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ADVANCED PETROPHYSICAL ANALYSIS AND WATER SATURATION PREDICT
The Three Forks Formation, as the lower part of the Bakken petroleum system, is a complex reservoir with variable mineralogy, thin-bed characteristics, and low permeability. Advanced logging tools and techniques are required to characterize and estimate water saturation (Sw), porosity, and mineralogy in this type of formations.
In this paper, to overcome these challenges, we used three different methods to estimate Sw. The first method was based on an integrated petrophysical workflow proposed to evaluate the reservoir quality. A complex petrophysical model was developed for Three Forks Formation by the integration of the advanced logging to the workflow including pulsed neutron spectroscopy for the mineralogy and grain density, nuclear magnetic resonance for the porosity, clay bound water, and free fluid, and multifrequency array dielectric measurements for Sw. Both deterministic and probabilistic methods were used to assess the output component and fluid volumes. The integration of the elemental dry weights fraction with conventional logs allows more accurate mineralogical determination and calculation.
The complex petrophysical model results provided the basis to extrapolate the model to the wells that are remote from any advanced logging and core analysis. The challenge was to rescale the input components to the minimum components to be solved and set the appropriate matrix parameters, uncertainties and weight multipliers for each equation. Also, additional constraints were necessary to supply the model with more information.
In the second approach, Sw was estimated from dielectric measurements, which is independent of resistivity. The two models showed good agreements with core measurement results. This confirms the Archie parameters and the formation water resistivity used as an input into the Modified Simandoux equation.
In a third attempt, the application of machine learning and deep learning algorithms were applied to estimate Sw using only conventional logs. This was with the aim of generalizing the results to the entire extent of the Three Forks reservoir in the Williston Basin. The performance of support vector regression was compared to that of backpropagation neural network model based on the correlation coefficient, root mean square error, and maximum absolute error indexes. The results suggest the use of the two algorithms complementary to each other for Sw estimation. These methods captured the complexity of the Three Forks formation where the laminations are in abundance with a complex pore size distribution.
On the other hand, the NMR T2 Log Mean was applied to investigate the pore size distribution and its relation with Sw. The average T2 Log Mean values of equal or greater than 8 msec was defined as cutoff corresponding to oil-bearing intervals in the Three Forks formation.
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Author(s):
Aldjia Boualam and Vamegh Rasouli, Chantsalmaa Dalkhaa and Sofiane Djezzar
Company(s):
University of North Dakota; Energy and Environmental Research Center
Year:
2020
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