Estimation of Petrophysical Parameters from Seismic Inversion by Combining Particle Swarm Optimization and Multilayer Li
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Original Paper
Estimation of Petrophysical Parameters from Seismic Inversion by Combining Particle Swarm Optimization and Multilayer Linear Calculator Qamar Yasin,1,2 Ghulam M. Sohail,3 Yan Ding,1,2,4 Atif Ismail,5 and Qizhen Du1,2,6 Received 23 July 2019; accepted 15 February 2020
In heterogeneous reservoir rocks, the accurate characterization of lithology and reservoir parameters is significant to minimize drilling risks and to improve oil and gas recoveries. In this work, a joint inversion strategy based on multilayer linear calculator (MLC) and particle swarm optimization (PSO) algorithm was applied to predict the spatial variations of key petrophysical (porosity, permeability, and saturation) and geomechanical parameters (YoungÕs modulus, PoissonÕs ratio, and brittleness) for inter-well regions. In this method, acoustic impedance (AI) models are computed from post-stack seismic amplitude data by applying the proposed strategy (MLC + PSO) and back propagation neural network-based seismic inversion in the time domain with measured log density and velocity as constraints. The obtained results reveal that the proposed strategy, which combines MLC and PSO, leads to the optimization of lateral and vertical facies heterogeneities and accurate prediction of reservoir parameter distribution, i.e., the low AI is related to sand facies and corresponds to high porosity, permeability, saturation, and mid-range of YoungÕs modulus. The time slice maps of inverted porosity and permeability at various time intervals indicate a reasonable calibration with the measured core and well log data. The methodology proposed in this study may be considered useful for other basins in Pakistan with similar geological settings and anywhere in the world for reservoir characterization, particularly for intercalated shale and variable depositional environments. KEY WORDS: Reservoir characterization, Particle swarm optimization, Multilayer linear calculator, Petrophysical modeling.
INTRODUCTION 1
The Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Changjiang West Road 66th, Qingdao 266580, China. 2 The Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China. 3 Department of Civil, Geological and Environmental Engineering, University of Saskatchewan, Saskatoon, Canada. 4 Beijing Rock Star Petroleum Technology Co., LTD, Beijing 102200, China. 5 Department of Geological Engineering, University of Engineering and Technology, Lahore, Pakistan. 6 To whom correspondence should be addressed; e-mail: [email protected]
In the past few decades, acoustic impedance (AI) mapping obtained from the inversion of poststack seismic amplitude data has become a common approach to predict spatial reservoir properties. The inverted models of AI, i.e., P-impedance, S-impedance, and density, can be further used for the estimation of lithofacies, petrophysical, and elastic parameters (Leiphart and Hart 2001; Walls et al. 2002; Pramanik et al. 2004; Calderon 2
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