Structural Uncertainty Modelling and Updating by Production Data Integration

Seiler, Alexandra; Rivenæs, Jan C.; Aanonsen, Sigurd Ivar; Evensen, Geir

doi:10.2118/125352-ms

Cited by 18 publications

(13 citation statements)

References 32 publications

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“…Seiler et al [152] introduced structural uncertainty to the standard EnKF workflow, where depth of the top and bottom surface of the reservoir is estimated through the assimilation of production data. All the other reservoir property are assumed known.…”

Section: Larger Variety Of Model Parametersmentioning

confidence: 99%

Recent progress on reservoir history matching: a review

2010

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History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. In this review paper, we will summarize key developments in history matching and then review many of the accomplishments of the past decade, including developments in reparameterization of the model variables, methods for computation of the sensitivity coefficients, and methods for quantifying uncertainty. An attempt has been made to compare representative procedures and to identify possible limitations of each.

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Section: Larger Variety Of Model Parametersmentioning

confidence: 99%

Recent progress on reservoir history matching: a review

2010

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“…phenomena occurring during operation so as to analyze and predict behavior as a function of time. The reservoir simulation can be used as well in inverse engineering problems for optimizing existing numerical models and couple the dynamic/historic data (production) in the simulation [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Methods in Oil Recovery Processes and Reservoir Simulation

Druetta¹,

Tesi²,

Persis³

et al. 2016

ACES

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The exploitation of an oil field is a complex and multidisciplinary task, which demands a lot of prior knowledge, time, and money. A good reservoir characterization is deemed essential in the accomplishment of Enhanced Oil Recovery (EOR) processes in order to estimate accurately the properties of the porous medium affecting the flow properties. Several techniques at a field scale are currently being used to determine these properties, which are time and money consuming. But these alone do not guarantee the success of the project. Reservoir simulation and numerical techniques were then included in the pre-development and follow-up studies as an effective tool to determine the productivity and future behavior of the oil field. As the computational power increased, more advanced and detailed models were developed, including different chemical and physical phenomena. But alongside this process, there was an active research in the area of reservoir simulation, improving the accuracy and efficiency of the numerical schemes used for the flow, transport, and energy equations. The aim of this review is to address the topics described. Firstly, the origin of an oil recovery process, the economic factors and field tests involved are introduced. Secondly, the oil and porous medium origin and characterization as well as an introduction to the fundamental concepts and equations are associated to reservoir simulation. Finally, a brief description and analysis of the techniques are used in reservoir simulation employing finite difference methods, their downsides and possible ways to overcome these problems.

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“…Thore et al [19] underline the importance of assessing the sources of uncertainty in the structural modelling and show how this has impact on gross rock-volume calculations, well planning, flow simulation and history matching. In Seiler et al [17] and Seiler et al [18] the structural uncertainty modelling is included in the history matching workflow by accounting for the uncertainty in horizon and fault modelling. Røe et al [16] describe how the uncertainty associated with depth migration, seismic interpretation and well data can be incorporated in the structural modelling by presenting a flexible method for stochastic simulation of fault shape and position based on a parametric fault model.…”

Section: Introductionmentioning

confidence: 99%

Fault displacement modelling using 3D vector fields

et al. 2012

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In history matching and sensitivity analysis, flexibility in the structural modelling is of great importance. The ability to easily generate multiple realizations of the model will have impact both on the updating workflow in history matching and uncertainty studies based on Monte Carlo simulations. The main contribution to fault modelling by the work presented in this paper is a new algorithm for calculating a 3D displacement field applicable to a wide range of faults due to a flexible representation. This gives the possibility to apply this field to change the displacement and thereby moving horizons and fault lines. The fault is modelled by a parametric format where the fault has a reference plane defined by a centre point, dip and strike angles. The fault itself is represented as a surface defined by a function z = f (x, y), where x, y and z are coordinates local to the reference plane, with the z-axis being normal to the plane. The displacement associated with the fault outside the fault surface is described by a 3D vector field. The displacement on the fault surface can be found by identifying the intersection lines between horizons and the fault surface (fault lines), and using kriging techniques to fill in values at all points on the surface. Away from the fault surface the displacement field is defined by a monotonic decreasing function which ensures zero displacement at a specified distance from the fault. An algorithm is developed where the displacement can be increased or decreased according to user-defined parameters. This means that the whole displacement field is changed and points on horizons around the fault can be moved accordingly by applying the modified displacement field on them. The interaction between several faults influencing the same points is taken care of by truncation rules and the ordering of the faults. The method is demonstrated on a realistic synthetic case based on a real reservoir.

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Structural Uncertainty Modelling and Updating by Production Data Integration

Cited by 18 publications

References 32 publications

Recent progress on reservoir history matching: a review

Recent progress on reservoir history matching: a review

Methods in Oil Recovery Processes and Reservoir Simulation

Fault displacement modelling using 3D vector fields

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