Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs

Birkle, Peter; Zouch, Mariem; Alzaqebah, Malek; Alwohaibi, Maram

doi:10.3997/2214-4609.201902193

Cited by 5 publications

(7 citation statements)

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“…Machine learning has become more prominent recently in many research fields, and this is due to the fast data growth and the need to meaningfully use them. Machine learning concerns discovering useful information from huge data using some machine learning techniques including anomaly detection, classification, and clustering [1,2]. Accordingly, dimensionality can impede the machine learning process as it incurs high computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…Such a huge number of features could not be handled by traditional machine learning methods. Feature selection is therefore vital as a preprocessing phase as it decreases data dimensionality while also removing duplicating and useless features in the dataset [2][3][4]. Feature selection process aims to obtain the optimal set of useful features while maintaining good accurateness in representing the initial features of the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…When compared with filter approach, wrapper approach possess more computational costs aside from showing an over-fitting risk. However, in embedded techniques, the features selection method is embedded within the model (s) training process [2,4,11], followed by the generation of an ideal group of features through the optimization of the objective function. Among the three mentioned types of feature selection, wrapper methods are chosen in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Alzaqebah

Jawarneh

Mohammad

et al. 2021

IJECE

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Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Alzaqebah

Jawarneh

Mohammad

et al. 2021

IJECE

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“…Many feature selection algorithms have been discovered and widely used by scientists and researchers in experimental. Methods for feature selection are divided into three types depending on their relations with the classifiers [2,3], these types are: The filter method works on overall characteristics of the data regardless of the classifier select the valuable features. The wrapper methods use optimization techniques to optimize the prediction process and the selected features.…”

Section: Introductionmentioning

confidence: 99%

“…The wrapper methods use optimization techniques to optimize the prediction process and the selected features. And the embedded methods, in the embedded method the feature selection is connected to the classification having the advantages of wrapper method which contain the interaction with the classification, while filter methods are less consumption of computer resources than wrapper methods [2][3][4]. Yet, this type is much robustness than in the wrapper method.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Alzaqebah¹,

Alrefai²,

Ahmed³

et al. 2020

IJECE

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Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.

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Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

et al. 2021

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Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.

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Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs

Cited by 5 publications

References 0 publications

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

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