Saturation Mapping From 4-D Seismic Data in the Statfjord Field

Doyen, P.M.; Psaila, David; Astratti, D.; Kvamme, L. B.; Al-Najjar, N.F.

doi:10.4043/12100-ms

Cited by 3 publications

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“…The choice of Wood's law, equation (12), maximise the sensitivity of the acoustic impedances to gas saturation. Water velocity Vw is then a function of p,T and the salinity s, while oil velocity Vo is a function of pressure, temperature and r s .…”

Section: Appendixmentioning

confidence: 99%

Integrated History-Matching of Production and 4D Seismic Data

Gosselin¹,

Berg²,

Cominelli³

2001

Proceedings of SPE Annual Technical Conference and Exhibition

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fax 01-972-952-9435. AbstractThe reservoir characterisation methods are still improving the integration of all available data for more accurate predictions, but the history-matching confrontation between observed and simulated data as predicted by the model remains the crucial test. Time-lapse 3D seismic data now provides a new type of information on the dynamics of fluids in the reservoir, relating variations of seismic signal to movement and/or depressurisation of hydrocarbons. This powerful reservoir-monitoring tool can be fully exploited when it is used to update the reservoir model in an integrated matching loop of all history data -4D data, spatially distributed over the whole reservoir, jointly with time series of well observations. In a classical gradient-based minimisation of an objective function, measuring the mismatch between observed and simulated data, we add a seismic term to the well term. This contribution, defined in terms of elastic parameter variations within the reservoir, comes from an inversion of the seismic signals. On the other hand the fluid flow simulator is coupled with a petro-elastic module to convert the simulated saturation/pressure and static rock properties into simulated elastic properties. The optimiser uses gradients of both terms to minimise the global mismatch, updating the reservoir properties. In this paper, we justify our approach using elastic parameters, describe the relationships to derive elastic properties from reservoir properties, and present the integrated iterative loop matching both kinds of dynamic data (production and 4D seismic), based on sensitivity analysis to select the most efficient parameters and applied to the synthetic PunqS3 case 6,1 .

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Section: Appendixmentioning

confidence: 99%

Integrated History-Matching of Production and 4D Seismic Data

Gosselin¹,

Berg²,

Cominelli³

2001

Proceedings of SPE Annual Technical Conference and Exhibition

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Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors

2021

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Fluid flow simulations are commonly used to predict the fluid displacement in subsurface reservoirs; however, model validation is challenging due to the lack of direct measurements. Geophysical data can be used to monitor the displacement of the fluid front. The updating of the fluid front location in two-phase flow problems based on time-lapse geophysical data can be formulated as an inverse problem, specifically a data assimilation problem, where the state is a vector of binary variables representing the fluid-facies and the observations are measurements of continuous geophysical properties, such as electrical or elastic properties. In geosciences, many data assimilation problems are solved using ensemble-based methods relying on the Kalman filter approach. However, for discrete variables, such approaches cannot be applied due to the Gaussian-linear assumption. An innovative approach for mixed discrete-continuous problems based on ensemble updating of binary state vectors is presented for fluid-facies prediction problems with time-lapse geophysical properties. The proposed inversion method is demonstrated in a synthetic two-dimensional simulation example where water is injected into a reservoir and hydrocarbon is produced. Resistivity values obtained from controlled-source electromagnetic data are assumed to be available at different times. According to the results, the proposed inversion method is to a large extent able to reproduce the true underlying binary field of fluid-facies.

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Geological Parameterization of a Reservoir Model for History Matching Incorporating Time-Lapse Seismic Based on a Case Study of the Statfjord Field

Ditzhuijzen

Oldenziel

Kruijsdijk

2001

SPE Annual Technical Conference and Exhibition

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The geological structure can have a tremendous impact on hydrocarbon flow and, especially in structurally complex reservoirs, is often one of the biggest uncertainties in reservoir modeling. One can think for example, of the sealing capacity of faults or of preferential fluid flow paths dictated by geological bodies. Using geological and structural parameters, these features can be incorporated when history matching a reservoir model. History matching with geologic parameters can enhance reservoir management by identifying non-swept regions and optimizing the placement of infill wells. In this paper we demonstrate an automated history matching method, using geological parameterization. Both time-lapse seismic and production data are used in an integrated manner. The method is tested on a synthetic reservoir, which resembles a structurally complex gravity slide system, common in North Sea reservoirs and other reservoirs situated in similar rift systems. The East Flank of the Statfjord Field is used as an example. The slump and associated faults are characterized by the following parameters: position, size and throw. Results show that the slumps / faults in a structurally complex reservoir can successfully be history matched. Evaluation of the conditioning data shows that the time-lapse seismic data gives more accurate information about the structure than production data. Introduction Oil and gas reservoirs need to be optimally managed, from an environmental, physical, political and economical point of view. To aid reservoir management, a model of the reservoir at hand is created to analyze its behavior and forecast future production. The better the model represents the actual reservoir, the more optimal [CvK1]the reservoir can be managed. During the lifetime of the field, a wealth of data becomes available, which is used to constrain the reservoir model. In the exploration phase, the reservoir model is constructed using 3D seismic data, geological knowledge of the surrounding area and data from a few exploration wells. This data is referred to as static data. Well data comprises log and core measurements. The seismic survey provides information on the reservoir geometry. During the appraisal phase more wells are drilled providing also dynamic data in the form of well test data. Once the field is in production, the wells provide production data. Moreover monitor (time-lapse) seismic surveys might be acquired. The production and time-lapse seismic data is referred to as dynamic data. The dynamic data is used to condition the reservoir model by a process referred to as history matching. By adjusting the model parameters e.g. permeability and other flow properties, the modeled behavior of the reservoir model is made to fit the historical (dynamic) data as observed at the wells and by (time-lapse) seismic measurements. The geological structure of the reservoir model is created early in the life of an oil field. It is sometimes manually adjusted, but more often kept fixed. In structural complex fields, the geometry of a reservoir is one of the biggest uncertainties. Incorrectly identifying the structural features, such as fault planes, might lead to badly placed wells, by-passed hydrocarbon and poor estimates of the Oil-In-Place (OIP). Hydrocarbons may be trapped in compartments that are surrounded by no-flow boundaries or their flow paths may be anomalous due to the presence of faults. In this paper we suggest a method to history match the reservoir model using geological parameters. To that effect the reservoir model is geologically parameterized. We test the automated method on a synthetic reservoir, which resembles a structurally complex gravity slide system, which is common in North Sea reservoirs and other reservoirs that are situated in similar rift systems. The East Flank of the Statfjord Field is used as an example. Production data and time-lapse seismic data of the synthetic reservoir are used to condition the history matching process.

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Saturation Mapping From 4-D Seismic Data in the Statfjord Field

Cited by 3 publications

References 0 publications

Integrated History-Matching of Production and 4D Seismic Data

Integrated History-Matching of Production and 4D Seismic Data

Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors

Geological Parameterization of a Reservoir Model for History Matching Incorporating Time-Lapse Seismic Based on a Case Study of the Statfjord Field

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