The oil bearing lenticular multilayered reservoir sands in the South Umm Gudair Field is embedded in the thick Ratawi Shale Member. They are sedimented as thin tidal bars anastomosed with distributary and tidal channels deposited under pro-deltaic to delta front environment.

The petrophysical properties estimated from well data with sequential Gaussian simulation could not resolve the problem of identification of thin reservoir sands because of the lack of spatial variability within wells. The seismic attributes such as coherency, spectral decomposition, first and second order amplitude, phase and frequency attempted individually within interpreted seismic horizons could not detect these thin sands. The gross picture of the channel/bar system was evolved with simulation of net to gross of reservoir sands identified in 35 drilled wells for Upper sequences (4a,4b,4c) and Lower sequences (2a,2b), within Ratawi Shale and Ratawi Limestone of Cretaceous Age. The neural network technique was applied to estimate Vclay and effective porosity integrating well information and 3D multi-attributes between seismically mapped horizons to delineate this complex reservoir sand channels/bars. This method is more efficient than conventional estimation with the ability to build a non-linear relationship between seismic traces and target porosity and Vcl logs for interpolation. The sum of amplitude slice derived between sequence Top and base tracked on depth converted estimated Vclay along inlines and crosslines provided the envisaged pattern of distributary channels.

This paper presents an integrated approach to delineate complex reservoir channels/bars to plan for side-tracking of existing wells and drilling appraisal & delineation wells.


The South Umm Gudair Field, located at north-western corner in the Partitioned Neutral Zone is (Figure 1) jointly operated by Saudi Arabian Texaco and Kuwait Gulf Oil Company.

Fig. 01
Figure 1. Location map of South Umm Gudair Field.

The oil accumulation in interbedded sands within the Ratawi Shale Member (Figure 2) has been confirmed in a few wells.

Fig. 02
Figure 2. Generalized Stratigraphic Column for PNZ.

The multiattribute transforms coupled with neural network technique were used to model high porosity reservoir sands and vclay integrating impedance derived with sparse spike technique from seismic data. Lithofacies, effective porosity, Vcl simulated using Sequential Gaussian with well data confirmed wide occurrences of reservoir sands in upper sequences as delineated with neural network technique.

Reservoir layering and correlation

The representative wells over the structure were correlated to identify different layers with sequential sedimentology approach.

Structural and Stratigraphic Framework

The Ratawi Shale Formation and associated sands are of deltaic origin. They are stacked as concentric belts of deltaic facies at the edge of a delta plain and delta front, with important variations in location of the distributary channels.

Reservoir units

The reservoir units, stratigraphic intervals bounded by two geological markers were classified as sequences: 2a, 2b, 3a, 3b, 4a, 4b, 4c etc. through analysis of channels and bars in key wells.

Porosity and Vshale computation

Density and Neutron logs were processed to determine effective porosity and Vclay for 40 wells (Figure 3).The Cross-plot of Hydrocarbon Pore Volume (HPV) vs Vclay for key wells (Figure 4) could discriminate reservoir sands (Phie > 10%, Vcl< 40%) and tight sands with range Phie < 10% , Vcl > 40%.

Fig. 03
Figure 3. Log interpretation, upscaling and sequence classification for W- 12.
Fig. 04
Figure 4. Hydrocarbon pore volume distribution versus Vclay to estimate cut off for reservoir sand.


Time migrated processed 155 sq. km 3D seismic data was interpreted to delineate these lenticular reservoir sands embedded in shale with multiattributes using a Neural Network technique in the following scheme (Table-I).

Table 01
Table 1. Work flow to derive effective porosity and Vcl volumes with multiattributes using neural network.

The geological markers of existing key wells were calibrated on seismic data to identify seismic horizons which were tracked for the entire area for Top Ratawi Shale and Top Ratawi Limestone. The stacking velocity was modelled constraining on seismic horizons tied on well markers to convert time domain seismic into depth ( Figure 5).

Fig. 05
Figure 5. Interpreted seismic inline from depth volume with Depth Structure map at the Top of Ratawi Shale.

Inversion process

An initial acoustic impedance model for seismic inversion was created with integration of picked horizons, extracted wavelets and correlated P-wave with seismic data processed to minimum phase. The impedance volume was derived with sparse spike inversion method.

Multi-attribute analysis

The Vcl logs within Ratawi Shale and Ratawi Limestone for 35 wells were integrated with extracted seismic traces and impedance curves on well locations. The combinations of 20 attributes were selected on the basis of error factors and correlations between actual and predicted Vcl (Figure 6).

Figure 6: Crossplot between actual vs predicted Vclay using 20 seismic attributes showing correlation coefficient

The decreasing error shows that the expected prediction error decreases with increasing number of attributes (Figure 7) and addition of more attributes is similar to fitting higher order polynomial to a set of points.

Fig. 07
Figure 7. Plot showing minimum average error (0.02) for 20 attributes and maximum validation error 0.144.

Neural Network Training

The neural network was trained with W-3, W-4, W-5, W-6, W-7, W- 8, W-9, W-10, W-11, W-12, W-13, W-14, W-15 and W-19, optimized iteratively through process of elimination with 20 attributes and 83% correlation coefficient was achieved. The neural network could not provide stable solutions for 6 and 10 attributes.

The process was run on the entire 3D seismic data using this trained Network and Vcl cube (Figure 8) was generated. Similar process was repeated with effective porosity (Figure 9) and the result was found to be in tune with each other as validated on drilled wells. The Vcl and porosity volumes in time was converted into depth to track the Top and base of Upper sequences 4a, 4b, 4c.

Fig. 08
Figure 8. Inline from Vcl volume along W-12 showing distributary channel for sequence 2a & 2b.
Fig. 09
Figure 9. Inline from effective porosity volume along W-12 showing localized nature of Sequence 2a & 2b.
Fig. 10
Figure 10. 3D view Vclay inline and crossline in depth illustrating widespread development of upper sequence.

The individual reservoir sands viz. 2a, 2b, 4a, 4b, 4c are below seismic resolution. This study has proved however that the lower reservoir sands consisting of sequences (2a & 2b) and upper sequence (4a, 4b & 4c) when coupled together are seismically detectable.

Facies modelling

Three lithofacies were selected based on log interpretation curves and cut-offs (Vcl < 0.4 & Φ > 0.1 ) and simulated using Sequential Gaussian. Vclay and effective porosity volumes derived using well data shows widespread development of Sequence-4a & 4b. The net to gross of reservoir sands along top of sequences 2a (Figure 11) and 4a, 4b , 4c (Figure 12) from the simulated volume provided the gross picture of channel/bar system.

Fig. 11
Figure 11. Distribution of net to gross reservoir sands for Sequence 2a showing anastomosed channel/bar.
Fig. 12
Figure 12. Distribution of net to gross reservoir sands using well data for the sequence 4b showing anastomosed distributary channel/bar.

Mapping sandbody System

The thickness slice from depth domain Vclay volume with reference to W-12 Sequence-2a to 100 ft below provided the localized nature of sandbody trend (Figure 13).

Fig. 13
Figure 13. Distribution of reservoir sands derived with neural network for the Sequence 2a.

The Top Upper sequence 4c and Base-4a were tracked in this Vclay integrating on drilled wells. The sum of stacked Vclay within Top and Base (Figure 14) provided the lineament of channel/bar system in upper sequence which are in conformity to well derived Net to gross for the upper sequence 4b. An arbitrary line drawn across channel-2 shows the predominance of upper sequence sands along this trend (Figure 15).

Fig. 14
Figure 14. Distribution of reservoir sands (Vcl<40%) derived with neural network for the upper sequence-4 showing distributary channel/tidal bar trend.
Fig. 15
Figure 15. Inline (SW-NE) from Vclay volume showing distribution of reservoir sands across channel-1 (Fig14).


  • The Vclay and effective porosity volumes estimated using neural network has been confirmed by drilled wells.
  • The upper sequence sands 4a & 4b are widespread, whereas lower sequence sands 2a & 2b are localized.
  • The reservoir sandbody system delineated on the basis of neural network technique provides the trend of deposition.
  • This integrated approach has identified the complex reservoir sand channel/bar system which may guide to plan horizontal well drilling, appraisal & sidetracking of existing wells in sequences 2a, 2b, 4a and 4b.



We thank Kuwait Oil Ministry and Kuwait Gulf Oil Company to provide permission for the publication of this paper. We would like to recognize management and support staff Field Development Department, Wafra for their significant direct or indirect contribution to this paper.

About the Author(s)

Ram Kumar Thakur is a Specialist Geophysicist , Kuwait Gulf Oil Company, Kuwait. He holds Master in Science, MBA degrees from India and Specialisation in Petroleum Geostatistics from Centre of Geostatistics, Ecole De Mines Des Paris, France. He has been working as a Geophysicist in ONGC, India for 3D seismic data interpretation/reservoir characterization from 1986 to 2004. He was elected as Secretary, Society of Petroleum Geophysicist, India during 2003-2005. He has presented several papers, two of them have received Best Paper Award in international conferences in SPG-2000 and Petrotech, 2003.

Frederic Paul-Henri Duval, PhD (Geology), 1978, France. Frederic has been working in Total Oil Company since 1978. He has carried out several sequence stratigraphic projects in a number of petroliferous basins of the world.



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