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EP-4055418-B1 - IDENTIFYING FLUID TYPES AND ASSOCIATED VOLUMES IN ROCK SAMPLES USING NUCLEAR MAGNETIC RESONANCE ANALYSES

EP4055418B1EP 4055418 B1EP4055418 B1EP 4055418B1EP-4055418-B1

Inventors

  • Ijasan, Olabode
  • McLendon, Darren M.

Dates

Publication Date
20260506
Application Date
20200814

Claims (13)

  1. A computer-implemented method comprising: identifying modes in NMR T 1 -T 2 data, wherein NMR, T 1 and T 2 refer to nuclear magnetic resonance, longitudinal relaxation time and transverse relaxation time respectively, from a plurality of rock samples with a multimodal deconvolution or decomposition; deriving a modal properties vector comprising modal properties for each of the modes; performing a cluster analysis of the modes to identify clusters; assigning a poro-fluid class to the clusters based on one or more of the modal properties of the modes in each of the clusters; and deriving partitioned representations for the clusters based on the cluster analysis, CHARACTERIZED IN THAT : the modes are identified using regularized nonlinear inversion selected from the group consisting of Gauss-Newton inversion, Landweber inversion, Levenberg-Marquartz inversion and Occam's inversion; and wherein the modal properties vector is a mathematical representation of the poro-fluid modal properties for a given mode, or all present modes, wherein the poro-fluid modal properties include pore volume, peak T 1 , peak T 2 , peak T 1 /T 2 ratio or shape covariance matrix.
  2. The method of claim 1, wherein the multimodal deconvolution or decomposition is selected from the group consisting of Gaussian, Lorentzian, Voigt, exponentially modified Gaussian.
  3. The method of any preceding claim, wherein the nonlinear regularization inversion is an iterative calculation of multiple regularizations where the regularization in each iteration having a minimum misfit proceeds to the next iteration.
  4. The method of any preceding claim, wherein the poro-fluid classes are selected from the group consisting of free fluid, fluid in pores, fluid in macroporosity or fractures, fluid in inorganic pores, fluid in organic pores, free liquid, liquid in pores, liquid in macroporosity or fractures, liquid in inorganic pores, liquid in organic pores, free gas, gas in pores, gas in macroporosity or fractures, gas in inorganic pores, gas in organic pores, free oil, oil in pores, oil in macroporosity or fractures, oil in inorganic pores, oil in organic pores, free water, water in pores, water in macroporosity or fractures, water in inorganic pores, water in organic pores, clay-associated water, clay-bound water, surface relaxation-dominated fluid, surface relaxation-dominated oil, surface relaxation-dominated water, bulk relaxation-dominated fluid, bulk relaxation-dominated oil, bulk relaxation-dominated water, bulk relaxation-dominated gas, bound fluid, bound oil, bound water, capillary-bound fluid, capillary-bound water, capillary-bound oil, bitumen, bound hydrocarbon, free hydrocarbon.
  5. The method of any preceding claim, wherein assigning the poro-fluid class to the clusters includes analyzing a location of the modes on a plot of T 1 /T 2 ratio as a function of T 1 and a plot of T 1 /T 2 ratio as a function of T 2 .
  6. The method of any preceding claim further comprising: acquiring T 1 and T 2 relaxation time data for fluids in a rock sample; and determining the poro-fluid classes and respective amounts of the fluids in the rock sample based on partitioned representations for the clusters.
  7. The method of claim 6, wherein the rock sample is a subterranean formation or a core sample from the subterranean formation, and the method further comprises: managing hydrocarbons based at least in part upon the respective amounts of the fluids in the rock sample.
  8. The method of claim 7, wherein managing hydrocarbons comprises one or more of: identifying a zone of the subterranean formation for completion, and causing one or more completion operations to be carried out on the identified zone; identifying a portion of the subterranean formation from where to obtain a core sample for further analysis, and obtaining one or more core samples from the identified portion; causing a simulation or completion operation to be carried out on the subterranean formation; and/or identifying a horizontal well landing location within the subterranean formation, and causing a horizontal well to be drilled to the identified landing location.
  9. The method of claim 1, wherein the plurality of samples comprises a plurality of core samples from a subterranean formation.
  10. The method of claim 1 or 9, wherein the NMR T 1 -T 2 data from a plurality of samples comprises NMR logging data for fluids in a subterranean formation.
  11. The method of claim 1 or 9 or 10, wherein the plurality of samples comprise a synthetic core sample.
  12. The method of claim 1 further comprising: performing a NMR logging operation for a subterranean formation; analyzing data from a first portion of the NMR logging operation in real-time to produce the partitioned representations; and determining the poro-fluid classes and respective amounts of the fluids in the subterranean formation for a second portion of the NMR logging operation based on the partitioned representations.
  13. A computing device comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the method of claim of any one of claims 1-12.

Description

BACKGROUND The present disclosure relates to nuclear magnetic resonance (NMR) analyses for identifying the types of fluids and associated volumes in subterranean formations via core rock samples and/or borehole logging techniques. During oil and gas exploration, zones with higher concentrations of oil and gas can be identified as target zones. One method of identifying target zones is using NMR analysis with core rock samples and/or borehole logging techniques. One such NMR technique is to analyze a two-dimensional NMR cross-plot, specifically of the longitudinal relaxation time (T1) versus the transverse relaxation time (T2). The sum of the NMR signal amplitude over the T1-T2 cross-plot is proportional to the total fluid-filled porosity of the sample for which the NMR measurements were performed. A more detailed analysis of the T1-T2 cross-plot can be performed using polygonal partitioning, which is the most common industry practice for detailed interpretation of T1-T2 cross-plots. That is, portions of the T1-T2 cross-plot are partitioned and assigned a fluid type (e.g., water, gas, or oil). The sum of the NMR signal amplitude over individual partitions is proportional to the volume of the assigned fluid in the sample, for example, as described in (a) Kausik, R., Fellah, K., Rylander, E., Singer, P. M., Lewis, R. E., and Sinclair, S. M., 2016, NMR relaxometry in shale and implications for logging: Petrophysics, 57, no. 4, 339-350, (b) Xie, Z. H., and Gan, Z., 2019, Investigation of physical properties of hydrocarbons in unconventional mudstones using two-dimensional NMR relaxometry: Transactions of the Society of Petrophysicists and Well Log Analysts, 60th Annual Logging Symposium, and (c) Ye, S., Scribner, A., McLendon, D., Ijasan, O., Chen, S., Shao, W., and Balliet, R., 2019, Method of determining unconventional reservoir saturation with NMR logging: Society of Petroleum Engineers Annual Technical Conference and Exhibition, SPE 196069. Other techniques assume linear combinations of a priori known fluid sources that may be blindly separated, for example, as described in Anand, V., Ali, M. R., Abubakar, A., Grover, R., Neto, O., Pirie, I., and Iglesias, J. G., 2017, Unlocking the potential of unconventional reservoirs through new generation NMR T1/T2 logging measurements integrated with advanced wireline logs: Petrophysics, 58, no. 2, 81-96. However, the T1 and T2 values associated with a fluid can vary based on the type of rock (e.g., shale, sandstone, carbonates, and the like), fluid composition (gaseous or liquid), and NMR frequency (low or high field); so the partitions for one rock and measurement type may differ from another rock and measurement type. There are several tools that can be used to perform polygonal partitioning where the approaches to defining the polygons to partition the T1-T2 cross-plot vary widely, for example as described in Venkataramanan, L., Evirgen, N., Allen, D. F., Mutina, A., Cai, Q., Johnson, A. C., Green, A. Y., Jiang, T., 2018, An unsupervised learning algorithm to compute fluid volumes from NMR T1-T2 logs in unconventional reservoirs: Transactions of the Society of Petrophysicists and Well Log Analysts, 59th Annual Logging Symposium. Further, the person using the tools to analyze the T1-T2 cross-plot typically has a good deal of control over the partition boundaries, in part because partitions can vary based on rock type. This user control makes what appear to be systematic tools more subjective. These tools also do not account for any overlapping signals from different fluids. Accordingly, the state-of-the-art tools for a widely used analysis technique (polygonal partitioning of T1-T2 cross-plots) used to identify target zones can be tedious and inaccurate. SUMMARY OF THE INVENTION The present disclosure relates to NMR T1-T2 cross-plot analyses (referred to herein as a NMR petrophysical pore multimodal (NPPM) analysis) for identifying the types of fluids and associated volumes in subterranean formations. The invention is defined by the appended claims. A method of the present disclosure comprises: identifying modes in NMR T1-T2 data from a plurality of samples with a multimodal deconvolution or decomposition with regularized nonlinear inversion; deriving a modal properties vector comprising modal properties for each of the modes; performing a cluster analysis of the modes to identify clusters; assigning a poro-fluid class to the clusters based on one or more of the modal properties of the modes in each of the clusters; and deriving partitioned representations for the clusters based on the cluster analysis. A computing device of the present disclosure comprises: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the method according to the foregoing method. BRIEF DESCRIPTION OF THE DRAWINGS The following figures are included to illustrate certain aspects of the embo