Prediction of reservoir fluid properties using machine learning (original) (raw)
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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.
Beni-Suef University Journal of Basic and Applied Sciences
Background Prediction of accurate crude oil viscosity when pressure volume temperature (PVT) experimental results are not readily available has been a major challenge to the petroleum industry. This is due to the substantial impact an inaccurate prediction will have on production planning, reservoir management, enhanced oil recovery processes and choice of design facilities such as tubing, pipeline and pump sizes. In a bid to attain improved accuracy in predictions, recent research has focused on applying various machine learning algorithms and intelligent mechanisms. In this work, an extensive comparative analysis between single-based machine learning techniques such as artificial neural network, support vector machine, decision tree and linear regression, and ensemble learning techniques such as bagging, boosting and voting was performed. The prediction performance of the models was assessed by using five evaluation measures, namely mean absolute error, relative squared error, mea...
Ensemble SVM for characterisation of crude oil viscosity
Journal of Petroleum Exploration and Production Technology, 2017
This paper develops ensemble machine learning model for the prediction of dead oil, saturated and undersaturated viscosities. Easily acquired field data have been used as the input parameters for the machine learning process. Different functional forms for each property have been considered in the simulation. Prediction performance of the ensemble model is better than the compared commonly used correlations based on the error statistical analysis. This work also gives insight into the reliability and performance of different functional forms that have been used in the literature to formulate these viscosities. As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction.
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.
Prediction of Oil and Gas Reservoir Properties using Support Vector Machines
International Petroleum Technology Conference, 2011
Artificial Intelligence techniques have been used in petroleum engineering to predict various reservoir properties such as porosity, permeability, water saturation, lithofacie and wellbore stability. The most extensively used of these techniques is Artificial Neural Networks (ANN). More recent techniques such as Support Vector Machines (SVM) have featured in the literature with better performance indices. However, SVM has not been widely embraced in petroleum engineering as a possibly better alternative to ANN. ANN has been reported to have a lot of limitations such as its lack of global optima. On the other hand, SVM has been introduced as a generalization of the Tikhonov Regularization procedure that ensures its global optima and offers ease of training. This paper presents a comparative study of the application of ANN and SVM models in the prediction of porosity and permeability of oil and gas reservoirs with carbonate platforms. Six datasets obtained from oil and gas reservoirs in two different geographical locations were used for the training, testing and validation of the models using the stratified sampling approach rather than the conventional static method of data division. The results showed that the SVM model performed better than the popularly used Feed forward Back propagation ANN with higher correlation coefficients and lower root mean squared errors. The SVM was also faster in terms of execution time. Hence, this work presents SVM as a possible alternative to ANN, especially, in the characterization of oil and gas reservoir properties. The new SVM model will assist petroleum exploration engineers to estimate various reservoir properties with better accuracy, leading to reduced exploration time and increased production.
Journal of Petroleum Exploration and Production Technology
By determining the hydraulic flow units (HFUs) in the reservoir rock and examining the distribution of porosity and permeability variables, it is possible to identify areas with suitable reservoir quality. In conventional methods, HFUs are determined using core data. This is while considering the non-continuity of the core data along the well, there is a great uncertainty in generalizing their results to the entire depth of the reservoir. Therefore, using related wireline logs as continuous data and using artificial intelligence methods can be an acceptable alternative. In this study, first, the number of HFUs was determined using conventional methods including Winland R35, flow zone index, discrete rock type and k-means. After that, by using petrophysical logs and using machine learning algorithms including support vector machine (SVM), artificial neural network (ANN), LogitBoost (LB), random forest (RF), and logistic regression (LR), HFUs have been determined. The innovation of th...
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...
Artificial intelligence approach to reservoir fluid classification
International Journal of Innovation and Applied Studies, 2014
Fluid classification is a critical factor in decision of reservoir and production problems. Reservoir fluid can be classified into five types according to laboratory and production data as black oil, volatile oil, gas condensate, wet gas and dry gas. In this work a novel application of Neural Networks (ANN) is presented. Based on production and laboratory data neural networks model is developed for automatic classification of reservoir FLUID. More than 450 samples of five types of reservoir fluids are used to develop the neural network model. About 70 % of data are accepted for neural network training, 15 % for validation and 15 % are used as test set. The importance of different input fluid properties in classification was studied. The different types of architectures for different groups of input data were tested to select the optimal neural network architecture by fitness criteria. The optimized neural network model was capable of classifying the reservoir fluids with high accuracy. The performance of ANNs models was determined by classification quality index and network error. The model has been applied successfully to classification of Yemeni fluids using different range of parameters. The results show that the proposed novel ANN model can achieve high accuracy.
Support vector regression for prediction of gas reservoirs permeability
2012
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of permeability because they are usually available for all of the wells. Hence, attempts have been made to utilize well log data to predict permeability. However, because of complicate and non-linear relationship of well log and core permeability data, usual statistical and artificial methods are not completely able to provide meaningful results. In this regard, recent works on artificial intelligence have led to the introduction of a robust method generally called support vector machine (SVM). The term “SVM” is divided into two subcategories: support vector classifier (SVC) and support vector regr...
arXiv (Cornell University), 2023
This field case study aims to address the challenge of accurately predicting petrophysical properties in heterogeneous reservoir formations, which can significantly impact reservoir performance predictions. The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predict permeability log from conventional logs and match it with core data. The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The study utilized the Flow Zone Indicator (FZI) rock typing technique to understand the factors influencing reservoir quality. The findings will be used to improve reservoir simulation and locate future wells more accurately. The study concluded that the FZI approach and machine learning algorithms are effective in predicting permeability log and improving reservoir performance predictions.