Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network - Wavelet transform approach

Display Omitted Various reservoir models have been detected using novel coupled MLP-DWT model.Our proposed model detected reservoir models from training set with TCA=95.37%.Our coupled model recognized reservoir models from test set with TCA of 94.34%.The proposed model has been validated using synthetic and actual noisy real data. Well testing analysis is performed for detecting oil and gas reservoir model and estimating its associated parameters from pressure transient data which are often recorded by pressure down-hole gauges (PDG). The PDGs can record a huge amount of bottom-hole pressure data, limited computer resources for analysis and handling of these noisy data are some of the challenging problems for the PDGs monitoring. Therefore, reducing the number of the recorded data by PDGs to a manageable size is an important step in well test analysis. In the present study, a discrete wavelet transform (DWT) is employed for reducing the amount of long-term reservoir pressure data obtained for eight different reservoir models. Then, a multi-layer perceptron neural network (MLPNN) is developed to recognize reservoir models using the reduced pressure data. The developed algorithm has four steps: (1) generating pressure over time data (2) converting the generated data to log-log pressure derivative (PD) graphs (3) calculating of the multi-level discrete wavelet coefficient (DWC) of the PD graphs and (4) using the approximate wavelet coefficients as the inputs of a MLPNN classifier. Sensitivity analysis confirms that the most accurate reservoir model predictions are obtained by the MLPNN with 17 hidden neurons. The proposed method has been validated using simulated test data and actual field information. The results show that the suggested algorithm is able to identify the correct reservoir models for training and test data sets with total classification accuracies (TCA) of 95.37% and 94.34% respectively.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2]  Jitendra Kikani,et al.  Multi-resolution Analysis of Long-Term Pressure Transient Data Using Wavelet Methods , 1998 .

[3]  Behzad Vaferi,et al.  Simulation of dynamic pressure response of finite gas reservoirs experiencing time varying flux in the external boundary , 2015 .

[4]  Hsin Rau,et al.  Learning-based automated negotiation between shipper and forwarder , 2006, Comput. Ind. Eng..

[5]  S. Akin.  Integrated nonlinear regression analysis of tracer and well test data , 2003 .

[6]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[7]  K. Shanti Swarup,et al.  Artificial neural network using pattern recognition for security assessment and analysis , 2008, Neurocomputing.

[8]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[9]  Elif Derya Übeyli,et al.  A recurrent neural network classifier for Doppler ultrasound blood flow signals , 2006, Pattern Recognit. Lett..

[10]  Roland N. Horne,et al.  Processing and Interpretation of Long-Term Data Acquired From Permanent Pressure Gauges , 2002 .

[11]  Tong Heng Lee,et al.  Geometrical interpretation and architecture selection of MLP , 2005, IEEE Transactions on Neural Networks.

[12]  Morris Muskat,et al.  Use of Data Oil the Build-up of Bottom-hole Pressures , 1937 .

[13]  Behzad Vaferi,et al.  Model identification for gas condensate reservoirs by using ANN method based on well test data , 2014 .

[14]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[15]  Han-Chung Yang,et al.  Potential hazard analysis from the viewpoint of flow measurement in large open-channel junctions , 2012, Natural Hazards.

[16]  Roland N. Horne,et al.  Automatic Parameter Estimation From Well Test Data Using Artificial Neural Network , 1995 .

[17]  Ali N. Akansu,et al.  Emerging applications of wavelets: A review , 2010, Phys. Commun..

[18]  Cheng-Wu Chen,et al.  RETRACTED: Neural network-based fuzzy logic parallel distributed compensation controller for structural system , 2013 .

[19]  A. Jahanmiri,et al.  Prediction of transient pressure response in the petroleum reservoirs using orthogonal collocation , 2012 .

[20]  Zarita Zainuddin,et al.  Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data , 2011, Appl. Soft Comput..

[21]  Teresa Bernarda Ludermir,et al.  Wavelet Filter for Noise Reduction and Signal Compression in an Artificial Nose , 2003, HIS.

[22]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[23]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[24]  Jan-Erik Nordtvedt,et al.  Improved Wavelet Filtering and Compression of Production , 2005 .

[25]  V. Dua.  A mixed-integer programming approach for optimal configuration of artificial neural networks , 2010 .

[26]  M. Lashkarbolooki,et al.  Comparison the capability of artificial neural network (ANN) and EOS for prediction of solid solubil , 2011 .

[27]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[28]  Mohamed Kamoun,et al.  Stabilization of unknown nonlinear systems using neural networks , 2008, Appl. Soft Comput..

[29]  D. Bourdet,et al.  Supplement to SPE 12777, Use of Pressure Derivative in Well-Test Interpretation , 1989 .

[30]  Behzad Vaferi,et al.  Application of Recurrent Networks to Classification of Oil Reservoir Models in Well-testing Analysis , 2015 .

[31]  Madhusudan Singh,et al.  New fuzzy wavelet neural networks for system identification and control , 2005, Appl. Soft Comput..

[32]  C. K. Shum,et al.  Fuzzy-wavelet based prediction of Earth rotation parameters , 2011, Appl. Soft Comput..

[33]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering , 2006 .

[34]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[35]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[36]  Francisco Martínez-Álvarez,et al.  Neural networks to predict earthquakes in Chile , 2013, Appl. Soft Comput..

[37]  Ali Mohebbi,et al.  Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks , 2006 .

[38]  Behzad Vaferi,et al.  Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks , 2011 .