Improved Permeability Measurement using T2 Bin-Distribution and Bulk

Improved Permeability Measurement using T2 Bin-Distribution and Bulk

Improved Permeability Measurement using T2 Bin-Distribution and Bulk Volume Irreducible from Nuclear Magnetic Resonance Tools Case Study: Granite Wash, Hemphill and Wheeler Counties, TX. Ken Huggins Halliburton Reservoir Evaluation Services Oklahoma City Standard porosity-based permeability measurements often do not indicate the best zones to perforate or reflect the ultimate hydrocarbon production potential. Permeability measurements using Nuclear Magnetic Resonance (NMR) bin-distribution and bulk volume irreducible (BVI) data are compared to standard porosity-based permeability measurements as indicators

of hydrocarbon production. Predicting permeability from porosity Cubic Packing r k r = = =

47.6 % 5000 md 2.0 Porosity is controlled by: Packing Grain size distribution Permeability is controlled by: Packing Grain size distribution

= 47.6 % k = 5.00 md r = 0.5 Grain size Porosity is independent of MRIAN XXXX Magnetic Resonance

Imaging Analysis MRIL service X Bin Distribution is a function of: Pore size Fluid type XXXX XXXX Case study Seven wells were drilled in Hemphill and Wheeler Counties, TX

All were air drilled to avoid drilling problems and mud invasion into the formation 5 wells were loaded with fluid before logging 2 were logged with no fluid in the well Client driven partnership between the operator and the service company. Stimulation and wireline logging personnel met with client representatives to determine the best procedures and techniques for success. Drilling, logging, and stimulation Rotary cores from an offset well were used to aid in the petrophysical analysis. Cores were characterized by NMR laboratory measurements to define BVI and SBVI relationships and permeability parameters.

Triple combo data was logged. Sonic was not, but could have been used in the frac design. NMR (Magnetic Resonance Imaging Log MRIL) was recommended and run as a porosity, BVI, and permeability measurement. Conventional rotary core and NMR analysis: Porosity, permeability and grain density NMR T2 analysis for BVI, SBVI and permeability coefficients At the time, Coates IV was the preferred permeability equation: MPERM MPHI / C 2 * ( FFI / BVI )

m The Bin Perm Equation is based on the relationship of pore size to T2 time. High porosity in the larger bin sizes increases bin permeability: T 2 Bphi 2048 ms BPERM MPHI / C 2 * ( wf * T Bphi / BVI ) 2 T 2 Bphi 4 ms

m T2 time is highly influenced by pore size and fluid type. wf is a weighting factor based on NMR Bin distribution Typical Well Stimulation Program Job Procedure Granite Wash A Proposal for the Waterfrac of the Granite Wash A interval at 130bpm and 3100psi down the casing. Job Summary

7.5%_Hydrochloric Acid Treated Water 336,923 Premium White-20/40 Total Job Volume 340,923 Total HES Supplied Water Total Water Required Total Proppant Quantity Pad Percentage 50.90 Job Rate 130 bbl/min Total Customer Supplied ** Fresh Water ** 336,923

4,000 Gal 180,000 Gal 3,240 340,163 180,000 % Gal lbm Gal Gal lbm Gal

Drilling, logging, and stimulation procedures were fairly uniform across all seven wells Granite Wash A & B Permeability Comparisons Comparison of Permeability equations: XXXX Timur Coates IV

BinPerm BinPerm response in relatively high permeability XXXX BinPerm response in relatively low permeability XXXX Permeability Comparisons MRIL BigBin Perm Coates IV Perm

Timur Production vs Permeability 400 Daily Gas Production MCF/Day 350 300 250 200 150 100 50 0 0.000

0.002 0.004 0.006 0.008 0.010 0.012 Ave rage Perme ability (M D/Ft)

0.014 0.016 0.018 0.020 Permeability Comparisons MRIL BigBin Perm Coates IV Perm Timur Production vs Permeability 500000

Cumulative Gas Production MCF 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 0.000 0.002

0.004 0.006 0.008 0.010 0.012 Ave rage Permeability (M D/Ft) 0.014

0.016 0.018 0.020 Conclusions Permeability measurements using NMR bin-porosity and BVI data provide a good indicator of ultimate hydrocarbon production, at least in this Granite Wash field study. More comparisons are suggested to determine if this method can be applied to other fields and formation types.

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