History Matching a Reservoir Model to Time-Lapse Seismic Using the Pilot Point Method

Arenas, Eliana; Oldenziel, T.; Kruijsdijk, C.P.J.W. van

doi:10.3997/2214-4609-pdb.15.f-03

Cited by 6 publications

(1 citation statement)

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“…Limited seismic timelapse monitoring can take advantage of abundant well production history data to interpret effectively the course of reservoir production. Many authors have worked on the integration of seismic time-lapse data in the simulation of conventional reservoirs (Huang, Meister and Workman 1997;Arenas, Oldenziel and Van kruijsdijk 2001;Gosselin et al 2003;Mezghani, Langlais and Lucet 2004;Caers, Strebelle and Wen 2006;Zou, Bentley and Lines 2006;Kazemi and Stephen 2008). This integration is ultimately intended to alleviate the non-uniqueness problem in the history matching of fluid-flow simulation.…”

Section: Introductionmentioning

confidence: 99%

Effect of oil composition on fluid substitution in heavy oil reservoirs

Shamsa

Lines²

2014

Geophysical Prospecting

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Unlike light oils, heavy oils do not have a well‐established scheme for modelling elastic moduli from dynamic reservoir properties. One of the main challenges in the fluid substitution of heavy oils is their viscoelastic nature, which is controlled by temperature, pressure, and fluid composition. Here, we develop a framework for fluid substitution modelling that is reliable yet practical for a wide range of cold and thermal recovery scenarios in producing heavy oils and that takes into account the reservoir fluid composition, grounded on the effective‐medium theories for estimating elastic moduli of an oil–rock system. We investigate the effect of fluid composition variations on oil–rock elastic moduli with temperature changes. The fluid compositional behaviour is determined by flash calculations. Elastic moduli are then determined using the double‐porosity coherent potential approximation method and the calculated viscosity based on the fluid composition. An increase in temperature imposes two opposing mechanisms on the viscosity behaviour of a heavy‐oil sample: gas liberation, which tends to increase the viscosity, and melting, which decreases the viscosity. We demonstrate that melting dominates gas liberation, and as a result, the viscosity and, consequently, the shear modulus of the heavy oils always decrease with increasing temperature. Furthermore, it turns out that one can disregard the effects of gas in the solution when modelling the elastic moduli of heavy oils. Here, we compare oil–rock elastic moduli when the rock is saturated with fluids that have different viscosity levels. The objective is to characterize a unique relation between the temperature, the frequency, and the elastic moduli of an oil–rock system. We have proposed an approach that takes advantage of this relation to find the temperature and, consequently, the viscosity in different regions of the reservoir.

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Section: Introductionmentioning

confidence: 99%

Effect of oil composition on fluid substitution in heavy oil reservoirs

Shamsa

Lines²

2014

Geophysical Prospecting

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Seismic History Matching of Nelson using Time-lapse Seismic Data: An Investigation of 4D Signature Normalization

Kazemi¹,

Stephen²,

Shams³

2010

SPE EUROPEC/EAGE Annual Conference and Exhibition

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History matching of a reservoir model is always a difficult task. In some fields we can use time-lapse (4D) seismic data to detect production induced changes as a complement to more conventional production data. In seismic history matching we predict these data and compare to observations. Observed time-lapse data often consists of relative measures of change which requires normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching.We apply the approach to the Nelson field in which four surveys are available over nine years of production. We normalize the 4D signature in a number of ways. Firstly we use predictions of 4D signature from vertical wells that match production and derive a normalization function. As an alternative, we use cross-plots of the full field prediction against observation. Normalized observations are used in an automatic history matching process where the model is updated. We analyse the results of the two normalization approaches and compare against the case of just using production data.The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8% and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods respectively. When we use only production data the production history match is improved to a similar degree (45%) but in predictions, the improvement is only 25% and the 4D seismic misfit is ten percent worse.Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching we can better predict reservoir behaviour and identify regions of remaining oil.

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Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

Kazemi¹,

Stephen²,

Shams³

2011

SPE Reservoir Evaluation &Amp; Engineering

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History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching.We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data.The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse.Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil., .

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History Matching a Reservoir Model to Time-Lapse Seismic Using the Pilot Point Method

Cited by 6 publications

References 0 publications

Effect of oil composition on fluid substitution in heavy oil reservoirs

Effect of oil composition on fluid substitution in heavy oil reservoirs

Seismic History Matching of Nelson using Time-lapse Seismic Data: An Investigation of 4D Signature Normalization

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

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