The study area comprises an oil play with numerous opportunities, identifying sandstone sequences with proven potential. The main sequence was deposited in the Upper Miocene within a transitional environment (external neritic), resulting in the formation of bars in deltaic facies and channels, which represents an excellent quality and lateral extension of the storage rock, but also complexity due to internal variability. The trap is structural with closure against faults, formed in an extensive tectonic regime giving rise to normal faulting and increasing the degree of complexity for the characterization of the reservoirs.

Seismic data have accurate information about these characteristics, however, it is insufficient to solve the variability in the vertical scale, so incorporating all the information obtained in the wells through seismic inversion is essential when characterizing highly heterogeneous reservoirs with thin thickness. Furthermore, the geostatistical inversion combines Bayesian inference with a sampling algorithm called Markov Chain Monte Carlo (MCMC) that allows incorporating all the information from well logs, geological information, geostatistical parameters, and seismic data, generating models that honor the input data (Hameed et al., 2011). Additionally, the method provides a solution to the problem of non-uniqueness of the results, based on a statistical distribution of the multiple realizations derived from the initial model.

This work proposes a flow that integrates quantitative analysis, establishing a direct link between seismic measurements and well logs, which additionally, when combined with non-linear techniques such as geostatistical seismic inversion, can minimize the differences in scales, obtaining better models, more predictive and with quantification of uncertainty. The static workflow used consists of 6 main components: Pre-stacked gather conditioning, curve modeling by rock physics (Vp, Vs and Rho), geostatistical seismic inversion (impedance P, Vp/Vs ratio, density), determination of facies cubes (oil-sand, brine-sand and shale) and petrophysical properties (Vcl, Phie, permeability) using a robust algorithm combining Bayesian inference and Markov Chain Monte Carlo (MCMC), quantification of uncertainty and volumetric estimation by ranking multiple realizations (P10, P50, P90) and transfer to a geological mesh (upscaling) ready for numerical simulation without the use of typical extrapolation algorithms such as kriging or Sequential Gaussian Simulation (SGS), managing to minimize the scale differences, obtaining better models, more predictive and capable of estimating uncertainty.

With the results obtained, redefined geo-bodies were extracted, already discretizing the sandstones with good rock quality from the sandstones with good rock quality and bearing hydrocarbons to have greater precision in the development of these fields. Subsequently, the dynamic information was coupled to analyze the existing Pressure Transient Analyses (PTA) that have identified pseudo-steady state and the Rate Transient Analyses (RTA) to numerically model the response, checking the volumes obtained previously. Additionally, a benchmarking was considered with more than 590 oil producing fields in siliciclastics worldwide, considering the main properties of the fluid, porosity, facies and depositional environments and drive mechanisms, thus identifying new development opportunities with less uncertainty.

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