A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude

Joint History Matching of Multiple Types of Field Data in a 3D Field-Scale Case Study

This work presents an ensemble-based workflow to simultaneously assimilate multiple types of field data in a proper and consistent manner. The aim of using multiple field datasets is to improve the reliability of estimated reservoir models and avoid the underestimation of uncertainties. The proposed framework is based on an integrated history matching workflow, in which reservoir models are conditioned simultaneously on production, tracer and 4D seismic data with the help of three advanced techniques: adaptive localization (for better uncertainty quantification), weight adjustment (for balancing the influence of different types of field data), and sparse data representation (for handling big datasets). The integrated workflow is successfully implemented and tested in a 3D benchmark case with a set of comparison studies (with and without tracer data). The findings of this study indicate that joint history matching using production, tracer and 4D seismic data results in better estimated reservoir models and improved forecast performance. Moreover, the integrated workflow is flexible, and can be extended to incorporate more types of field data for further performance improvement. As such, the findings of this study can help to achieve a better understanding of the impacts of multiple datasets on history matching performance, and the proposed integrated workflow could serve as a useful tool for real field case studies in general.publishedVersio

Adequate model complexity and data resolution for effective constraint of simulation models by 4D seismic data

4D seismic data bears valuable spatial information about production-related changes in the reservoir. It is a challenging task though to make simulation models honour it. Strict spatial tie of seismic data requires adequate model complexity in order to assimilate details of seismic signature. On the other hand, not all the details in the seismic signal are critical or even relevant to the flow characteristics of the simulation model so that fitting them may compromise the predictive capability of models. So, how complex should be a model to take advantage of information from seismic data and what details should be matched? This work aims to show how choices of parameterisation affect the efficiency of assimilating spatial information from the seismic data. Also, the level of details at which the seismic signal carries useful information for the simulation model is demonstrated in light of the limited detectability of events on the seismic map and modelling errors. The problem of the optimal model complexity is investigated in the context of choosing model parameterisation which allows effective assimilation of spatial information in the seismic map. In this study, a model parameterisation scheme based on deterministic objects derived from seismic interpretation creates bias for model predictions which results in poor fit of historic data. The key to rectifying the bias was found to be increasing the flexibility of parameterisation by either increasing the number of parameters or using a scheme that does not impose prior information incompatible with data such as pilot points in this case. Using the history matching experiments with a combined dataset of production and seismic data, a level of match of the seismic maps is identified which results in an optimal constraint of the simulation models. Better constrained models were identified by quality of their forecasts and closeness of the pressure and saturation state to the truth case. The results indicate that a significant amount of details in the seismic maps is not contributing to the constructive constraint by the seismic data which is caused by two factors. First is that smaller details are a specific response of the system-source of observed data, and as such are not relevant to flow characteristics of the model, and second is that the resolution of the seismic map itself is limited by the seismic bandwidth and noise. The results suggest that the notion of a good match for 4D seismic maps commonly equated to the visually close match is not universally applicable

Integração de caracterização de reservatórios com ajuste de histórico baseado em poços piloto : aplicação ao campo Norne

Orientador: Denis José SchiozerTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de GeociênciasResumo: As incertezas inerentes à simulação numérica de reservatórios podem originar modelos com diferenças significativas relativamente aos dados dinâmicos observados. A redução destas diferenças, processo conhecido por ajuste de histórico, é muitas vezes acompanhada por certa negligência da consistência geológica dos modelos, comprometendo a confiabilidade no processo e nas previsões de produção. Para manter a consistência geológica dos modelos, é fundamental integrar iterativamente o processo de ajuste de histórico com a modelagem geoestatística do reservatório. Apesar das diversas abordagens apresentadas nas últimas décadas, este processo de integração continua a ser altamente desafiante. Este trabalho propõe um fluxograma de modelagem geológica integrado com um fluxograma de ajuste de histórico, baseado no conceito do ponto piloto. O método do ponto piloto é uma técnica de parametrização geoestatística aplicada a modelos de reservatório, gerados a partir de um conjunto de dados medidos e de dados sintéticos definidos em outros pontos do reservatório, designados por pontos piloto. Neste trabalho os dados sintéticos correspondem a poços sintéticos e, por isso, designados por poços piloto. A metodologia é aplicada a um reservatório real, o reservatório arenítico de Norne, testando, desta forma, os diferentes procedimentos num cenário de elevada complexidade. Numa primeira etapa, é efetuada uma caracterização das heterogeneidades geológicas através da classificação de electrofacies juntamente com um refinamento do malha de simulação, por forma a obter volumes de fácies e propriedades petrofísicas com elevada resolução. Esta etapa apresenta diversas vantagens: (1) permite-nos mapear as heterogeneidades de pequena escala materializadas por camadas muito finas de folhelho e carbonatos cimentados que poderão atuar como barreiras estratigráficas verticais à dispersão dos diferentes fluídos; (2) permite a definição de novos atributos a serem usados durante a fase de ajuste como permeabilidade e transmissibilidade verticais, diferentes curvas de permeabilidade relativa associadas a diferentes tipos de rocha e, sobretudo, a definição das propriedades a serem incluídas nos poços piloto; (3) aumenta o controle geológico do processo de ajuste de histórico. Após a classificação de electrofacies, os modelos de alta resolução são integrados num processo iterativo entre a modelagem geológica e um processo de ajuste de histórico probabilístico e multiobjectivo guiado por poços piloto. Um dos maiores desafios do método dos poços piloto reside na configuração a adotar (número, localização e propriedades a modificar), sendo a flexibilidade do método uma das suas maiores vantagens. A configuração tem em conta os dados de produção, linhas de fluxo e enquadramento geológico-estrutural. A flexibilidade do método é demonstrada por meio de dois estudos de caso: a geração de figuras sedimentares, como é exemplo, o canal construído no segmento-G; a capacidade para procurar a melhor localização das camadas carbonatadas, altamente restritiva ao deslocamento dos fluídos no segmento C. Em última análise, o processo iterativo de modelagem geológica e ajuste de histórico guiado por poços piloto permitiu obter modelos geologicamente mais fiáveis que honrassem ao mesmo tempo o dado observadoAbstract: The inherent uncertainties in numerical reservoir simulation can lead to models with significant differences to observed dynamic data. History matching reduces these differences but often neglects the geological consistency of the models, compromising forecasting reliability. To maintain the geological consistency of the models, the history-matching process must be integrated with geostatistical modeling. Despite many suggested approaches in recent decades, this integration process remains a challenge. This work proposes a geological modeling workflow integrated within a general history-matching workflow, utilizing the pilot point¿s concept (in this study assuming the form of pilot wells). The pilot point method is a geostatistical parameterization technique that calibrates a pre-correlated field, generated from measured values and a set of additional synthetic data at unmeasured locations in the reservoir, referred to as pilot points. In this study, the synthetic data corresponds to synthetic wells, henceforth referred to as pilot wells. The methodology is applied to a real, complex, sandstone reservoir, the Norne field. The geological heterogeneities are characterized, in detail, through electrofacies analysis and combined with a refined simulation grid, to create high-resolution facies and petrophysical 3D models. This stage has several advantages: (1) allows the mapping of fine-scale heterogeneities generally comprising decimeter shales and calcareous-cemented layers that may act as stratigraphic barriers to vertical fluid displacement; (2) allows the addition of new attributes used during the history-matching stage, such as properties used in the pilot wells, vertical permeability and transmissibility models, and different kr curves assigned to different rock types; and (3) increases geological control over the history-matching process. After analyzing electrofacies, the high-resolution datasets are integrated into an iterative loop between geostatistical modeling and a probabilistic, multi-objective history-matching process, guided by pilot wells. A key challenge using the pilot wells method is to optimize the pilot well configuration (number, location and properties to disturb), and the flexibility of the pilot well method is a principal advantage. The configuration includes production data, the preferred fluid flow paths (revealed during a streamline analysis) and the geological framework. The flexibility of the method is demonstrated in the two case studies presented here: generating specific sedimentary features (e.g. channels in the G-segment) and finding the best location for the cemented stringers responsible for the fluid behavior observed in C-segment. This work shows that the iterative process combining geological modeling and geostatistical-based history matching, guided by pilot wells, created geologically consistent models that honor observed dataDoutoradoReservatórios e GestãoDoutor em Ciências e Engenharia de Petróle