4D Signal Enhancement Using Singular Value Decomposition – OWC Movement on the Nelson Field

Reid, Fiona; MacBeth, Colin; McInally, A

doi:10.3997/2214-4609-pdb.6.a11

Cited by 2 publications

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“…Map showing picked oil-water contact (OWC) movement between the 1990 (pre-production) and 2000 (monitor) surveys on Nelson, compared with the moved oil-water contact predicted from the reservoir simulation (after Reid et al 2003). 4D seismic has proved to be a valuable tool in monitoring the drainage patterns in Nelson, with predictions from the reservoir simulator being in good overall agreement with the seismic estimates.…”

Section: Figure 1 (A)mentioning

confidence: 99%

“…Geological and reservoir details of this field are given in Table 1. Figure 1(a) shows an example of the seismically estimated oil-water contact movement across the Nelson Field compared with the simulator prediction (after Reid, MacBeth and McInally 2003). Figure 1(b) illustrates, in vertical section, the characteristic trough-peak signature in the 4D seismic associated with this movement.…”

Section: Rocksmentioning

confidence: 99%

“… (a) Map showing picked oil–water contact (OWC) movement between the 1990 (pre‐production) and 2000 (monitor) surveys on Nelson, compared with the moved oil–water contact predicted from the reservoir simulation (after Reid et al . 2003).…”

Section: Introductionmentioning

confidence: 99%

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The 4D seismic signature of oil–water contact movement due to natural production in a stacked turbidite reservoir

MacBeth

Stephen

McInally

2005

Geophysical Prospecting

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A B S T R A C TCombined time-lapse reservoir simulation and seismic modelling has been performed on both 1D and 3D models of a channelized turbidite reservoir. The models have been built using core, log, laboratory and seismic data from the Nelson Field (central North Sea) as a template. Oil and water movement in the main channels, channel margins and interchannel regions is investigated, with a particular focus being the effect of poor net-to-gross. The analysis confirms that saturation effects dominate the response whilst stress-sensitivity effects play a minor role. The trough-peak signature in the seismic difference volumes formed by the sweep of the water can be continued and mapped slightly further than the channel margins. This characteristic 4D signature remains roughly intact, despite the complicated depositional architecture, and accurately delineates the area of moved fluid, but it needs additional calibration to combat the detrimental influence of the low net-to-gross. Signal strength is largely dependent on reservoir quality, but is also compromised by the net-to-gross, fluid distribution and, more critically, by the exact timing of the seismic survey. For example, a region of bypassed oil zone remains undetected as it forms early during the production. This work demonstrates that to understand fully the 4D signature at a quantitative level requires adequate knowledge of the fluid properties, but also, more critically, the geology.

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Section: Figure 1 (A)mentioning

confidence: 99%

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confidence: 99%

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The 4D seismic signature of oil–water contact movement due to natural production in a stacked turbidite reservoir

MacBeth

Stephen

McInally

2005

Geophysical Prospecting

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Multiple Model Seismic and Production History Matching: A Case Study

et al. 2005

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Time-lapse (or 4D) seismic is increasingly being used as a qualitative description of reservoir behaviour for management and decision making purposes. When combined quantitatively with geological and flow modelling, as part of history matching, improved predictions of reservoir production can be obtained. Here, we apply a method of multiple model history matching based on simultaneous comparison of spatial data offered by seismic, as well as individual well production data. Using a petro-elastic transform and suitable re-scaling, forward modelled simulations are converted into predictions of seismic impedance attributes and compared to observed data by calculation of a misfit. With a similar approach applied to dynamic well data, the total misfit is used to update model probabilities in a Bayesian framework. These are then used to guide the re-sampling of the parameter space, generating multiple models using the quasi-global stochastic Neighbourhood Algorithm (NA) method. This approach improves on gradient-based methods by avoiding entrapment in local minima and can be used to efficiently analyse uncertainty of the matched parameters. We demonstrate the method by applying it to a UKCS turbidite reservoir, updating the operator's initial model, which had previously been history matched to dynamic well data by conventional methods. We consider a number of parameters to be uncertain. The reservoir's net to gross is initially updated to better match the RMS amplitudes of the observed seismic baseline survey. We match simultaneously for permeability, fault transmissibility multipliers and the petro-elastic transform parameters. Our results show a good match to the observed seismic and dynamic well data with significant improvement to the operator's base case. Introduction Reservoir management requires tools such as simulation models to predict asset behaviour. History matching is often employed to alter these models so that they compare favourably to observed well rates and pressures. This well information is obtained at discrete locations, and thus lacks the areal coverage necessary to accurately constrain dynamic reservoir parameters such as permeability and the location and effect of faults, among others. Time-lapse seismic captures the effect of pressure and saturation on seismic impedance attributes giving two-dimensional maps or three-dimensional volumes of the missing information. The process of seismic history matching attempts to overlap the benefits of both types of information to improve estimates of the reservoir model parameters. We present an automated history matching method that includes time-lapse seismic along with production data, based on an integrated workflow (Figure 1). It improves on the classical approach, where the engineer manually adjusts parameters in the simulation model. Our method also improves on gradient-based methods, such as Steepest Descent, Gauss-Newton and Levenberg-Marquardt algorithms (e.g. Lépine, et al., 1999, Dong and Oliver, 2003, Gosselin et al., 2003, Mezghani, et al, 2004), which are good at finding local likelihood maxima but can fail to find the global maximum. Our method is also faster than stochastic methods such as genetic algorithms and simulated annealing, which often require more simulations and may have slower convergence rates. Finally, multiple models are generated enabling uncertainty analysis in a Bayesian framework. The posterior probability surface is resampled to obtain parameter distributions. We have applied our method to a UKCS turbidite reservoir, in which time-lapse seismic has shown great promise (Chapin, 2000; Parr et al., 2000; Saxby, 2001). The original geological model was constructed by the field operator using typical approaches where facies objects and static flow properties such as porosity and permeability were distributed stochastically. The model was constrained to well logs and a qualitative match to the baseline seismic survey was obtained. The operator upscaled and then manually history matched the model to the well production data before we applied our method.

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4D Signal Enhancement Using Singular Value Decomposition – OWC Movement on the Nelson Field

Cited by 2 publications

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The 4D seismic signature of oil–water contact movement due to natural production in a stacked turbidite reservoir

The 4D seismic signature of oil–water contact movement due to natural production in a stacked turbidite reservoir

Multiple Model Seismic and Production History Matching: A Case Study

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