35 publications

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“…Although the daily oil production rate from the oilfields is millions of barrels, these two phases leave more than half of the original oil in place (OOIP) [2][3][4][5][6][7]. An effective strategy to recover the residual oil is enhanced oil recovery (EOR), a generic term for the techniques that aim to increase the oil production rate after the primary and secondary oil recovery phases [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems

et al. 2021

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For the first time, the present work assesses the feasibility of using Korean red ginseng root extract, a non-ionic surfactant, for the purposes of enhanced oil recovery (EOR). The surfactant is characterized by Fourier-transform infrared spectroscopy (FT-IR) analysis. Pendant drop and sessile drop techniques are employed to study the oil–water interfacial tension (IFT) and wettability nature of the sandstone rock, respectively. In addition, oil recovery enhancement is investigated using micromodel and core floods. In the salt-free system, IFT measurements indicate that the surfactant carries a critical micelle concentration of 5 g/L. In a saline medium (up to 50 g/L), the addition of a surfactant with different concentrations leads to an IFT reduction of 47.28–84.21%. In a constant surfactant concentration, a contact angle reduction is observed in the range of 5.61–9.30°, depending on salinity rate, revealing a wettability alteration toward more water-wet. Surfactant flooding in the glass micromodel provides a more uniform sweeping, which leads to an oil recovery enhancement of 3.02–11.19%, depending on the extent of salinity. An optimal salt concentration equal to 30 g/L can be recognized according to the results of previous tests. Surfactant flooding (10 g/L) in optimal salt concentration achieves an additional oil recovery of 7.52% after conventional water flooding.

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Section: Introductionmentioning

confidence: 99%

A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems

et al. 2021

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“…[54] More of the EOR methods can be included to increase the applicability of the method. [55] The performance accuracy and reliability of soft sensor models in online implementation are well demonstrated but the R value is relatively low. A new methodology should be designed to increase overall accuracy.…”

Section: Reservoir Engineeringmentioning

confidence: 99%

“…Khojastehmehr et al [ 55 ] proposed a method for selecting a suitable technique to apply an EOR project in an oilfield worldwide. The technique for order of preference by similarity to ideal solution (TOPSIS) method with a multiple criteria decision making (MCDM) approach was used to screen numerous fields under the EOR techniques based on several rock and fluid properties and conditions, and an analytic hierarchy process (AHP) was applied to determine the relative significance of the reservoir parameters.…”

Section: And Da Applications In Upstream Petroleum Industrymentioning

confidence: 99%

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

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In the mid-19th century, the first oil well was drilled, which marked the beginning of the world oil economy. This era embarked on the phase of evolution and development of society in every aspect, with huge dependence on petroleum products, which subsequently led to tremendous growth in terms of technological advancements in the exploration and production activities over time. With the existing petroleum fields approaching toward depletion, the global need for energy resources is increasing simultaneously. This continuously rising demand has led us to move to complex environments for oil and gas exploration and production activities. Moreover, the real-time monitoring of reservoirs and well operations produces enormous data, which require efficient processing and analysis tools for decisionmaking regarding field development and asset management. [1,2] Subsequently, the need for optimum facilities design, [3] transportation [4] and refining [5] activities at reduced cost has also grown. Several attempts [6-9] are also being made to develop unconventional fields commercially based on technoeconomic viability. The implementation of computer-based techniques has proven to be promising toward many challenging problems for various oilfield operations. Several researchers have shown that the machine learning (ML) and data analytics (DA) approaches hold promising solutions toward the operational challenges being encountered at present in the industry and can efficiently help in resolving the issues related to interpretation and analysis of large datasets. [10-15] Nikravesh and Aminzadeh [16] have shown the use of neural networks (NNs) and fuzzy logic for mining petroleum data. Li and Li [17] combined a NN and cluster analysis to put forth a predictive model for identifying complex lithology. Fath et al. [18] proposed a novel approach for bubble point pressure prediction using an NN model with reservoir temperature, solution gas oil ratio (GOR), oil gravity, and gas specific gravity as input attributes. Ahmadi and Bahadori [19] showed the use of a supervised learning algorithm to determine the well placement and conning occurrence in horizontal wells. Maucec et al. [20] performed data mining and ML on well stimulation data for enhancing the prediction capabilities. Gupta et al. [21] applied DA for safeguarding real-time electrical submersible pumping operations. Cadei et al. [22] forecasted operational upsets using advanced analytics in an upstream production system. Dongxiao et al. [23] presented in their research that long short-term memory (LSTM), cascaded LSTM, and a fully connected NN can be utilized for generating synthetic well logs by supplementing the missing logging input data. Ghorbani et al. [24] proposed an artificial Intelligence (AI)based approach to predict the flow rate of oil from an orifice

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“…The management of these reserves is one of the most critical factors that can affect the profitability of the oil industry. 1 As the oil reservoir’s production rate is gradually declining, enhanced oil recovery (EOR) techniques are becoming more prevalent in the oil industry. These techniques can help maintain the reservoir’s production capacity.…”

Section: Introductionmentioning

confidence: 99%

Decision-Making Techniques for Water Shutoff Using Random Forests and Its Application in High Water Cut Reservoirs

et al. 2021

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The major oil fields are currently in the middle and late stages of waterflooding. The water channels between the wells are serious, and the injected water does little effect. The importance of profile control and water blocking has been identified. In this paper, the decision-making technique for water shutoff is investigated by the fuzzy evaluation method, FEM, which is improved using a random forest, RF, classification model. A machine learning random forest algorithm was developed to identify candidate wells and to predict the well performance for water shutoff operation. A data set consisting of 21 production wells with three-year production history is used, where out of the mentioned well data, 70% of them are implemented for training and the remaining are used for testing the model. After fitting the model, the new weights for the factors are established and decision-making is made. Accordingly, 16 wells out of 21 wells are selected by the FEM where 8 wells out of 21 wells are selected by the new factor weight created by RF for water shutoff. A numerical simulation model is established to plug the selected wells by both methods after which the influence of plugging on water cut, daily oil production, and cumulative oil production is compared. The paper shows that the reservoir had a better performance after eight wells were selected using a new weighting system created by RF instead of the 16 wells that were selected using the FEM model. The paper also states that the new weighting model’s accuracy improved the decision-making abilities of the wells.

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Screening of enhanced oil recovery techniques for Iranian oil reservoirs using TOPSIS algorithm

Cited by 50 publications

References 35 publications

A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems

A Systematic Study to Assess Displacement Performance of a Naturally-Derived Surfactant in Flow Porous Systems

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

Decision-Making Techniques for Water Shutoff Using Random Forests and Its Application in High Water Cut Reservoirs

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