Orientación para usuarios intermedios para evaluar si trabajan dentro de los límites establecidos por el ES

Fig. 1.17 presents data from a shaly sand formation in Egypt. Track 1 contains MRIL permeability (green curve) and core permeability (red asterisks). Track 2 contains MRIL porosity (blue curve) and core porosity (black asterisks). In this reservoir, the highly variable grain sizes lead to a considerable variation in rock permeability. Capillary-pressure measure- ments on rock samples yielded a good correlation between the pore bodies and the pore throat structures. This correlation indicates that the NMR T₂ distribution is a good representa- tion of the pore throat size distribution when the pores are 100% water-saturated.

Fig. 1.18 shows an MRIL log through a massive low-porosity (approximately 10 p.u.), low- permeability (approximately 1 to 100 md) sandstone reservoir in Australia’s Cooper basin.23 Track 1 contains gamma ray and caliper logs. Track 2 contains deep- and shallow-reading resistivity logs. Track 3 presents the MRIL calculated permeability and core permeability. Track 4 shows the MRIL porosity response, neutron and density porosity readings (based on a sandstone matrix), and core porosity. This well was drilled with a potassium chloride (KCl) polymer mud [48-kppm sodium chloride (NaCl) equivalent] and an 8.5-in. bit. MRIL data were acquired with TW = 12 s and TE = 1.2 ms.

Over the interval depicted, the log shows a clean sandstone formation at the top, a shaly sandstone at the bottom, and an intervening shale between the two sandstones. Agreement between MPHI and the core porosity is good. The slight underestimation of MPHI relative to

Figure 1.15—Through the subtraction of echo trains obtained at two polarization times, light hydrocarbons can be identified. Track 5 displays the differential spectrum obtained from the subtraction of the two separate T2-distributions

derived from echo trains acquired with short and long polarization times, TWS = 1 s

and TWL = 8 s. The water

signals completely cancel while hydrocarbon signals only partially cancel and remain when the two T2

distributions are subtracted from one another. Track 6 displays the TDA results. Performed in time domain (as opposed to T2 domain),

TDA can quantify up to three phases (gas, light oil, and water; gas and water; or light oil and water). Mud filtrate that flushed the oil constitutes the movable water shown in Track 6.

18 Summary of NMR Logging Applications and Benefits Chapter 1 Figure 1.16—The combination

of conventional deep-resistivity data with NMR-derived MCBW, BVI, MFFI, and MPHI can greatly enhance petro- physical estimations of effective pore volume, water cut, and permeability. The MRIAN analysis results displayed in Track 5 show that the whole interval from X160 to X255 has a BVI almost identical to the water satura- tion interpreted from the resistivity log. This zone will likely produce water-free because of this high BVI.

Figure 1.17—These data from a shaly sand formation in Egypt show the good agreement between core data and MRIL porosity and permeability.

20 Summary of NMR Logging Applications and Benefits Chapter 1 Figure 1.18—This low-

porosity, low-permeability example from South Australia shows good agreement between core data and MRIL porosity and permeability.

Figure 1.19—In this gas reservoir, MRIL porosity is affected by the hydrogen index of the pore fluids. A corrected porosity, either from another source such as nuclear logs or from MPHI after HI correction, should be used for permeability calculation.

22 Summary of NMR Logging Applications and Benefits Chapter 1

core porosity is attributed to residual gas in the flushed zone. The MRIL permeability curve was computed using a model customized to this area. The agreement between MRIL perme- ability and core permeability is very good.

Fig. 1.19 compares core data with MRIL porosity and permeability recorded in a gas

reservoir.23 _{Track 1 contains gamma ray and caliper logs. Track 2 contains deep- and shallow-} reading resistivity logs. Track 3 presents the MRIL-derived permeability and core permeability. Track 4 presents the core porosity, MRIL porosity MPHI, neutron and density porosity (based on a sandstone matrix), BVI from a model customized to this reservoir, and a bulk volume water (CBVWE) from resistivity logs. The MRIL log in this example was acquired with a TW = 10 s,

TE = 1.2 ms, and NE = 500, where NE is the number of echoes per echo train.

A gas/water contact at X220 is easily identified on the resistivity logs. Immediately above the contact, a large gas crossover (yellow) is observed between the neutron and density logs. A decrease in MRIL porosity occurs here because of the hydrogen-index effect of the unflushed gas. Accurate data for BVI and MFFI are important for permeability calculations with the Coates model. The MPERM curve in Track 3 was calculated from the Coates model: MPHI was used for porosity, and the difference between MPHI and BVI was used for MFFI. Used in this way, the Coates model will give good estimates of permeability when the MRIL porosity is unaffected by gas. In zones where the MRIL porosity is affected by gas, MPERM is pessimistic because the difference between MPHI, and BVI underestimates MFFI. In this situation, the difference between BVI and the porosity obtained from the nuclear logs gives a better estimate of MFFI for calculating permeability The PMRI curve was computed in this manner. It is a more reasonable representation of permeability in the gas zones and in this example, matched very well with the core permeability. Below the gas/water contact, MRIL porosity and perme- ability match core data quite well.