A Hybrid of Functional Networks and Support Vector Machine models for the prediction of petroleum reservoir properties

Anifowose, Fatai; Labadin, Jane; Abdulraheem, Abdulazeez

doi:10.1109/his.2011.6122085

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Thus, advanced linear regression models such as Lasso (LSS), Ridge (RDG) and ElasticNet (EN) with regularization parameters that resolve collinearity problems are employed . Support vector regressor (SVR) models achieve high prediction accuracies and broad generalization capacity independent of data dimensionality. ,, Decision tree regression models (DTR) as the name implies create a tree of related properties to define data relationships, yielding interpretable results. However, DTR is limited by overfitting, poor robustness, low bias and high variance making it a poor predictor for new data or continuous variables .…”

Section: Introductionmentioning

confidence: 99%

Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

Olofinnika,

Selveindran,

Patel

et al. 2024

Energy Fuels

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This study provides the proof-of-concept for identifying the most suitable machine-learning (ML) model that predicts minimum miscibility pressure (MMP) based on temperature, crude oil, and injected fluid composition. MMP defined as the lowest pressure injected gas developing miscibility with reservoir oil is crucial for gas-enhanced oil recovery. Slimtube experiments considered the most reliable for MMP predictions are time-consuming. Although researchers have considered ML to expedite MMP predictions, validation of the optimal model that integrates the main controlling factors remains outstanding. We tested eight ML models of different complexities to determine the most suitable for predicting MMP. The models were trained and tested using 75 and 25% of 142 publicly available slim-tube experiments and validated using six in-house slim-tube MMP experiments. The injected gas compositions varied and included H 2 S, CO 2 , N 2 , CH 4 , and C 2 + . We assessed model suitability using mean absolute error (MAE). Models with MAEs <7% estimated the MMP. The highest-performing model after testing and validation was the neural network. This work identifies the most suitable machine-learning technique for MMP prediction validated using recent experiments. The optimal model provides an instantaneous MMP chart for gas injections typical to a Permian field. Also, we demonstrate a workflow for recommending optimal injection gas compositions with low MMP and reduced emissions associated with gas EOR. The procedure ultimately reduces the cost of performing the slim-tube experiment.

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Section: Introductionmentioning

confidence: 99%

Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

Olofinnika,

Selveindran,

Patel

et al. 2024

Energy Fuels

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“…Architectural integration HML seamlessly wholly or partly combines the architecture of two or more traditional ML algorithms, in a complementary manner to evolve a more robust standalone algorithm. The most commonly used example is the adaptive neuro-fuzzy inference system (ANFIS) which is a combination of fuzzy logic and ANN principles (Anifowose et al, 2013). Another example of an architectural integration HML method is the naïve Bayes tree which combines the architectures of naïve Bayes and decision tree algorithms.…”

Section: (I) the Hml Based On Architectural Integrationmentioning

confidence: 99%

Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers

Josephat

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With the advent of machine learning (ML) techniques, various algorithms have been applied in previous studies to develop models for predicting soil fertility status. However, these models are observed to use varying fertility target classes, and variations have been reported in these models' predictive performances. As a result, practical applications of these models for obtaining the most accurate predictions may become hindered. While the weighted voting ensemble (WVE) ML technique can be used to improve soil fertility status prediction by aggregating individual models prediction, guaranteeing finding of an optimal WVE assignment weights is challenging. Whereas a brute exhaustive search procedure can be applied for the mentioned task, there is a lack of exploration on the exploitation of automated classifiers' precise weights combinations as search spaces for successful optimization. This research aims to develop a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated 1EXP(-)Z+ multi-precision weights of hybrid classifiers. Soil chemical properties and ML modeling algorithms for modeling soil fertility status were identified. Base hybrid ML classification models for predicting soil fertility status were evaluated using Tanzania as a case study. Finally, the base ML hybrids WVE models were optimized using brute exhaustive search procedure’s novel developed search spaces generation algorithm for guaranteed optimal solution finding. The research was designed using design science research methodology, with the application of unsupervised machine learning K-mean algorithm with a knee detection method to find the optimal number of soil fertility status target classes, and supervised learning algorithms were applied to model classifiers for those optimal classes. Three soil fertility target classes were identified by clustering technique. The model achieved on test data a predictive accuracy of 98.93%, with respective AUC of 82%, 83%, and 87% for low, medium, and high soil fertility targets classes. Whereas these performances are observed higher compared to models in previous studies, 92% correct classifications were obtained on validation against external unseen laboratory-based tested soil results. Therefore, soil testing laboratories and farmers should consider using the model to smartly manage soil fertility which may lead to improved crop growth and productivity. The government could set agricultural-related policies that require the use of the model by farmers with the provision of agricultural inputs subsidies. Future work could be to develop an integrated real-time web and mobile application for providing farmers with soil fertility status information.

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Hybrid Approaches for Time Series Prediction

Fontes

Silva

2019

Hybrid Intelligent Systems

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A Hybrid of Functional Networks and Support Vector Machine models for the prediction of petroleum reservoir properties

Cited by 10 publications

References 26 publications

Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers

Hybrid Approaches for Time Series Prediction

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