An intelligent heuristic-clustering algorithm to determine the most probable reservoir model from pressure–time series in underground reservoirs

Precise characterization of underground reservoirs requires accurate calculations of the reservoir’s petrophysical data and accurate selection of the mathematical model governing the reservoir’s dynamic. In this study, we develop a novel heuristic-clustering algorithm, namely GA–DBSCAN–KMEANS, that can be applied over pressure transient data to assess the true reservoir model out of a pool of candidates. In this algorithm, each specific reservoir model is considered a subpopulation in the GA (genetic algorithm). Then, the simultaneous optimization of all the reservoir models is sought using the proposed hybrid algorithm. During the optimization process, the population size of different models will be either decreased, increased, or unchanged based on the average quality match obtained for each model. A combined DBSCAN (density-based spatial clustering of applications with noise)–KMEANS clustering scheme is used to increase the population size for the best reservoir model in each iteration of the GA. The accuracy of the proposed algorithm was verified using several synthetic data and a real field case obtained from the open literature. The tested data were collected from different types of reservoir models, including homogeneous reservoirs, matrix-fracture dual-porosity reservoirs, and fault-limited reservoirs. For uncertainty analyses and to test the performance of the algorithm under large numbers of initializations, Monte Carlo simulations were conducted. Results of the Monte Carlo simulations unveiled high values of P 10, P 50, and P 90 for the probability of the true reservoir model and low values of these statistics for the false reservoir models. This shows that the outcome of the proposed algorithm is not affected by the initial randomization of the solution subspaces; hence, the developed algorithm is a reliable tool in determining the most probable reservoir model from transient well testing data.

[1]  Olivier Francois Allain,et al.  A Practical Artificial Intelligence Application in Well Test Interpretation , 1992 .

[2]  Sheik S. Rahman,et al.  An integrated model for multiobjective design optimization of hydraulic fracturing , 2001 .

[3]  D. T. Rian,et al.  Optimization Methods for History Matching of Complex Reservoirs , 2001 .

[4]  M. Adibifard,et al.  Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data , 2014 .

[5]  W. J. Lee,et al.  An Artificial Neural Network Approach To Identify the Well Test Interpretation Model: Applications , 1990 .

[6]  I. S. Sitanggang,et al.  Determination of Optimal Epsilon (Eps) Value on DBSCAN Algorithm to Clustering Data on Peatland Hotspots in Sumatra , 2016 .

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

[8]  Iraj Ershaghi,et al.  A Robust Neural Network Model for Pattern Recognition of Pressure Transient Test Data , 1993 .

[9]  Jonathan Carter,et al.  A Modified Genetic Algorithm for Reservoir Characterisation , 2000 .

[10]  R. Schulze-Riegert,et al.  Evolutionary Algorithms Applied to History Matching of Complex Reservoirs , 2002 .

[11]  Akhil Datta-Gupta,et al.  Handling Conflicting Multiple Objectives Using Pareto-Based Evolutionary Algorithm for History Matching of Reservoir Performance , 2013, ANSS 2013.

[12]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[13]  Dilza Szwarcman,et al.  Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints , 2009 .

[14]  A. Sima Etaner-Uyar,et al.  A Gene Based Adaptive Mutation Strategy for Genetic Algorithms , 2004, GECCO.

[15]  Kusum Deep,et al.  A new crossover operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[16]  A. Awotunde.  Estimation of well test parameters using global optimization techniques , 2015 .

[17]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[18]  Ali Nazari,et al.  Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks , 2012, Neural Computing and Applications.

[19]  Turgay Ertekin,et al.  The Development of an Artificial Neural Network as a Pressure Transient Analysis Tool for Applications in Double-Porosity Reservoirs , 2007 .

[20]  M. Wheeler,et al.  Optimal design of hydraulic fracturing in porous media using the phase field fracture model coupled with genetic algorithm , 2018, Computational Geosciences.

[21]  Meisam Adibifard,et al.  Using Particle Swarm Optimization (PSO) Algorithm in Nonlinear Regression Well Test Analysis and Its Comparison with Levenberg-Marquardt Algorithm , 2016, Int. J. Appl. Metaheuristic Comput..

[22]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[23]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[24]  Zahra Arab Aboosadi,et al.  Estimation of subsurface petrophysical properties using different stochastic algorithms in nonlinear regression analysis of pressure transients , 2018, Journal of Applied Geophysics.

[25]  Mohammad Sharifi,et al.  Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure , 2017 .

[26]  Meisam Adibifard,et al.  A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter , 2019, Neural Computing and Applications.

[27]  Ankit Garg,et al.  A multi-gene genetic programming model for estimating stress-dependent soil water retention curves , 2014, Computational Geosciences.

[28]  Joe M. Kang,et al.  Prediction of Nonlinear Production Performance in Waterflooding Project Using a Multi-Objective Evolutionary Algorithm , 2011 .

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

[30]  John C. Davis,et al.  Book Review: Introduction to statistical pattern recognition. 2nd edition, by Keinosuke Fukunaga, Academic Press, San Diego, 1990, 591 p., ISBN 0-12-269851-7, US$69.95 , 1996 .

[31]  Jonathan Carter,et al.  Using genetic algorithms for reservoir characterisation , 2001 .

[32]  Roland N. Horne,et al.  Uncertainty Assessment of Well Placement Optimization , 2001 .

[33]  Melanie Mitchell,et al.  Genetic algorithms: An overview , 1995, Complex..

[34]  R Kharat,et al.  DETERMINATION OF RESERVOIR MODEL FROM WELL TEST DATA, USING AN ARTIFICIAL NEURAL NETWORK , 2008 .

[35]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning , 2011 .

[36]  Roland N. Horne,et al.  Automated Reservoir Model Selection in Well Test Interpretation , 2001 .