Accurate Porosity Measurement in Gas Bearing Formations
- Olivier Desport (Schlumberger Middle East SA) | Kim Copping (Husky Energy) | Barry L. Johnson (Alberta Ltd.)
- Document ID
- Society of Petroleum Engineers
- Canadian Unconventional Resources Conference, 15-17 November, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 1.11 Drilling Fluids and Materials, 1.6 Drilling Operations, 2.4.3 Sand/Solids Control, 1.6.9 Coring, Fishing, 5.2 Reservoir Fluid Dynamics, 5.6.1 Open hole/cased hole log analysis, 5.2.2 Fluid Modeling, Equations of State, 5.1.5 Geologic Modeling
- 0 in the last 30 days
- 519 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Porosity is a key reservoir parameter and high accuracy is needed to properly estimate reserves. But even though there is a long history of porosity measurements and various tools from which to derive it, this can still remain a difficult task. None of the logging tools directly measure porosity but instead respond to density, lithology and fluid. Combining different measurements can help to solve for porosity but also brings the complexity of invasion as all the tools do not have the same radial response. This problem is even more complex when dealing with gas formations as the fluid effect on the measurements is very high.
This paper looks at various methods to improve porosity computations via the integration of Nuclear Magnetic Resonance (NMR) and other porosity measurements in South China Sea gas reservoirs.
As illustrated in Fig. 1 the gas reservoirs in the South China Sea are composed of sand shale sequences with good porosity. Wells are drilled with oil base mud, invasion is shallow and gas effect is large on the neutron and density measurements. Fig. 1 shows a strong neutron-density gas effect and highlights the importance of fluid corrections to obtain reservoir porosity.
As gas effect is opposite on the neutron and density measurements, the usual workflow is to compute an apparent porosity from density assuming a water filled rock and then apply a weighted average of this apparent density porosity with an apparent neutron porosity.
Fig. 2 shows the result of such an approach and the comparison to core. One can observe that in some zones the match is good and not as good in others. One possible reason is a variation in clay content, clay contains neutron absorbers which can have a large impact on the neutron. This effect seems unlikely as these sand reservoirs are clean with minimal amounts of clay. Another possible explanation is that computing a weighted average assumes that the gas effect is similar on neutron and density, but as these tools see different volumes of rock, this assumption is incorrect when invasion is variable.
This paper illustrates the limitation of using neutron-density measurements to compute porosity and the necessity to include other measurements to reduce uncertainty.
In order to accurately compute porosity and adequately correct for invasion, one needs to integrate measurements that respond to similar volumes of rock. Fig. 3 shows the geometrical responses of density, neutron and two NMR tools. The Combinable Magnetic Resonance (CMR) tool has a single depth of investigation (DOI) while the Magnetic Resonance Scanner (MR Scanner) has three depths of investigation. One can observe that 80% of the density information comes from the first four inches away from the borehole while this same volume represents only 15% of the neutron response. This explains why shallow invasion effects cannot be properly compensated when combining neutron and density measurements, any small variations in the invasion will have different effects on the density and neutron.
NMR is another measurement which can be combined with density to compute porosity. Similar to neutron, NMR reads low in the presence of gas. This is a consequence of gas' low hydrogen index (HI) and of its hydrogen polarization deficit due to the long longitudinal relaxation time (T1) and the limited acquisition wait time.
A quantitative workflow called Density Magnetic Resonance Porosity (DMRP) combining density and NMR measurements was developed in 1998 (Freedman et al, 1998). NMR is acquired with short wait time to boost the gas effect (apparent porosity deficit) and total porosity is computed using a weighted average of density derived and apparent NMR porosity. The weights are computed from gas properties and NMR acquisition parameters.
|File Size||2 MB||Number of Pages||13|