Integration of Self Organizing Map with MLFF Neural Network to Predict Oil Formation Volume Factor: North Africa Crude Oil Examples

2020 ◽  
Author(s):  
Hossein Algdamsi ◽  
Ahmed Alkouh ◽  
Ammar Agnia ◽  
Ahmed Amtereg ◽  
Gamal Alusta
Keyword(s):  
Neural Network ◽  
Crude Oil ◽  
North Africa ◽  
Self Organizing Map ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Oil Formation ◽  
Self Organizing

Integration of Self Organizing Map and Date Driven Methods to Predict Oil Formation Volume Factor: North Africa Crude Oil Examples

2021 ◽  
Author(s):  
Gamal Alusta ◽  
Hossein Algdamsi ◽  
Ahmed Amtereg ◽  
Ammar Agnia ◽  
Ahmed Alkouh ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Statistical Prediction ◽  
Support Vector ◽  
Self Organizing Map ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Som Neural Network ◽  
Oil Formation ◽  
Self Organizing

Abstract In this paper we introduce for the first time an innovative approach for deriving Oil Formation Volume Factor (Bo) by mean of artificial intelligence method. In a new proposed application Self-Organizing Map (SOM) technology has been merged with statistical prediction methods integrating in a single step dimensionality reduction, extraction of input data structure pattern and prediction of formation volume factor Bo. The SOM neural network method applies an unsupervised training algorithm combined with back propagation neural network BPNN to subdivide the entire set of PVT input into different patterns identifying a set of data that have something in common and run individual MLFF ANN models for each specific PVT cluster and computing Bo. PVT data for more than two hundred oil samples (total of 804 data points) were collected from the north African region representing different basin and covering a greater geographical area were used in this study. To establish clear Bound on the accuracy of Bo determination several statistical parameters and terminology included in the presentation of the result from SOM-Neural Network solution. the main outcome is the reduction of error obtained by the new proposed competitive Learning Structure integration of SOM and MLFF ANN to less than 1 % compared to other method. however also investigated in this work five independents means of model driven and data driven approach for estimating Bo theses are 1) Optimal Transformations for Multiple Regression as introduced by (McCain, 1998) using alternating conditional expectations (ACE) for selecting multiple regression transformations 2), Genetic programing and heuristic modeling using Symbolic Regression (SR) and cross validation for model automatic tuning 3) Machine learning predictive model (Nearest Neighbor Regression, Kernel Ridge regression, Gaussian Process Regression (GPR), Random Forest Regression (RF), Support Vector Regression (SVM), Decision Tree Regression (DT), Gradient Boosting Machine Regression (GBM), Group modeling data handling (GMDH). Regression Model Accuracy Metrics (Average absolute relative error, R-square), diagnostic plot was used to address the more adequate techniques and model for predicting Bo.

Neural Network Model for Estimating the PVT Properties of Middle East Crude Oils

1999 ◽  
Vol 2 (03) ◽  
pp. 255-265 ◽  
Author(s):  
Ridha B.C. Gharbi ◽  
Adel M. Elsharkawy
Keyword(s):  
Neural Network ◽  
Middle East ◽  
Specific Gravity ◽  
Crude Oil ◽  
Average Error ◽  
Pvt Properties ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Data Points ◽  
Oil Formation

Summary The importance of pressure/volume/temperature (PVT) properties, such as the bubblepoint pressure, solution gas-oil ratio, and oil formation volume factor, makes their accurate determination necessary for reservoir performance calculations. An enormous amount of PVT data has been collected and correlated over many years for different types of hydrocarbon systems. Almost all of these correlations were developed with linear or nonlinear multiple regression or graphical techniques. Artificial neural networks, once successfully trained, offer an alternative way to obtain reliable results for the determination of crude oil PVT properties. In this study, we present neural-network-based models for the prediction of PVT properties of crude oils from the Middle East. The data on which the network was trained represent the largest data set ever collected to be used in developing PVT models for Middle East crude oils. The neural-network model is able to predict the bubblepoint pressure and the oil formation volume factor as a function of the solution gas-oil ratio, the gas specific gravity, the oil specific gravity, and the temperature. A detailed comparison between the results predicted by the neural-network models and those predicted by other correlations are presented for these Middle East crude-oil samples. Introduction In absence of experimentally measured pressure/volume/temperature (PVT) properties, two methods are widely used. These methods are equation of state (EOS) and PVT correlations. The equation of state is based on knowing the detailed compositions of the reservoir fluids. The determination of such quantities is expensive and time consuming. The equation of state involves numerous numerical computations. On the other hand, PVT correlations are based on easily measured field data: reservoir pressure, reservoir temperature, oil, and gas specific gravity. In the petroleum process industries, reliable experimental data are always to be preferred over data obtained from correlations. However, very often reliable experimental data are not available, and the advantage of a correlation is that it may be used to predict properties for which very little experimental information is available. The importance of accurate PVT data for material-balance calculations is well understood. It is crucial that all calculations in reservoir performance, in production operations and design, and in formation evaluation be as good as the PVT properties used in these calculations. The economics of the process also depends on the accuracy of such properties. The development of correlations for PVT calculations has been the subject of extensive research, resulting in a large volume of publications.1–10 Several graphical and mathematical correlations for determining the bubblepoint pressure (Pb) and the oil formation volume factor (Bob) have been proposed during the last five decades. These correlations are essentially based on the assumption that P b and Bob are strong functions of the solution gas-oil ratio (Rs) the reservoir temperature (T), the gas specific gravity (?g) and the oil specific gravity (?o) or P b = f 1 ( R s , T , γ g , γ o ) , ( 1 ) B o b = f 2 ( R s , T , γ g , γ o ) . ( 2 ) In 1947, Standing1 presented graphical correlations for the determination of bubblepoint pressure (Pb) and the oil formation volume factor (Bob) In developing these correlations, Standing used 105 experimentally measured data points from 22 different crude-oil and gas mixtures from California oil fields. Average relative errors of 4.8% and of 1.17% were reported for Pb and Bob respectively. Later, in 1958, Lasater9 developed an empirical equation based on Henry's law for estimating the bubblepoint pressure. He correlated the mole fraction of gas in solution to a bubblepoint pressure factor. A total of 137 crude-oil and gas mixtures from North and South America was used for developing this correlation. An average error of 3.8% was reported. Lasater did not present a correlation for Bob In 1980, two sets of correlations were reported, one by Vasquez and Beggs10 and the other by Glasø.7 Vasquez and Beggs used 600 data points from various locations all over the world to develop correlations for Pb and Bob. Two different types of correlations were presented, one for crudes with °API>30 and the other for crudes with °API 30. An average error of 4.7% was reported for their correlation of Bob Glasø used a total of 45 oil samples from the North Sea to develop his correlations for calculating Pb and Bob. He reported an average error of 1.28% for the bubblepoint pressure and ?0.43% for the formation volume factor. Recently, Al-Marhoun4 used 160 experimentally determined data points from the PVT analysis of 69 Middle Eastern hydrocarbon mixtures to develop his correlations. Average errors of 0.03% and ?0.01% were reported for Pb and Bob respectively. Dokla and Osman6 used a total of 50 data points from reservoirs in the United Arab Emirates to develop correlations for Pb and Bob. They reported an average error of 0.45% for the bubblepoint pressure and 0.023% for the formation volume factor. The conventional approach to develop PVT correlations is based on multiple-regression techniques. An alternative approach will be to use an artificial neural network (ANN). PVT models based on a successfully trained ANN can be excellent, reliable tools for the prediction of crude-oil PVT properties. The massive interconnections in the ANN produces a large number of degrees of freedom, or fitting parameters, and thus may allow it to capture the system's nonlinearity better than conventional regression techniques. Recently, artificial neural networks have found use in a number of areas in petroleum engineering.11–20 The objective of this study is to use ANNs to develop accurate PVT correlations for Middle East crude oil to estimate Pb and Bob as functions of Rs, T, ?g, ?o. With additional experimental data, the neural-network model can be further refined to incorporate these new data. In addition, in this article we evaluate the accuracy of the ANN models developed in this study compared to other PVT correlations.

Development of a Computationally Intelligent Model to Estimate Oil Formation Volume Factor

2021 ◽  
Author(s):  
Mohammad Rasheed Khan ◽  
Shams Kalam ◽  
Rizwan Ahmed Khan
Keyword(s):  
Crude Oil ◽  
Mean Squared Error ◽  
Evaluation Studies ◽  
Coefficient Of Determination ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Artificial Neural ◽  
Error Metric ◽  
Intelligent Model ◽  
Oil Formation

Abstract This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art computational intelligence (CI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserve evaluation studies and reservoir engineering calculations. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can aid in optimizing time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of Pakistan, Iran, UAE, and Malaysia. Resultantly, this allows to move step forward towards the creation of a generalized model. Multiple CI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models for CI are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, and crude oil API gravity. Comparative analysis of various CI models is performed using visualization/statistical analysis and the best model pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented. Graphical analysis using scatter plots with a coefficient of determination (R2) illustrates that ANN equation produces the most accurate predictions for oil FVF with R2 in excess of 0.96. Moreover, during this study an error metric is developed comprising of multiple analysis parameters; Average Absolute Error (AAE), Root Mean Squared Error (RMSE), correlation coefficient (R). All models investigated are tested on an unseen dataset to prevent the development of a biased model. Performance of the established CI models are gauged based on this error metric, which demonstrates that ANN outperforms the other models with error within 2% of the measured PVT values. A computationally derived intelligent model proves to provide the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.

Oil formation volume factor modeling: Traditional vs. Stochastically optimized neural networks

2013 ◽  
Vol 5 (4) ◽  
Author(s):  
Parisa Bagheripour ◽  
Mojtaba Asoodeh ◽  
Ali Asoodeh
Keyword(s):  
Neural Network ◽  
Pattern Search ◽  
Model Assessment ◽  
Local Minima ◽  
Dissolved Gas ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Data Points ◽  
Oil Formation

AbstractOil formation volume factor (FVF) is considered as relative change in oil volume between reservoir condition and standard surface condition. FVF, always greater than one, is dominated by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. In addition to limitations on reliable sampling, experimental determination of FVF is associated with high costs and time-consumption. Therefore, this study proposes a novel approach based on hybrid genetic algorithm-pattern search (GA-PS) optimized neural network (NN) for fast, accurate, and cheap determination of oil FVF from available measured pressure-volume-temperature (PVT) data. Contrasting to traditional neural network which is in danger of sticking in local minima, GA-PS optimized NN is in charge of escaping from local minima and converging to global minimum. A group of 342 data points were used for model construction and a group of 219 data points were employed for model assessment. Results indicated superiority of GA-PS optimized NN to traditional NN. Oil FVF values, determined by GA-PS optimized NN were in good agreement with reality.

Artificial Intelligence-based Predictive Technique to Estimate Oil Formation Volume Factor

2021 ◽  
Author(s):  
Shams Kalam ◽  
Mohammad Rasheed Khan ◽  
Rizwan Ahmed Khan
Keyword(s):  
Artificial Intelligence ◽  
Crude Oil ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Artificial Neural ◽  
Error Metric ◽  
Oil Formation

Abstract This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art artificial intelligence (AI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserves calculation and reservoir engineering studies. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can optimize time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of the Middle East region. Multiple AI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, bubble point pressure, and crude oil API gravity. Comparative analysis of developed AI models is performed using visualization/statistical analysis, and the best model is pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented. Graphical analysis is used to evaluate the performance of developed AI models. The results of scatter plots showed most of the points are lying on the 45 degree line. Moreover, during this study, an error metric is developed comprising of multiple analysis parameters; Average absolute percentage error (AAPE), Root Mean Squared Error (RMSE), coefficient of determination (R2). All models investigated are tested on an unseen dataset to prevent a biased model's development. Performance of the established AI models is gauged based on this error metric, demonstrating that ANN outperforms ANFIS with error within 1% of the measured PVT values. A computationally derived intelligent model provides the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.

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