In this study, we apply the streamline-based production data integration method to condition a multimillion cell geologic model to historical production response for a giant Saudi Arabian reservoir. The field has been under peripheral water injection with 16 injectors and 70 producers. There is also a strong aquifer influx into the field. A total of 30 years of production history with detailed rate, infill well and re-perforation schedule were incorporated via multiple pressure updates during streamline simulation. Also, gravity and compressibility effects were included to account for water slumping and aquifer support. To our knowledge, this is the first and the largest such application of production data integration to geologic models accounting for realistic field conditions. We have developed novel techniques to analytically compute the sensitivities of the production response in the presence of gravity and changing field conditions. This makes our method computationally extremely efficient. The field application takes less than 6 hours to run on a PC. The geologic model derived after conditioning to production response was validated using field surveillance data. In particular, the flood front movement, the aquifer encroachment and bypassed oil locations obtained from the geologic model was found to be consistent with field observations. Finally, an examination of the permeability changes during production data integration revealed that most of these changes were aligned along the facies distribution, particularly the 'good' facies distribution with no resulting loss in geologic realism. We also propose a novel assisted history matching procedure for finite difference simulators using streamline derived sensitivity calculations. Unlike existing assisted history matching techniques where the user is required to manually adjust the parameters, this procedure combines the rigor of finite difference models and efficiencies of streamline simulators to perform history matching. Finite difference simulator is used to solve for pressure, flux and saturations which, in turn, are used as input for the streamline simulator for estimating the parameter sensitivities analytically. The streamline derived sensitivities are then used to update the reservoir model. The updated model is then used in the finite difference simulator in an iterative mode until a significant satisfactory history match is obtained. The assisted history matching procedure has been tested for both synthetic and field examples. The results show a significant speed-up in history matching using conventional finite difference simulators.

This dissertation presents several new techniques for the analysis of the long-term production performance of tight gas wells. The main objectives of this work are to determine pressure response function for long-term production for a the slightly compressible liquid case, to determine the original gas in place (OGIP) during pseudosteady state (PSS), to determine OGIP in the transient period, and to determine the effects of these parameters on linear flow in gas wells. Several methods are available in the industry to analyze the production performance of gas wells. One common method is superposition time. This method has the advantage of being able to analyze variable-rate and variable-pressure data, which is usually the nature of field data. However, this method has its shortcomings. In this work, simulation and field cases illustrate the shortcomings of superposition. I present a new normalized pseudotime plotting function for use in the superposition method to smooth field data and more accurately calculate OGIP. The use of this normalized pseudotime is particularly important in the analysis of highly depleted reservoirs with large change in total compressibility where the superposition errors are largest. The new tangent method presented here can calculate the OGIP with current reservoir properties for both constant rate and bottomhole flowing pressure (pwf) production. In this approach pressure-dependent permeability data can be integrated into a modified real gas pseudopressure,m(p), which linearizes the reservoir flow equations and provides correct values for permeability and skin factor. But if the customary real-gas pseudopressure, m(p) is used instead, erroneous values for permeability and skin factor will be calculated. This method uses an exponential equation form for permeability vs. pressure drop. Simulation and field examples confirm that the new correction factor for the rate dependent problem improves the linear model for both PSS and transient period, whether plotted on square-root of time or superposition plots.

Interwell tracers help us understand flow patterns within the reservoir and in getting reliable information of the reservoir continuity. Thus, one can obtain different information about the reservoir barriers, fractures and productivity from the amount of tracer produced at each tracer. The main objective of this study is an attempt to model interwell connectivity by analytically calculating missing tracer data in oil fields for the next step of the calculation of swept volume. The feasibility of using analytical solutions to estimate early data and check differences was carried out. In general; all of these applications refer to the applicability and relative ease of using tracers in oil field. The idea is to determine if it is possible to get a good estimate of the swept pore volumes at an early time before the tracer flood is finished since it often takes a long time to capture the complete tracer tail and there is great value in being able to get an early estimate of the results Results indicate that the extrapolation of tracer tail and using the residence time distribution method give accurate sweep volume predictions without the need to wait for long times to get the full tracer profile. / text

Made available in DSpace on 2015-03-13T17:08:22Z (GMT). No. of bitstreams: 1 Flavio_LS.pdf: 1955029 bytes, checksum: 8e0fa408c324ef805ccd084d89be3a06 (MD5) Previous issue date: 2005-07-15 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The history match procedure in an oil reservoir is of paramount importance in order to obtain a characterization of the reservoir parameters (statics and dynamics) that implicates in a predict production more perfected. Throughout this process one can find reservoir model parameters which are able to reproduce the behaviour of a real reservoir.Thus, this reservoir model may be used to predict production and can aid the oil file management. During the history match procedure the reservoir model parameters are modified and for every new set of reservoir model parameters found, a fluid flow simulation is performed so that it is possible to evaluate weather or not this new set of parameters reproduces the observations in the actual reservoir. The reservoir is said to be matched when the discrepancies between the model predictions and the observations of the real reservoir are below a certain tolerance. The determination of the model parameters via history matching requires the minimisation of an objective function (difference between the observed and simulated productions according to a chosen norm) in a parameter space populated by many local minima. In other words, more than one set of reservoir model parameters fits the observation. With respect to the non-uniqueness of the solution, the inverse problem associated to history match is ill-posed. In order to reduce this ambiguity, it is necessary to incorporate a priori information and constraints in the model reservoir parameters to be determined. In this dissertation, the regularization of the inverse problem associated to the history match was performed via the introduction of a smoothness constraint in the following parameter: permeability and porosity. This constraint has geological bias of asserting that these two properties smoothly vary in space. In this sense, it is necessary to find the right relative weight of this constrain in the objective function that stabilizes the inversion and yet, introduces minimum bias. A sequential search method called COMPLEX was used to find the reservoir model parameters that best reproduce the observations of a semi-synthetic model. This method does not require the usage of derivatives when searching for the minimum of the objective function. Here, it is shown that the judicious introduction of the smoothness constraint in the objective function formulation reduces the associated ambiguity and introduces minimum bias in the estimates of permeability and porosity of the semi-synthetic reservoir model / O processo de ajuste de hist?rico de produ??o em um reservat?rio de petr?leo ? de fundamental import?ncia para que se possa obter uma caracteriza??o dos par?metros do reservat?rio (est?ticos e din?micos) que implique em uma previs?o de produ??o mais acurada. Atrav?s deste processo pode-se encontrar par?metros para um modelo de reservat?rio que sejam capazes de reproduzir o comportamento do reservat?rio real. Assim, esse modelo de reservat?rio pode ser utilizado em previs?es de produ??o e no aux?lio ao gerenciamento do campo de ?leo/g?s. No processo de ajuste de hist?rico, os par?metros do modelo do reservat?rio s?o modificados e para cada modelo com o novo conjunto de par?metros, uma simula??o de fluxo ? realizada para que se possa avaliar se este conjunto reproduz ou n?o as curvas de produ??o de um reservat?rio real. O reservat?rio ? ajustado quando as discrep?ncias entre as previs?es do modelo de reservat?rio e a do reservat?rio real s?o abaixo de certa toler?ncia. Determinar um modelo de reservat?rio por meio do processo de ajuste de hist?rico requer a minimiza??o de uma fun??o objetivo (diferen?a entre a produ??o observada e simulada) em um espa?o de par?metros que em geral possui muitos m?nimos, ou seja, mais de um modelo de reservat?rio ajusta as observa??es. No sentido da n?o-unicidade da solu??o, o problema inverso associado ao processo de ajuste de hist?rico ? mal-posto. A fim de reduzir esta ambig?idade e regularizar o problema, ? necess?ria a incorpora??o de informa??es a priori e de v?nculos nos par?metros do reservat?rio a serem determinados. Neste trabalho, a regulariza??o do problema inverso associado ao ajuste de hist?rico foi realizada por meio da introdu??o de um v?nculo de suavidade nos par?metros: porosidade e permeabilidade, de um reservat?rio. Esse v?nculo possui o vi?s geol?gico de que os valores de porosidade e permeabilidade variam suavemente ao longo do reservat?rio. Nesse sentido, ? necess?rio encontrar um valor do peso deste v?nculo, na fun??o objetivo, que estabilize o problema e ainda introduza nos par?metros do modelo de reservat?rio o menor vi?s geol?gico poss?vel

“History matching” (model calibration, parameter identification) is an established method for determination of representative reservoir properties such as permeability, porosity, relative permeability and fault transmissibility from a measured production history; however the uniqueness of selected model is always a challenge in a successful history matching. Up to now, the uniqueness of history matching results in practice can be assessed only after individual and technical experience and/or by repeating history matching with different reservoir models (different sets of parameters as the starting guess). The present study has been used the stochastical theory of Kullback & Leibler (K-L) and its further development by Akaike (AIC) for the first time to solve the uniqueness problem in reservoir engineering. In addition - based on the AIC principle and the principle of parsimony - a penalty term for OF has been empirically formulated regarding geoscientific and technical considerations. Finally a new formulation (Penalized Objective Function, POF) has been developed for model selection in reservoir history matching and has been tested successfully in a North German gas field. / „History Matching“ (Modell-Kalibrierung, Parameter Identifikation) ist eine bewährte Methode zur Bestimmung repräsentativer Reservoireigenschaften, wie Permeabilität, Porosität, relative Permeabilitätsfunktionen und Störungs-Transmissibilitäten aus einer gemessenen Produktionsgeschichte (history). Bis heute kann die Eindeutigkeit der identifizierten Parameter in der Praxis nicht konstruktiv nachgewiesen werden. Die Resultate eines History-Match können nur nach individueller Erfahrung und/oder durch vielmalige History-Match-Versuche mit verschiedenen Reservoirmodellen (verschiedenen Parametersätzen als Startposition) auf ihre Eindeutigkeit bewertet werden. Die vorliegende Studie hat die im Reservoir Engineering erstmals eingesetzte stochastische Theorie von Kullback & Leibler (K-L) und ihre Weiterentwicklung nach Akaike (AIC) als Basis für die Bewertung des Eindeutigkeitsproblems genutzt. Schließlich wurde das AIC-Prinzip als empirischer Strafterm aus geowissenschaftlichen und technischen Überlegungen formuliert. Der neu formulierte Strafterm (Penalized Objective Function, POF) wurde für das History Matching eines norddeutschen Erdgasfeldes erfolgreich getestet.

“History matching” (model calibration, parameter identification) is an established method for determination of representative reservoir properties such as permeability, porosity, relative permeability and fault transmissibility from a measured production history; however the uniqueness of selected model is always a challenge in a successful history matching. Up to now, the uniqueness of history matching results in practice can be assessed only after individual and technical experience and/or by repeating history matching with different reservoir models (different sets of parameters as the starting guess). The present study has been used the stochastical theory of Kullback & Leibler (K-L) and its further development by Akaike (AIC) for the first time to solve the uniqueness problem in reservoir engineering. In addition - based on the AIC principle and the principle of parsimony - a penalty term for OF has been empirically formulated regarding geoscientific and technical considerations. Finally a new formulation (Penalized Objective Function, POF) has been developed for model selection in reservoir history matching and has been tested successfully in a North German gas field. / „History Matching“ (Modell-Kalibrierung, Parameter Identifikation) ist eine bewährte Methode zur Bestimmung repräsentativer Reservoireigenschaften, wie Permeabilität, Porosität, relative Permeabilitätsfunktionen und Störungs-Transmissibilitäten aus einer gemessenen Produktionsgeschichte (history). Bis heute kann die Eindeutigkeit der identifizierten Parameter in der Praxis nicht konstruktiv nachgewiesen werden. Die Resultate eines History-Match können nur nach individueller Erfahrung und/oder durch vielmalige History-Match-Versuche mit verschiedenen Reservoirmodellen (verschiedenen Parametersätzen als Startposition) auf ihre Eindeutigkeit bewertet werden. Die vorliegende Studie hat die im Reservoir Engineering erstmals eingesetzte stochastische Theorie von Kullback & Leibler (K-L) und ihre Weiterentwicklung nach Akaike (AIC) als Basis für die Bewertung des Eindeutigkeitsproblems genutzt. Schließlich wurde das AIC-Prinzip als empirischer Strafterm aus geowissenschaftlichen und technischen Überlegungen formuliert. Der neu formulierte Strafterm (Penalized Objective Function, POF) wurde für das History Matching eines norddeutschen Erdgasfeldes erfolgreich getestet.