Seismic reservoir characterization of a U.S. Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks

Walls, Joel; Taner, M. Turhan; Taylor, Gareth; Smith, Maggie; Carr, Matthew; Derzhi, Naum; Drummond, Jock; McGuire, Donn; Morris, Stan; Bregar, John; Lakings, James

doi:10.1190/1.1481248

Cited by 29 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8, 9), which is used as an effective indicator to show the acoustic impedance contrast. Consequently, the reflection strength attribute is useful for recognising the stratigraphic variations within the fluvial channels (Walls et al 2002). As a result of integrating the reflection configurations, the relative acoustic impedance attribute and the nearby well data, it can be concluded that the mapped channels are mainly filled with shaly beds of relatively low impedance (dark colour), whereas the sandy beds have high impedance (light colour) and are surrounded by shale beds that were deposited on the channel banks, point bars and crevasse splays, as shown in Figs.…”

Section: Resultsmentioning

confidence: 99%

Delineating the fluvial channel system in the Upper Triassic formation of the Elward area in the Syrian Euphrates Graben using 3-D seismic attributes

Alsouki

Taifour

Hamad

2014

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

3-D seismic data were used to evaluate the reservoir in the Mulussa F formation in the Elward area and to discover new drillable locations by delineating the fluvial channel system and the features of its facies. Seismic attributes such as relative acoustic impedance, coherency volume and reflection strength were considered. The attribute maps revealed different types of channels such as braided, meandering and anastomosing channels. They also revealed fluvial features such as point bars, Ox-Bows and crevasse splays, which are considered potential points of hydrocarbon accumulation due to the abundance of sandy bodies. The map of relative acoustic impedance demonstrated that the sandstone beds have high impedance, whereas the shale beds have low impedance. In fact, well data indicated that the shaly beds were surrounded by sandy beds, and their thickness was in the range of 20-25 m within the Mulussa F formation. The coherency attribute showed some of the meandering tributary channels that were not seen on the relative acoustic impedance map. The abundance of fluvial channels in the Euphrates region indicates a large water discharge during the deposition of the Mulussa F formation because of the wet climate prevailing during the Upper Triassic.

show abstract

Section: Resultsmentioning

confidence: 99%

Delineating the fluvial channel system in the Upper Triassic formation of the Elward area in the Syrian Euphrates Graben using 3-D seismic attributes

Alsouki

Taifour

Hamad

2014

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…The awareness of using many seismic attributes to predict borehole log properties was foremost projected by Schultz et al (1994); Robinson (2001). Numerous case histories have been cited in the previous works for such prediction of borehole logs properties using multilinear stepwise regression and artificial neural networks (Russell et al 1997;Skolen et al 2006;Fogg 2000;Tonn 2002;Walls 2002;Pramanik et al 2004). In this method, the training points are considered to be well to seismic ties.…”

Section: Preamblementioning

confidence: 99%

3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of “P” field, Niger Delta

Fajana

2020

NRIAG Journal of Astronomy and Geophysics

View full text Add to dashboard Cite

show abstract

“…Over the recent years, several types of algorithms have been developed for mapping AI from post-stack seismic amplitude data and further linking it to reservoir properties distribution in space [5]. Nowadays, an increase in computing power and modern technologies of acquisition, processing, and interpretation of seismic data has empowered the reservoir geophysicists to focus on machine learning, i.e., extracting AI using neural network algorithms [6][7][8][9]. The advantages of artificial neural network algorithms over traditional statistical inversions are briefly discussed in the literature [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

et al. 2020

View full text Add to dashboard Cite

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.

show abstract

Seismic reservoir characterization of a U.S. Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks

Cited by 29 publications

References 0 publications

Delineating the fluvial channel system in the Upper Triassic formation of the Elward area in the Syrian Euphrates Graben using 3-D seismic attributes

Delineating the fluvial channel system in the Upper Triassic formation of the Elward area in the Syrian Euphrates Graben using 3-D seismic attributes

3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of “P” field, Niger Delta

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

Contact Info

Product

Resources

About