Integration of well data into geostatistical seismic amplitude variation with angle inversion for facies estimation

Abstract

We have developed a new iterative geostatistical seismic amplitude variation with angle (AVA) inversion algorithm that inverts prestack seismic data, sorted by angle gathers, directly for highresolution density, P-wave velocity, S-wave velocity, and facies models. This novel iterative geostatistical inverse procedure is based on two key main principles: the use of stochastic sequential simulation and cosimulation as the perturbation technique of the model parameter space and a global optimizer based on a crossover genetic algorithm to converge the simulated earth models toward an objective function, in this case, the mismatch between the recorded and synthetic prestack seismic data. As a geostatistical approach, all the elastic models simulated during the iterative pro cedure honors the well-log data at its own locations, the marginal prior distributions of P-wave velocity and S-wave velocity, and density estimated from the available well-log data, and the corresponding joint distributions between density versus P-wave velocity and P-wave versus S-wave velocity. We successfully tested and implemented this new algorithm on a synthetic prestack data set that mimicked the main properties of a real reservoir, and on a real seismic data set acquired over a deepwater turbidite oil reservoir. In both cases, the results showed a good convergence between the recorded and synthetic seismic. The synthetic example showed high-resolution inverted petroelastic models that reproduced the true petroelastic models. The inverted petroelastic models retrieved from the real case study found high resolution and do agree with previous seismic reservoir characterization studies.

Exploring seismic inversion methodologies for non‐stationary geological environments: a benchmark study between deterministic and geostatistical seismic inversion

“…); and geostatistical seismic AVA inversion directly to facies (GAVAFAC; Azevedo et al . ). Since we want to focus this paper on the retrieved subsurface elastic inverse models and the parameterisation of each seismic inversion technique, a detailed description of each of these inversion methodologies is not presented here.…”

“…The integration of the available data within the geo‐modelling workflow should be done in such a way that the differences in the scale support and the uncertainty related with each kind of data are not neglected but, on the other hand, integrated within the solution (e.g., Azevedo et al . ).…”

“…; Azevedo et al . ) search the model parameter space considerably more but are computational expensive, and the solution to the model parameter space is not mathematically tractable.…”

Geostatistics for Seismic Characterization of Oil Reservoirs

“…The first group of stochastic methodologies approach the seismic inversion as an optimization problem in an iterative and convergent process. This includes what are traditionally designated by iterative geostatistical seismic inversion methods, from the seminal work by Bortolli et al (1993), until the most recent geostatistical inversion methods (Soares et al 2007;Nunes et al 2012;Azevedo et al 2015;Azevedo and Soares 2017). The second group of stochastic seismic inversion algorithms is known by linearized Bayesian inverse methodologies.…”

“…A summary of a real application example, using a fullstack seismic volume acquired offshore Brazil, illustrates herein the method (a detailed description of the dataset is available in Azevedo et al 2015). The best-fit Ip model (Fig.…”