Hydraulic flow units' estimation from seismic data using artificial intelligence systems, an example from a gas reservoir in the Persian Gulf

Abstract In recent years, considering the reservoir pressure drop in productive wells, designing the optimal well trajectory for production and injection in enhanced oil recovery (EOR) plans requires to determine hydraulic flow unit (HFU) in the reservoir. HFUs can also be used for petrophysical zonation of reservoirs as well as permeability predictions in uncored intervals or zones with low quality core data of wells. In the present study we have tried to integrate 3D seismic data with well data in order to find a quantitative relationship between flow zone index (FZI) and seismic attributes through employing linear regression and artificial intelligence models. Using this approach, FZI can be predicted using the information which is propagated in the whole field and achievable in the early stage of field development and consequently a suitable HFUs model may be represented. To this end, the suitable attributes for FZI estimation were selected by stepwise linear regression from extracted acoustic impedance (AI) and sample based attributes. Afterward, three optimal intelligent systems including probabilistic neural network (PNN), multi-layer feed forward network (MLFN), and radial basis function networks (RBFN) were employed. The obtained results reveal that PNN is the most accurate estimator compared to MLFN, RBFN, and multi-attribute regression methods. In the final stage, PNN was applied to develop 3D hydraulic flow unit model for the reservoir section of the investigated carbonate gas field located in Persian Gulf.

[1]  F. Coren,et al.  Gas hydrate physical properties imaging by multi-attribute analysis — Blake Ridge BSR case history , 2001 .

[2]  Amir Hatampour,et al.  Hydraulic flow units, depositional facies and pore type of Kangan and Dalan Formations, South Pars Gas Field, Iran , 2015 .

[3]  Yangkang Chen,et al.  Random noise attenuation by f-x empirical mode decomposition predictive filtering , 2014 .

[4]  Weilin Huang,et al.  Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method , 2016 .

[5]  Hikari Fujii,et al.  Permeability Prediction by Hydraulic Flow Units - Theory and Applications , 1996 .

[6]  Ernesto Della Rossa,et al.  Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion , 2010 .

[7]  H. Rahimpour-Bonab,et al.  FLOW UNIT DISTRIBUTION AND RESERVOIR MODELLING IN CRETACEOUS CARBONATES OF THE SARVAK FORMATION, ABTEYMOUR OILFIELD, DEZFUL EMBAYMENT, SW IRAN , 2012 .

[8]  M. Enamul Hossain,et al.  Modified Kozeny–Carmen correlation for enhanced hydraulic flow unit characterization , 2011 .

[9]  Yangkang Chen,et al.  Random noise attenuation using local signal-and-noise orthogonalization , 2015 .

[10]  Amit Kumar Srivastava,et al.  Estimation of effective porosity using geostatistics and multiattribute transforms: A case study , 2004 .

[11]  Amir Maher Sayed Lala,et al.  The application of petrophysics to resolve fluid flow units and reservoir quality in the Upper Cretaceous Formations: Abu Sennan oil field, Egypt , 2015 .

[12]  Amir Hatampour,et al.  Estimation of NMR Total and Free Fluid Porosity from Seismic Attributes Using Intelligent Systems: A Case Study from an Iranian Carbonate Gas Reservoir , 2017 .

[13]  Quincy Chen,et al.  Seismic attribute technology for reservoir forecasting and monitoring , 1997 .

[14]  John Quirein,et al.  Use of multiattribute transforms to predict log properties from seismic data , 2001 .

[15]  J. Ghiasi-Freez,et al.  Prediction of Flow Units in Heterogeneous Carbonate Reservoirs Using Intelligently Derived Formula: Case Study in an Iranian Reservoir , 2014 .

[16]  K. Spikes,et al.  Probabilistic seismic inversion based on rock-physics models for reservoir characterization , 2007 .

[17]  Liang Xiao,et al.  Tight-Gas-Sand Permeability Estimation From Nuclear-Magnetic-Resonance (NMR) Logs Based on the Hydraulic-Flow-Unit (HFU) Approach , 2013 .

[18]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[19]  Javad Ghiasi-Freez,et al.  Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran , 2012, Comput. Geosci..

[20]  Characterization of flow units in shaly sand reservoirs—Hassi R'mel Oil Rim, Algeria , 2006 .

[21]  Peyman Pourafshary,et al.  Improved Method to Identify Hydraulic Flow Units for Reservoir Characterization , 2015 .

[22]  Kevin P. Dorrington,et al.  Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction , 2004 .

[23]  Bruce S. Hart,et al.  Comparison of linear regression and a probabilistic neural network to predict porosity from 3‐D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico , 2001 .