A novel approach to sand production prediction using artificial intelligence

Abstract Over the years, accurate and early prediction of oil or gas well sanding potential has been of great importance in order to design an effective sand control management strategy. Significant technical and economic benefits can be achieved if the correct and early design of sand control method is considered. In this study, critical total drawdown (CTD) as an index of sand production onset is aimed to be estimated through 4 proposed methods. A total of 23 field data sets collected from problematic wells of North Adriatic Sea were used to develop these models. First, simple regression analysis was performed to recognize the statistically important affecting parameters. Using these variables, multiple linear regression (MLR) and genetic algorithm evolved MLR (GA-MLR) were developed for estimation of CTD. Two artificial neural networks (ANN) with back propagation (BP) and particle swarm optimization (PSO) algorithms were constructed to correlate CTD to all affecting parameters extracted from the literature. The performance comparison showed that the artificial intelligent system could be employed successfully in sanding onset prediction and minimizing the uncertainties. More accurate results were obtained when PSO algorithm was applied to optimize the weights and thresholds of neural network.

[1]  P. Valko,et al.  Predicting Critical Drawdown for the Onset of Sand Production , 2004 .

[2]  S. M. Lo,et al.  Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong , 2003, Neurocomputing.

[3]  Nobuo Morita,et al.  A Comprehensive Study of Sanding Rate From a Gas Field: From Reservoir to Completion, Production, and Surface Facilities , 2011 .

[4]  Paulin Coulibaly,et al.  Nonstationary hydrological time series forecasting using nonlinear dynamic methods , 2005 .

[5]  T. K. Perkins,et al.  Prediction of Sand Production in Gas Wells: Methods and Gulf of Mexico Case Studies , 1995 .

[6]  M. Ahmadi.  Neural network based unified particle swarm optimization for prediction of asphaltene precipitation , 2012 .

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Alireza Nouri,et al.  A comparison of two sanding criteria in physical and numerical modeling of sand production , 2006 .

[9]  Nobuo Morita,et al.  The Engineering Approach to Sand Production Prediction , 1987 .

[10]  D. R. Davies,et al.  Sand Production Prediction Review: Developing an Integrated Approach , 1991 .

[11]  Jean-Claude Roegiers,et al.  Fuzzy control: An alternate mean of sand exclusion in wells? , 1999 .

[12]  E. R. Jefferys.  Design Applications of Genetic Algorithms , 1993 .

[13]  Ibrahim Sami Nashawi,et al.  Estimating the fracture gradient coefficient using neural networks for a field in the Middle East , 2005 .

[14]  Ge Xiurun,et al.  An improved PSO-based ANN with simulated annealing technique , 2005, Neurocomputing.

[15]  Nobuo Morita,et al.  Parametric study of sand production prediction: analytical approach. , 1989 .

[16]  J. Almedeij,et al.  Influence of sand production on pressure drawdown in horizontal wells: Theoretical evidence , 2005 .

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

[18]  S. A. M. Dehghani,et al.  Minimum miscibility pressure prediction based on a hybrid neural genetic algorithm , 2008 .

[19]  Mazen Y. Kanj,et al.  Realistic Sanding Predictions: A Neural Approach , 1999 .

[20]  Ali Ghalambor,et al.  A Study of Relevant Parameters To Predict Sand Production in Gas Wells , 2002 .

[21]  Kwok-wing Chau,et al.  Application of a PSO-based neural network in analysis of outcomes of construction claims , 2007 .

[22]  Guohe Huang,et al.  Development of an artificial neural network model for predicting minimum miscibility pressure in CO2 flooding , 2003 .

[23]  F. J. Santarelli,et al.  Basin scale rock mechanics: Field observations of sand production , 1994 .

[24]  F. Esmaeilzadeh,et al.  Sand production control by chemical consolidation , 2009 .

[25]  Vahidoddin Fattahpour,et al.  An experimental investigation on the effect of rock strength and perforation size on sand production , 2012 .

[26]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .