The Startup of Permanent Reservoir Monitoring for Snorre and Grane

Thompson, Mark; Andersen, Mona; Elde, R.M.; Roy, Shibendu Shekhar; Skogland, S.M.

doi:10.3997/2214-4609.201412544

Cited by 10 publications

(3 citation statements)

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“…Grane is an offshore oil field in the Norwegian North Sea. A permanent reservoir monitoring (PRM) system was installed in 2014 over approximately 50 km 2 of the Grane seabed (Thompson et al, 2015). Ambient seismic noise is recorded continuously at the field using four-component sensors -3-component geophones (Vertical, North and East) and a hydrophone.…”

Section: Grane Datamentioning

confidence: 99%

Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

Earp

Curtis

Zhang

et al. 2020

Geophysical Journal International

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Summary Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear-wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear-wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of neural networks called mixture density networks, to invert dispersion curves for shear-wave velocity models and their non-linearised uncertainty. Mixture density networks are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties in the mixture density network gives more reliable mean velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear-wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26,772 locations to produce a 3D velocity model in 21 seconds on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3D subsurface imaging and monitoring.

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Section: Grane Datamentioning

confidence: 99%

Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

Earp

Curtis

Zhang

et al. 2020

Geophysical Journal International

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“…The event was recorded on a Permanent Reservoir Monitoring (PRM) system deployed on the seafloor in the North Sea. The PRM system comprises of 3458 sensors, 3C geophones with a hydrophone, arranged in a gridded-style as shown in Figure 2 of [15]. This event is referred to as the "G8 event"and has been previously analysed using a small subset of receivers by [16].…”

Section: Datamentioning

confidence: 99%

Bidirectional recurrent neural networks for seismic event detection

Birnie¹,

Hansteen²

2020

Preprint

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Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters. Whilst numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be globally applied, or they are too computationally expensive therefore cannot be run real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bi-directional, long-short-term memory, neural network is trained solely on synthetic traces. Evaluated on synthetic and field data, the neural network approach significantly outperforms the STA/LTA trigger both on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its real time applicability is proven with 600 traces processed in real time on a single processing unit.Recently, Machine Learning (ML) methodologies, in particular Deep Learning (DL), has seen a resurgence across all fields of seismology, and wider geoscience applications. The majority of applications have focused on the adaptation of computer vision approaches for aiding seismic interpretation. For example, salt body detection [8], fault detection [9], and horizon detection [10]. However, other studies have considered how ML methodologies can be utilised for seismic event detection. [11] proposed the combination of convolutional Neural Networks (NN) with k-means clustering for detection of seismic arrivals, whilst [12] presents one of the earliest studies that considered using NNs for event detection on broadband seismometers. Novelly, they combined three different back-propagation NNs that

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“…The field is composed of one main reservoir and a few other segments at a depth of 1,700 m. A permanent monitoring system has been deployed in the field, which contains 3458 four-component sensors (Z-vertical, N-north, E-east component and H-hydrophone). This records seismic data from the field continuously (Thompson et al, 2015) and thus provides the possibility to use ambient noise tomography to monitor the reservoir.…”

Section: Introductionmentioning

confidence: 99%

1‐D, 2‐D, and 3‐D Monte Carlo Ambient Noise Tomography Using a Dense Passive Seismic Array Installed on the North Sea Seabed

et al. 2020

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In a variety of geoscientific applications we require 3‐D maps of properties of the Earth's interior and the corresponding map of uncertainties to assess their reliability. On the seabed it is common to use Scholte wave dispersion data to infer these maps using inversion‐based imaging theory. Previously we introduced a 3‐D fully nonlinear Monte Carlo tomography method that inverts for shear velocities directly from frequency‐dependent travel time measurements and which improves accuracy of the results and better estimates uncertainties. Here for the first time we apply that method to real data and compare it to two previous methods. We cross correlated 6.5 hr of ambient noise data recorded on a dense seismic array over Grane, North Sea, and observed two Scholte wave modes. For each mode, phase velocity maps are estimated using Eikonal tomography, which are in turn used to study the shear‐wave velocity structure of the subsurface. We applied three nonlinear inversion methods to the Grane data: standard 1‐D depth inversions, a 2‐D joint inversion along a vertical cross section, and a fully 3‐D inversion. We compare the shear‐velocity and uncertainty structures estimated along the same 2‐D cross section. Thus we show that the standard 1‐D inversion method creates significant errors in the results due to the independence of those 1‐D inversions, whereas the 2‐D and 3‐D inversions improve results by accounting for lateral spatial correlations. The 3‐D inversion bypasses the initial seabed Eikonal tomography step, thus avoiding the errors that the initial step introduces into subsequent 1‐D and 2‐D inversions.

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The Startup of Permanent Reservoir Monitoring for Snorre and Grane

Cited by 10 publications

References 0 publications

Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

Bidirectional recurrent neural networks for seismic event detection

1‐D, 2‐D, and 3‐D Monte Carlo Ambient Noise Tomography Using a Dense Passive Seismic Array Installed on the North Sea Seabed

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