Field appraisal and accurate resource estimation from 3D quantitative interpretation of seismic and CSEM data

Morten, Jan Petter; Roth, F.; Karlsen, Stig Arne; Timko, David; Pacurar, Constantin; Olsen, Per Atle; Nguyen, Anh Kiet; Gjengedal, Jakob Andreas

doi:10.1190/tle31040447.1

Cited by 25 publications

(4 citation statements)

References 7 publications

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“…The underestimation factor is approximately 1.3 when the correct resistivity background is used. Similar underestimation factors have been observed earlier in 3-D CSEM studies, for example 1.28 for the Skrugard oil reservoir (Gabrielsen et al 2013) and 1.1 for the Troll oil field (Morten et al 2012). The good news is that this underestimation should not cause any problems when inverting field data.…”

Section: Discussionsupporting

confidence: 83%

Time-lapse CSEM: how important is survey repeatability?

Shantsev

Nerland

Gelius

2020

Geophysical Journal International

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Summary An important concern for time-lapse studies using the controlled-source electromagnetic (CSEM) method is repeatability of acquisition parameters for the base and monitoring surveys. We consider a challenging case when variations in source and receiver positions, conductivity of seawater etc. will lead to differences in the recorded EM fields that exceed EM response due to production-induced changes in the reservoir resistivity. We show that even in that case, 4D response can often be clearly resolved if the base and monitor EM datasets are inverted to produce 3D resistivity distributions. More precisely, for a synthetic model of the Snøhvit gas field, changes in the inverted resistivity maps caused by such non-repeatability will be at least 10 times smaller than the time-lapse differences in the reservoir resistivity. By contrast, measurement errors and poor knowledge of background resistivity may reduce the resolution of 4D CSEM to a much stronger degree. Analysis of field CSEM data from the Wisting oil field supports our conclusion about strongly relaxed repeatability requirements when 4D effects are established by examining inverted resistivity volumes rather than by looking at raw EM data.

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Section: Discussionsupporting

confidence: 83%

Time-lapse CSEM: how important is survey repeatability?

Shantsev

Nerland

Gelius

2020

Geophysical Journal International

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“…An integrated approach provides a sharper resistivity contrast between the background and the anomaly. This allows for an improved interpretation of the anomalous areal extent and consequently reduces the uncertainty in the reserve estimates (Morten et al, 2012). However, it is recommended to always perform an unconstrained study to understand the information content of the CSEM data and get a subsurface image independent of prior interpretations.…”

Section: Csem Acquisition and Imagingmentioning

confidence: 99%

Exploring channel reservoirs in a frontier area with regional CSEM as fluid indicator

Pedersen¹,

Hiner²

2014

SEG Technical Program Expanded Abstracts 2014

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In this paper we explore the West Africa-South America analogue proven by the Jubilee and Zaedyus discoveries in Ghana and French Guiana, respectively. Instead of the Turonian turbidite fan reservoirs, we consider younger unproven deep water channel systems in Foz do Amazonas Basin in Brazil which could be analogous to the Campanian pay found in the Teak field in Ghana. We present a geological overview of the basin together with acquisition details and imaging results of a regional 3D CSEM survey in the area. Using CSEM as a fluid indicator, we calculate the Net Rock Volume (NRV) from a channel-shaped EM anomaly and with conservative parameterization we obtain a P10/P90 ratio of 14.7 and an average NRV of ~23000hm 3 .

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“…Most of the current deep learning interpolation methods are based on the training of large datasets or specialized training datasets, which is contrary to the reality of interpolation tasks. In real scenarios, the acquisition of velocity field data will consume a great amount of manpower and financial resources and is easily hindered by natural conditions, making the data very sparse [29]. Secondly, the network finds it difficult to transfer in the face of different underground features in various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism

Yao,

Cui,

Wang

et al. 2023

Applied Sciences

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The establishment of a three-dimensional velocity field is an essential step in seismic exploration, playing a crucial role in understanding complex underground geological structures. Accurate 3D velocity fields are significant for seismic imaging, observation system design, precise positioning of underground geological targets, structural interpretation, and reservoir prediction. Therefore, obtaining an accurate 3D velocity field is a focus and challenge in this field of study. To achieve intelligent interpolation of the 3D velocity field more accurately, we have built a network model based on the attention mechanism, JointA 3DUnet. Based on the traditional U-Net, we have added triple attention blocks and channel attention blocks to enhance dimension information interaction, while adapting to the different changes of geoscience data in horizontal and vertical directions. Moreover, the network also incorporates dilated convolution to enlarge the receptive field. During the training process, we introduced transfer learning to further enhance the network’s performance for interpolation tasks. At the same time, our method is a deep learning interpolation algorithm based on an unsupervised model. It does not require a training set and learns information solely from the input data, automatically interpolating the missing velocity data at the missing positions. We tested our method on both synthetic and real data. The results show that, compared with traditional intelligent interpolation methods, our approach can effectively interpolate the three-dimensional velocity field. The SNR increased to 36.22 dB, and the pointwise relative error decreased to 0.89%.

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Field appraisal and accurate resource estimation from 3D quantitative interpretation of seismic and CSEM data

Cited by 25 publications

References 7 publications

Time-lapse CSEM: how important is survey repeatability?

Time-lapse CSEM: how important is survey repeatability?

Exploring channel reservoirs in a frontier area with regional CSEM as fluid indicator

Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism

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