North Sea reservoir characterization using rock physics, seismic attributes, and neural networks; a case history

Walls, Joel; Taner, M. Turhan; Guidish, T.; Taylor, Gareth; Dumas, David B.; Derzhi, Naum

doi:10.1190/1.1820825

Cited by 21 publications

(6 citation statements)

References 2 publications

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“…Additional discussion on attribute categories and their relationships to reservoir properties are found in much geophysical literature such as Brown (2001), Chen and Sidney (1997); Taner et al (1979), and Walls et al (1999).…”

Section: Rms Amplitudementioning

confidence: 99%

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Anifowose

Adeniye

Abdulraheem

et al. 2016

Journal of Petroleum Science and Engineering

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Section: Rms Amplitudementioning

confidence: 99%

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Anifowose

Adeniye

Abdulraheem

et al. 2016

Journal of Petroleum Science and Engineering

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“…This allows for the computation of the probability of occurrence of each facies group using self-organizing maps (Castro de Matos et al, 2007), neutral network technique (Haykins, 1999, Walls et al, 1999 or the deterministic probability density functions (pdf). An example of the clustering of seismic facies in IP-PR plane is shown below in Figure 2.…”

Section: Seismic Facies In Geo-modelingmentioning

confidence: 99%

Accounting for Deep-Water Reservoir Heterogeneities Through 3D/4D Seismic-Guided Reservoir Model Petrophysical In-filling

Amoyedo¹,

Buraimoh²,

Atoyebi³

et al. 2015

Day 3 Wed, September 30, 2015

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Adequate representation of reservoir heterogeneities in deep-water turbiditic systems presents a significant challenge in reservoir model building. Reservoir models are often too homogeneous, thus, making history matching not only difficult but sometimes less predictive and unreliable for long term production planning. In this paper we present a methodology to adequately capture and preserve variability in reservoir quality seen on well logs and seismic-derived reservoir properties inferred from 3D and 4D seismic. We demonstrate this technique in a deep-water field, offshore Niger delta.In the first segment of the paper, we show the use of time-lapse seismic (4D) to update seismic-derived facies proportion cubes. Such update ensures consistency between the predicted facies proportions and 4D results. We further present a workflow to combine well results and the updated seismic facies probabilities to produce a geologic facies proportion cube that preserves heterogeneities and respects all available data (well results, 3D/4D seismic). The methodology involves the use of weighting factors on 3D-propagated well data (reference cube) and the 4D-updated seismic facies proportion cubes (auxiliary cubes). We conclude the study with the use of seismic loop-back as a quality control tool to check the overall consistency of the static model petrophysical infilling with seismic-derived reservoir properties.We observed that most of the 4D-updated grid cells in the reservoir model are within the channel levees and lobe fringes, where seismic characterization might be less reliable but sometimes contributes significantly to oil in place (and reserve) in the field. The result highlights a useful application of 4D seismic for facies characterization in addition to reservoir monitoring. Also, the integrated facies proportion cube (well facies & seismic facies) not only retains known heterogeneities, it is consistent with 4D seismic. In conclusion, the seismic loop-back procedure, which involves the use of a calibrated petro-elastic model to generate reservoir elastic properties from model properties, shows that in most reservoir units in the field an overall good match was achieved between the reservoir model infilling and well data/seismic-inferred properties. In the field example, we observe that while a good match exists between the model and inverted parameters, nonetheless, important discrepancies have been found in areas lying mostly in wet channel levees. Careful analysis of such areas provides some insights about ways of improving the initial reservoir model infilling.The paper presents a unique technique to account for heterogeneities (in 3D sense) in reservoir models. It showcases a pathway to integrate and maintain consistency among well data, 3D and 4D seismic towards building a consistent, reliable and robust reservoir model.

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“…Seismic attribute volumes, together with appropriate classification schemes, help in the interpretation and identification of lateral changes in reservoir properties. These can further be calibrated with well-logs (Dumay and Fournier, 1988;Schultz et al, 1994;Fournier and Derain, 1995;Walls et al, 1999;Matos et al, 2007). Due to inconsistency and sparseness of seismic data, a priori knowledge of available clusters or classes is generally not available; therefore, unsupervised classification algorithms are attractive for these applications.…”

Section: Introductionmentioning

confidence: 98%

Artificial immune-based self-organizing maps for seismic-facies analysis

Saraswat

Sen

2012

GEOPHYSICS

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Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.

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North Sea reservoir characterization using rock physics, seismic attributes, and neural networks; a case history

Cited by 21 publications

References 2 publications

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Accounting for Deep-Water Reservoir Heterogeneities Through 3D/4D Seismic-Guided Reservoir Model Petrophysical In-filling

Artificial immune-based self-organizing maps for seismic-facies analysis

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