Inversion of reservoir porosity, saturation, and permeability based on a robust hybrid genetic algorithm

Abstract

Reservoir parameter inversion is an essential geophysical problem to quantify the volume of an oil and gas reservoir. This problem can be identified as a nonlinear multiparameter optimization problem, in which the rock-physics model links seismic measurements including the velocity of the P- and S-waves to reservoir properties including porosity, water saturation, and permeability. Due to the complexity of the rock-physics and the nonlinear relationship, local search methods fail if the starting model is too far from the true model. Stochastic methods such as the genetic algorithm have global search ability, but they suffer from weak local search ability and premature convergence. We have developed a novel hybrid genetic algorithm to overcome these drawbacks of conventional genetic algorithm. We have used concepts of the self-adaptive method and evaluation criteria from the simulated annealing method to develop a new self-adaptive selection operator and a new self-adaptive hybrid crossover operator. With the two new operators, the hybrid genetic algorithm has better local search ability and is able to prevent premature convergence. We applied the hybrid genetic algorithm to the reservoir parameter inversion problem, and we used the Biot-squirt model as a rock-physics model. Numerical experiments demonstrated the improved search performance of the hybrid genetic algorithm against conventional methods in terms of solution quality, convergence rate, and convergence speed. We also tested our method on a series of laboratory and field data, which also confirmed the effectiveness of our method.

Joint inversion for facies and petrophysical properties based on a bi‐level optimization model

“…Therefore, we propose a new definition of an individual to represent a discrete-continuous candidate solution based on the bi-level optimization model (Equations 19 and 20). In addition, we develop a new local search strategy for the genetic algorithm because the general genetic algorithms usually perform poorly in local searches, which limits the computational efficiency (Fang & Yang, 2015).…”

“…If the stop condition is reached, the iteration ends, and (𝑓 𝑀 , 𝐥 𝑀 ) is output as the optimal solution. (4) Calculate the self-adaptive fitness Θ 𝑖 = Θ 𝑖 𝑔(𝑑 𝑖,𝑀 ), where 𝑔(𝑑) = [1 + exp(−5(𝑑 − 0.5))] −1(Fang & Yang, 2015…”

A unified poroviscoelastic model with mesoscopic and microscopic heterogeneities

“…As seismic waves carry information about the subsurface characteristics [1,2], the establishment of accurate physical and mathematical models is of great importance to understand the wave propagation in real rocks. An accurate mathematical model is also the basis of seismic modeling and seismic inversion [3,4]. In seismic exploration, the development of models to predict the characteristics of seismic waves in porous media is a considerable challenge.…”

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

“…Detailed description is referred to Davis (1987), Goldberg (1989), Buckles and Petry (1992) and Forrest (1993). GA was first used by geophysicists in the early 1990s Gallagher et al, 1991;Smith et al, 1992;Scales et al, 1992) and has been used mainly in the area of seismic inversion Sen and Stoffa, 1992;Sambridge and Drijkoningen, 1992;Gallagher and Sambridge, 1994;Mallick, 1995Mallick, , 1999Boschetti et al, 1995Boschetti et al, , 1996Morgan et al, 2012;Tran and Hiltunen, 2012a;Padhi andMallick, 2013, 2014;Li and Mallick, 2015;Fang and Yang, 2015).…”