Modeling Approach for Niger- Delta Oil Formation Volume Factor Prediction Using Artificial Neural Network

2012 ◽  
Author(s):  
S.S. Ikiensikimama ◽  
I.I. Azubuike
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Niger Delta ◽  
Volume Factor ◽  
Modeling Approach ◽  
Oil Formation Volume Factor ◽  
Artificial Neural ◽  
Oil Formation

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.

Artificial neural network based hybrid modeling approach for flood inundation modeling

2021 ◽  
Vol 592 ◽  
pp. 125605
Author(s):  
Shuai Xie ◽  
Wenyan Wu ◽  
Sebastian Mooser ◽  
Q.J. Wang ◽  
Rory Nathan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Modeling ◽  
Flood Inundation ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Flood Inundation Modeling ◽  
Inundation Modeling

scholarly journals Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Well ◽  
Data Driven ◽  
Well Placement ◽  
Heterogeneous Reservoirs ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Well Data ◽  
Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

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.

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