Elemental Compositional Zoning Using Reservoir Formation Water Samples for Oilfield Applications
- I. A. Jimoh (Schlumberger) | E. G. Søgaard (Aalborg University) | J. Muff (Aalborg University) | M. Yahaya Kano (Aalborg University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 9-11 October, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 4.3.4 Scale, 5.2 Reservoir Fluid Dynamics, 5.2 Reservoir Fluid Dynamics
- Elemental composition, Reservoir, Facies, Formation water, Scale formation
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Formation water samples from 170 wells in Viola Limestone Formation in Kansas were analyzed based on multi elemental relationships on a field scale along 4733 ft. depth interval. Results indicated that physico-chemical parameters such as pH, total dissolved solid (TDS), specific gravity and resistivity showed a varying functional behavior with depth. In addition, the Na+, Ca2+, Mg2+, Cl–, SO42− and HCO3– ions predominate in the formation water samples from different wells. The relationships between the chemical compositions of formation waters showed that the concentrations of most dissolved constituents correlated positively with chloride ions with the exception of SO42− HCO3– and pH that correlated negatively. The model output of the self-organized map classification of Ca2+, TDS, HCO3–, SO42−, temperature and pH resulted in five classification groups from the formation water samples using a linear transformation. The relative frequency of the groups in the model were 18.53% for zone 1, 24.71 % for zone 2, 31.27 % for zone 3, 17.37 % for zone 4 and 8.11% for zone 5. The elemental correlation to the model of the principal components was 0.85 for pH, 0.80 for Ca2+, 0.75 for Temp, 0.74 for TDS, 0.67 for SO42− and 0.53 for HCO3– respectively. Based on these results, we were able to predict carbonate scale tendency of each group using Langelier method of saturation index and to interpolate the scale formation tendency of each classification group with depth.
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