Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

Ramos, Galarza; Akanji, Lateef

doi:10.3390/en10070837

Cited by 14 publications

(13 citation statements)

References 40 publications

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“…A soft computing approach can be implemented and investigated to get better performance results. [ 53 ]…”

Section: Suggestions and Discussionmentioning

confidence: 99%

“…[52] A soft computing approach can be implemented and investigated to get better performance results. [53] The proposed predictive model has improved the performance accuracy. Optimized CNN-based studies can be further examined to obtain higher accuracy.…”

Section: Reservoir Engineeringmentioning

confidence: 91%

“…The case studies discussed subsequently for implementations of ML and DA in EOR have been summarized in Table 6. Case Studies: ML and DA in EOR: Ramos and Akanji [53] performed a neuro-fuzzy simulation study combining an NN and fuzzy logic with integration of box plot data analysis, which provided a screening technique for selecting the suitable EOR method for the candidate reservoirs in the Angolan oil fields. The performance was evaluated with RMSE and the nondimensional error index (NDEI).…”

Section: Ma and Da In Eormentioning

confidence: 99%

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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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“…A soft computing approach can be implemented and investigated to get better performance results. [ 53 ]…”

Section: Suggestions and Discussionmentioning

confidence: 99%

Section: Reservoir Engineeringmentioning

confidence: 91%

Section: Ma and Da In Eormentioning

confidence: 99%

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

2020

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“…Table 6 lists the comparison results of the RMSE for the training and testing data for Boston housing data. As listed in Table 6, we finally obtained the best parameters (number of clusters: c = [4,8,9,9,5,9,2], number of rules: 46, weighting exponent: m = 1.5) when the number of linguistic contexts is seven (p = 7). The experimental results clearly showed that the proposed method was superior to the IGM.…”

Section: Automobile Mpg Prediction and Boston Housing Data Setsmentioning

confidence: 99%

“…Figure 12 illustrates the performance of parallel GA across generations. Here, we finally obtained the best parameters (number of clusters: c = [3,9,4,5,9,7,6], weighting exponent: m = 1.9285) when the number of linguistic contexts is seven (p = 7). As shown in Figure 12, we determine the best case of the fitness value across 30 generations when the number of contexts varies from five to eight.…”

Section: Coagulant Dosing Process In a Water Purification Plantmentioning

confidence: 99%

A Design for Genetically Oriented Rules-Based Incremental Granular Models and Its Application

Byeon

Kwak

2017

Symmetry

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Abstract:In this paper, we develop a genetically oriented rule-based Incremental Granular Model (IGM). The IGM is designed using a combination of a simple Linear Regression (LR) model and a local Linguistic Model (LM) to predict the modeling error obtained by the LR. The IGM has been successfully applied to various examples. However, the disadvantage of IGM is that the number of clusters in each context is determined, with the same number, by trial and error. Moreover, a weighting exponent is set to the typical value. In order to solve these problems, the goal of this paper is to design an optimized rule-based IGM with the use of a Genetic Algorithm (GA) to simultaneously optimize the number of cluster centers in each context, the number of contexts, and the weighting exponent. The experimental results regarding a coagulant dosing process in a water purification plant, an automobile mpg (miles per gallon) prediction, and a Boston housing data set revealed that the proposed GA-based IGM showed good performance, when compared with the Radial Basis Function Neural Network (RBFNN), LM, Takagi-Sugeno-Kang (TSK)-Linguistic Fuzzy Model (LFM), GA-based LM, and IGM itself.

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References

2022

Artificial Intelligence and Data Analytics for Energy Exploration and Production

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Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

Cited by 14 publications

References 40 publications

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

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

A Design for Genetically Oriented Rules-Based Incremental Granular Models and Its Application

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