... inference upstream oil & gas production forecasting reserves evaluation waic posterior distribution loo-cv predictive performance lfo-cv pareto distribution rate-time model artificial intelligence reservoir simulation approximation logistic growth model reservoir evaluation spe reservoir...

... that addresses the correlation between the model’s parameters solves the issues of mixing and autocorrelation of Markov chains and, thus, it speeds up speeding up the convergence of the Markov chains. Third, we generate replicated data from our posterior distributions to assess the robustness of our inferences...

... a hydraulic fracturing forward model. Posterior distributions and credible intervals were produced for the fracture length, matrix permeability, and skin factor. These credible intervals were then compared with true reservoir and hydraulic fracturing data. The methodology was also repeated for an actual case...

... in this work utilize three sampling vehicles, namely the Gibbs sampling (implemented in OpenBUGS, an open-source software), the Metropolis-Hastings (MH) algorithm, and approximate Bayesian computation (ABC) to sample parameter values from their posterior distributions. These different sampling algorithms...

.... Compositional simulations are conducted that incorporate a tuned pressure/volume/temperature (PVT) model and a set of measured cyclic injection/compaction pressure‐sensitive permeability data. Markov‐Chain Monte Carlo (MCMC) is used to estimate the posterior distributions of the model uncertain variables...

... associated with DCA models. This methodology does not require the estimation of the likelihood, which simplifies the Bayesian inference greatly. In approximate Bayesian computation (ABC) with rejection sampling, the posterior distribution is approximated by substituting different values of the decline...

... posterior distribution application truth case uncertain parameter data assimilation parameter space A central goal in reservoir management is determining how to produce oil and gas reservoirs effectively and profitability ( Thakur 1996 ). Reservoir simulation is regarded as a critical tool...

... of a posterior distribution for multiscale data integration using a hierarchical model and (b) implementing the MCMC method to explore the posterior distribution. A multiresolution MRF provides an efficient framework to integrate different scales of data hierarchically, provided that the coarse-scale...

... posterior distribution null 2 spe reservoir evaluation & engineering exp null 1 2 null exp null 1 2 In Eq. 3 , π ( x 1 ) is a prior distribution of the fine-scale represented by an MRF. We can generalize Eq. 3 to incorporate uncertainty in the prior spatial model: Fig. 2 Two...