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We propose a novel approach based on Deep Learning methods using a convolutional neural networks (CNNs) to compare observed four-dimensional (4-D) seismic data with reservoir simulation model results and select the simulation models with the best match. Our approach advantages are twofold: 1) it is not a pixel-based approach, as it learns spatial features through training examples and 2) it does not require the simulation model and 4-D seismic maps to be in the same domain, enabling a direct comparison of maps in the seismic domain with maps in the numerical simulation domain. Additionally, we present a new methodology to quantitatively assess the performance of each method relative to the expected results. For this purpose, we build an annotated dataset containing the maps that best match the 4-D seismic in different realistic scenarios while taking into account the specialists' knowledge. We apply our methodology to evaluate if a method is able to perform according to what specialists expect, considering the annotated data. We also propose an improvement to traditional methods using a statistical method to convert maps of different physical properties into a common property, which can be used as an alternative to avoid forward and/or inversion procedures commonly used to bring 4-D seismic and simulation data to the same domain. Finally, we present an extensive comparison, qualitative and quantitative, of our approaches with other methods in the literature, such as the traditional pixelwise strategy for maps in the same domain, and methods based on binarization of maps in different domains. The results show that our proposed methods are capable of more accurately identifying the best-matched models, according to the specialists' answers.

The current work introduces an automatized process coupled with geological modeling to reduce uncertainty in reservoir properties. The method assimilates dynamic data of wells interactively, and applies the deviations to constrain uncertainties. The methodology deals with scalar (e.g. rock compressibility) and spatial (e.g. porosity) attributes at same time, employing specialized uncertainty reduction procedures. The procedure reduces the uncertainty of scalar attributes through Iterative Discrete Latin HyperCube method (IDLHC). To reduce uncertainties of spatial attributes, we worked on an extension of a regionalized co-simulation (co-DSS) method. The main contributions regard the proposition of update, at same time, both kinds of uncertainties (spatial and scalar); the definition of sequential rules, that simplify the process execution and avoiding subjectivities on the coupling of the geological modeling on data assimilation, as well as the automation of the process. The procedure was validated under a siliciclastic black-oil benchmark field (UNISIM-I-M), established based on Namorado Field, Campos Basin, Brazil. The procedure reduced the range of uncertainty of the scalar attributes, centralizing final PDFs with the values presented in the reference model, without collapse to a particular level, as well as preserved the geological consistency throughout data assimilation, obtaining porosity responses in agreement with the reference porosity distribution. The potential of the procedure is supported by the consistent production forecast observed in the outcomes.

History matching (HM) is a complex process that aims to increase the reliability of reservoir simulation models. HM is an inverse problem with multiple solutions that calls for a probabilistic approach. When observed data are integrated with sampling methods, uncertainty can be reduced by updating the probability density function (pdf) of the attributes. This work presents a practical methodology to systematically reduce uncertainties in a multi-objective probabilistic assisted history matching, integrating well and 4D seismic data (4DS) quantitatively. The pdf update goes through an iterative process. The pdf of the current iteration is combined with the pdf generated using the best-matched models to generate the posterior pdf. The multiple local objective functions (OF) are evaluated independently, allowing the identification of OFs that indicate the requirement of re-parameterization. This quantitative process was applied in two phases: (I) using only well data to constrain the models; (II) adding 4DS data. The methodology was successfully validated against a benchmark case of medium complexity, with the history-production data generated at fine scale (reference model). The proposed methodology reduced uncertainties for the majority of attributes, achieving the highest probable levels similar to those of the reference values. The identification of objective functions that restricts the match allowed improving of the parameterization employed, by locally adding probabilistic perturbations to the petrophysical realizations. The method efficiently achieved multiple matched simulation models, with all (87) local objective functions within the defined tolerance range. Each iteration increased the number of matched models, demonstrating good convergence. The uncertainties were reduced gradually, avoiding premature level elimination (minimizing convergence at an incorrect solution). In both phases, the matched simulation models presented similar production forecasts to the reference model. The 4D seismic data were included regionally with an acceptable increase in computation time. The quantitative incorporation of 4D seismic data generated models that forecasted production with less variability than models generated without 4DS data. This was expected as for this study, 4D seismic data provided perfect information.

Assisted history matching procedures are usually implemented within an optimization procedure, where the goal is to minimize the object function, which is written in terms of the error between observed and simulated data. One of the problems of the process is that the objective function represented by a single value is not sufficient to express the complexity of the problem. The errors that are normally measured for each well and for all production rates and pressure are converted to a single value; usually the norm of all error vectors. If the problem is well behaved and the objective function quickly converges within a desired tolerance, this would not be a problem, but this is usually not the case for complex history matching processes, since it is very difficult to find a model (or a set of them) that matches with a reasonable tolerance the production profiles for every wells. This work proposes a new approach to deal simultaneously with several objective functions (for instance, well rates and pressure). The methodology follows a probabilistic approach where several simulation models are handled during the entire procedure. The initial step is to generate several simulation models by combining the most important uncertainties of the reservoir. Then, an iterative procedure is performed to iteratively change the reservoir attributes and filter the models that are closer to history data. This procedure encompass a re-characterization step, where the probability of the attributes are updated, global multipliers are applied and local changes are made around problematic wells in order to provide a set of model that yield production and pressure curves with better dispersion compared to history data for every well. The key point of the methodology is that for every iteration the errors of each model, well and objective function can be visualized in a very concise plot that is based on the normalized quadratic error of each curve. The plot clearly shows global and local problems of the set of simulation models, so it is a good indicator of the changes to be made in the next iteration. Another point to highlight is that the same type of iterative procedure is performed to integrate 4D seismic into the process. Thus, after selecting a set of models with a good representation of well history data the same iterative process is repeated to generate a new set of models that match 4D seismic data as well. Thus, the proposed methodology integrates 4D seismic and well history data to reduce uncertainties with a probabilistic approach. To validate the methodology an application is shown where it can be seen the gradual improvement achieved for the models along the iterations.