An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity (original) (raw)
Related papers
Oil PVT characterisation using ensemble systems
2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016
In reservoir engineering, there is always a need to estimate crude oil Pressure, Volume and Temperature (PVT) properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are often used instead of costly laboratory experiments to estimate these properties. However, these correlations do not always give sufficient accuracy. This paper develops ensemble support vector regression and ensemble regression tree models to predict two important crude oil PVT properties: bubblepoint pressure and oil formation volume factor at bubblepoint. The developed ensemble models are compared with standalone support vector machine (SVM) and regression tree models, and commonly used empirical correlations .The ensemble models give better accuracy when compared to correlations from the literature and more consistent results than the standalone SVM and regression tree models.
Prediction of reservoir fluid properties using machine learning
2018
The phase and volumetric behaviour of reservoir fluid properties, referred to as pressure-volumetemperature (PVT) properties, involve the thermodynamic studies of the fluid with respect to pressure, temperature and its volumetric compositions. PVT properties are usually determined by laboratory experiments performed on the actual samples of the reservoir fluid. Failing that, these fluid properties have been evaluated by some other methods such as equations of state, empirical correlations and recently, machine learning models. Machine learning is basically the prediction of the future with, (supervised learning), or without, (unsupervised learning), prior knowledge of the past. A common problem for the standalone machine learning technique is local minimum. In view of this, ensemble systems and hybrid techniques have been developed successfully for improvement in different fields. This work introduces two different ensemble methods based on support vector regression and regression trees where both ensemble approaches utilise a novel concept tagged "Tying Ranking" in selection of the base models. Also, a hybrid system for reservoir fluid characterisation with a novel way of grouping petroleum fluid properties using intelligent method was developed. The Declaration of Authorship Whilst registered as a candidate for the above degree, I have not been registered for any other research award. The results and conclusions embodied in this thesis are the work of the named candidate and have not been submitted for any other academic award.
Journal of Engineering Research and Reports, 2021
Oil viscosity is one of the most important physical and thermodynamic property used when considering reservoir simulation, production forecasting and enhanced oil recovery. Traditional experimental procedure is expensive and time consuming while correlations are replete however they are limited in precision, hence need for a new Machine Learning (ML) models to accurately quantify oil viscosity of Niger Delta crude oil. This work presents use of ML model to predict gas-saturated and undersaturated oil viscosities. The ML used is the Support Vector Machine (SVM), it is applicable for linear and non-linear problems, the algorithm creates a hyperplane that separates data into two classes. The model was developed using data sets collected from the Niger Delta oil field. The data set was used to train, cross-validate, and test the models for reliability and accuracy. Correlation of Coefficient, Average Absolute Relative Error (AARE) and Root Mean Square Error (RMSE) were used to evaluate the developed model and compared with other correlations. Result indicated that SVM model outperformed other empirical models revealing the accuracy and advantage SVM a ML technique over expensive empirical correlations.
2017
This thesis aims to estimate pore fluid saturation values in an oil sands reservoir using ensemble tree based machine learning models. Oil sands reservoirs provide an interesting opportunity to explore a relatively new technique in petrophysical analysis. The specific reservoir used in this study has high heterogeneity with discrete muddy layers that are difficult and time consuming to incorporate into a conventional petrophysical model. In addition, due to strong well control and sufficient well log data, the reservoir is a perfect candidate to test out a data-driven model by using techniques in Machine Learning – a subfield of Artificial Intelligence. Specifically, Random Forests and Extreme Gradient Boosted Trees are combined, which are two different ways to implement a decision-tree based model structure. The two algorithms have rapidly gained popularity in the machine learning community due to their robustness when dealing with outliers and/or bad data combined with a comparati...
Prediction of PVT properties in crude oil systems using support vector machines
2009
Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (P b ) and the oil formation volume factor (B ob Keywords-Support vector machine; support vector regression; PVT properties; bubble point pressue; oil formation volume factor.
International Journal of Frontiers in Engineering and Technology Research
Bubble point pressure (BPP) is a key parameter for oil and gas reservoir identification, characterization, and management. An accurate correlation of this property with the evolving digital technology of machine learning, in the absence of experimental PVT analysis, serves as guidance in the development and recovery of reservoir fluids. In this study, a predictive BPP correlation was derived by intrinsically linearizing a nonlinear multiple regression, with the best coefficients (global minimum) extracted using White-box (Linear Regression, Ridge Regression, and Lasso Regression) Machine Learning models. The new correlation was developed, validated, and tested using 314 measured PVT data points from the Niger Delta Region. The data were subdivided into four classes: extra-light crude for API > 45, light crude for 31.1 < API ≤ 45, medium crude for 22.3 < API ≤ 31.1, and heavy crude for API ≤ 22.3. Statistical evaluation metrics such as root mean squared error, average absolu...
Journal of Petroleum Science and Engineering, 2022
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Prediction Of Nigerian Crude Oil Viscosity Using Artificial Neural Network
Petroleum & Coal, 2009
The viscosity parameter is a very important fluid property in reservoir engineering computations. It should be determined in the laboratory but most of the time; the data is not either reliable or unavailable. Hence, empirical correlations were derived to estimate them. However, the success of the correlations in prediction depends on the range of data at which they were originally developed in the region. In this study, artificial neural network (ANN) was used to address the inaccuracy of empirical correlations used for predicting crude oil viscosity. The new artificial neural network model was developed to predict the crude oil viscosity using 32 data sets collected from the Niger Delta Region of Nigeria. About 17 data sets were used to train the model, 10 sets were used to test the accuracy of the model, and remaining 5sets to validate the relationships established during the training process. The test results revealed that the back propagation neural network model (BPNN) were better than the empirical correlations in terms of average absolute relative error and correlation coefficient.
Oil price prediction using ensemble machine learning
2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE), 2013
Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last couple of decades, both academicians and practitioners devoted proactive knowledge to address this issue. A strand of them has focused on some key factors that may influence the crude oil price prediction accuracy. This paper extends this particular branch of recent works by considering a number of influential features as inputs to test the forecasting performance of daily WTI crude oil price covering the period 4th January 1999 through 10th October 2012. Empirical results indicate that the proposed methods are efficient and warrant further research in this field.