Prediction Of Dew Point Pressure Research Articles

Application of Deep Neural Network-Artificial Neural Network Model for Prediction Of Dew Point Pressure in Gas Condensate Reservoirs from Field-X in the Niger Delta Region Nigeria

Reservoirs of natural gas and gas condensate have been proposed as a potential for providing affordable and cleaner energy sources to the global population growth and industrialization expansion simultaneously. This work evaluates reservoir simulation for production optimization using Deep Neural network - artificial neural network (DNN-ANN) model to predict the dew point pressure in gas condensate reservoirs from Field-X in the Niger Delta Region of Nigeria. The dew-point pressure (DPP) of gas condensate reservoirs was estimated as a function of gas composition, reservoir temperature, molecular weight and specific gravity of heptane plus percentage. Results obtained show that the mean relative error (MRE) and R-squared (R2) are 0.99965 and 3.35%, respectively, indicating that the model is excellent in predicting DPP values. The Deep Neural Network - Artificial Neural Network (DNN-ANN) model is also evaluated in comparison to earlier models created by previous authors. It was recommended that the DNN - ANN model developed in this study could be applied to reservoir simulation and modeling well performance analysis, reservoir engineering problems and production optimization.

Prediction of Dew Point Pressure in Gas Condensate Reservoirs Based On a Combination of Gene Expression Programming (GEP) and Multiple Regression Analysis

Prediction of Dew Point Pressure in Gas Condensate Reservoirs Based On a Combination of Gene Expression Programming (GEP) and Multiple Regression Analysis

GA-RBF model for prediction of dew point pressure in gas condensate reservoirs

This study presents the application of an intelligent algorithm in estimation of dew point pressure (DPP) of gas condensate systems. Radial Basis Function (RBF) network in conjunction with Genetic Algorithm (GA) as an optimization algorithm was utilized for this aim. The performance of the proposed GA-RBF model was investigated by statistical and graphical analysis of results. The analysis show that the implemented model is precise and robust in prediction of experimental DPP data. Furthermore, the GA-RBF model was compared with a literature intelligent approach (GEP model) as well as three well-known correlations. The comparison reveals that the GA-RBF model is superior to other model and correlations and successfully improves the predictions.

Rapid method for the estimation of dew point pressures in gas condensate reservoirs

The production of condensate, in addition to gas can improve the recovery factor of gas condensate reservoirs, as well as increase the economic feasibility of the reservoir. Dew point pressure (DPP) is regarded as one of the vital parameters for characterizing a gas condensate reservoir. The accurate estimation of DPP is however still a major challenge for reservoir engineers. In this study, a consistent, accurate, and simple-to-use model is proposed for the prediction of DPP in gas condensate reservoirs using a reliable soft-computing approach known as gene expression programming (GEP). The computational approach utilizes a comprehensive dataset of DPP, as well as properties of C7+, reservoir temperature, and hydrocarbon and non-hydrocarbon reservoir fluid compositions. The model proposed is compared to three well-known empirical correlations. The proposed model produces an average absolute relative deviation of approximately 7.88% and is clearly superior to previously published methods for the prediction of dew point pressure in gas condensate reservoirs.

Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm

Knowing dew point pressure considers as one of the preliminary requirements in retrograde gas condensate reservoir simulations. When the pressure declines below the dew point pressure, the condensate dropouts form, which could lead to a substantial decrease in gas relative permeability and well deliverability. Different methods such as equation of states, empirical correlations and experimental procedures have been proposed to determine the dew point pressure. However, due to their convergence problem, being expensive and time consuming, great efforts have been taken to develop an alternative method. In this study, a new method based on artificial neural network has been developed and optimized by genetic algorithm as an evolutionary technique. A data set consists of 308 sample collected from different sources and literature including one of Iranian gas-condensate field is used. Reservoir temperature, mole percentage of gas components and heavy fractions properties were considered as input parameters to this model. The performance of the proposed model was compared with some of the common correlations and Peng–Robinson equation of state. The results confirmed the accuracy and capability of this model in determination of dew point pressure based on 2.46%, 3.66%, 95.91%, 0.02% and 24.39% as average absolute deviation, root mean square error, correlation of determination, minimum and maximum percentage error; respectively. The sensitivity analysis is also performed on variables to determine the impact and importance of each parameter on prediction of dew point pressure. The results show that plus fraction properties and C3–C4 fraction have the greatest positive and negative impacts on estimation of dew point pressure; respectively.

A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

Dew point pressure (DPP) is one of the most important parameters to characterize gas condensate reservoirs. Experimental determination of DPP in a window pressure-volume-temperature cell is often difficult especially in case of lean retrograde gas condensate. Therefore, searching for fast and robust algorithms for determination of DPP is usually needed. This paper presents a new approach based on artificial neural network (ANN) to determine DPP. The back-propagation learning algorithms were used in the network as the best approach. Then equations for DPP prediction by using weights of the network were generated. With the obtained correlation, the user may use such results without a running the ANN software. Consequently, this new model is compared with results obtained using other conventional models to make evaluation among different techniques. The results show that the neural model can be applied effectively and afford high accuracy and dependability for DPP forecasting for the wide range of gas properties and reservoir temperatures.

Application of evolutionary Gaussian processes regression by particle swarm optimization for prediction of dew point pressure in gas condensate reservoirs

One of the most critical quantities for characterizing a gas condensate reservoir is dew point pressure. But, accurate determination of dew point pressure is a very challengeable task in reservoir development. Experimental measurement of dew point pressure in PVT (Pressure, Volume, Temperature) cell is often difficult, especially in the case of lean retrograde gas condensate. So, different empirical correlations and equations of state are developed by researchers to calculate this property. Empirical correlations do not have ability to reliably duplicate the temperature behavior of constant composition fluids, and equations of state have convergence problem and need to be tuned against some experimental data. In addition, these approaches are not generalizable to unseen data, and they usually memorize the data used to develop them. In this paper, we develop an intelligent model to predict dew point pressure of gas condensate reservoirs using Gaussian processes optimized by particle swarm optimization. The developed model is generalizable and can estimate unseen data with the same distribution of training data accurately. Results show that the proposed method in this paper outperforms the previous published models and correlations.

Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs

Dew point pressure is one of the most critical quantities for characterizing a gas condensate reservoir. So, accurate determination of this property has been the main challenge in reservoir development and management. The experimental determination of dew point pressure in PVT cell is often difficult especially in case of lean retrograde gas condensate. Empirical correlations and some equations of state can be used to calculate reservoir fluid properties. Empirical correlations do not have ability to reliable duplicate the temperature behavior of constant composition fluids. Equations of state have convergence problem and need to be tuned against some experimental data. Complexity, non-linearity and vagueness are some reservoir parameter characteristic which can be propagated simply by intelligent system. With the advantage of fuzzy sets in knowledge representation and the high capacity of neural nets (NNs) in learning knowledge expressed in data, in this paper a neural fuzzy system(NFS) is proposed to predict dew point pressure of gas condensate reservoir. The model was developed using 110 measurements of dew point pressure. The performance of the model is compared against performance of some of the most accurate and general correlations for dew point pressure calculation. From the results of this study, it can be pointed out that this novel method is more accurate and reliable with the mean square error of 0.058%, 0.074% and 0.044% for training, validation and test processes, respectively.