Hybrid functional networks for oil reservoir PVT characterisation

Predicting pressure-volume-temperature (PVT) properties of black oil is one of the key processes required in a successful oil exploration. As crude oils from different regions have different properties, some researchers have used API gravity, which is used to classify crude oils, to develop different empirical correlations for different classes of black oils. However, this manual grouping may not necessarily result in correlations that appropriately capture the uncertainties in the black oils. This paper proposes intelligent clustering to group black oils before passing the clusters as inputs to the functional networks for prediction. This hybrid process gives better performance than the empirical correlations, standalone functional networks and neural network predictions

Oil PVT characterisation using ensemble systems

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

Hybrid functional networks for PVT characterisation

Predicting pressure volume temperature properties of black oil is one of the key processes required in a successful oil exploration. As crude oils from different regions have different properties, some researchers have used API gravity, which is used to classify crude oils, to develop different empirical correlations for different classes of black oils. However, this manual grouping may not necessarily result in correlations that appropriately capture the uncertainties in the black oils. This paper proposes intelligent clustering to group black oils before passing the clusters as inputs to the functional networks for prediction. This hybrid process gives better performance than the empirical correlations, standalone functional networks and neural network predictions

Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs

 Bubble point pressure is of great significance in reservoir engineering calculations affecting the success of reservoir simulation. For determining this valuable parameter, experimental tests are the most reliable techniques; however, these measurements are costly and time-consuming. So, it is crucial to propose an empirical model for estimating bubble point pressure. The existing correlations mainly have large errors and develop based on restricted database from a specific geographical location. As a result, development of an all-inclusive correlation is essential. In current article, gene expression programming (GEP) was used to create a generalized model for bubble point pressure estimation. To do this, an all-inclusive source of data was utilized for training and testing the model from the petroleum industry. Several statistical approaches including both illustration tools and diverse error functions were utilized to show the supremacy of the developed GEP model. Consequently, the recommended model is the most accurate as compared to the similar correlations in literature with the average absolute relative error (AARE = 11.41%) and determination coefficient (R2 = 0.96). Furthermore, the solution gas-oil ratio shows to be the most influencing variable on determining bubble point pressure according to sensitivity analysis. The results of contour map analysis demonstrate that most portions of the experimental region are predicted via the GEP equation with fewer errors as compared to two well-known literature correlations. Finally, the proposed GEP model can be of high prominence for accurate bubble point pressure estimation.Cited as: Rostami, A., Daneshi, A., Miri, R. Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 126-134, doi: 10.26804/ager.2020.02.0

Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report

This Report summarizes the proceedings of the 2015 Les Houches workshop on Physics at TeV Colliders. Session 1 dealt with (I) new developments relevant for high precision Standard Model calculations, (II) the new PDF4LHC parton distributions, (III) issues in the theoretical description of the production of Standard Model Higgs bosons and how to relate experimental measurements, (IV) a host of phenomenological studies essential for comparing LHC data from Run I with theoretical predictions and projections for future measurements in Run II, and (V) new developments in Monte Carlo event generators.Comment: Proceedings of the Standard Model Working Group of the 2015 Les Houches Workshop, Physics at TeV Colliders, Les Houches 1-19 June 2015. 227 page

Prediction of Multivariable Properties of Reservoir Rocks by Using Fuzzy Clustering

AbstractForecasting of geological parameters is very important for decision making on investment to exploration of new hydrocarbon structures and fields. On the one hand, the complexity of this problem is originated from the nonlinearity and uncertainty of behavior of an ensemble of interrelated parameters changing with respect to the depth. This phenomenon is considered analogously to time series, where the depth plays the role of time. On the other hand, the available data are irregular over the depth, as represent different geological bodies with distinct properties. These features mandate necessity to consider multivariable time series of geological parameters with irregular intervals. In this paper, we consider multilag forecasting of five geological parameters over the depth. As a model of forecasting, fuzzy c-means based fuzzy if-then rules are used and this allows better capture of high complexity of the considered phenomena than the classical precise forecasting model. The experimental data show validity of the suggested approach