阅读说明：本技术 对井下多维测量结果的快速测量和解释 (Rapid measurement and interpretation of downhole multi-dimensional measurements ) 是由 Y-Q·宋 R·K·凯德亚姆·维斯瓦纳坦 M·赫里曼 V·阿南德 A·穆蒂纳 于 2018-05-23 设计创作，主要内容包括：地质地层的井下性质可使用由移动工具获得的核磁共振(NMR)测量结果来确定。为此,对通过移动数据获得的NMR数据的解释可考虑井下NMR工具的移动模型、表征或校准。另外地或可替代地,部分解释掩模可排除预期不太可能描述感兴趣的井下材料的某些数据区域(例如,T1-T2数据点或扩散系数-T2数据点)的解释。(The downhole properties of the geological formation may be determined using Nuclear Magnetic Resonance (NMR) measurements obtained from the mobile tool. To this end, the interpretation of the NMR data obtained from the movement data may take into account movement models, characterization, or calibration of the downhole NMR tool. Additionally or alternatively, the partial interpretation mask may exclude interpretation of certain data regions (e.g., T1-T2 data points or diffusion coefficient-T2 data points) that are not expected to likely describe the downhole material of interest.)

1. A method, the method comprising:

obtaining one or more nuclear magnetic resonance measurements from a geological formation using one or more downhole logging tools, wherein each of the one or more nuclear magnetic resonance measurements includes corresponding data;

obtaining a standard kernel based at least in part on known sensitivities of the one or more nuclear magnetic resonance measurements;

applying a mask to the one or more nuclear magnetic resonance measurements, the standard kernel, or both, to generate mask data;

inverting the mask data; and

determining a distribution function using the inverted mask data, wherein the distribution function indicates the presence or absence of certain components of a geological formation.

2. The method of claim 1, wherein the mask is selected from a plurality of defined masks based at least in part on data exceeding a defined threshold.

3. The method of claim 1, wherein the mask is referenced from a memory of a computing device.

4. The method of claim 1, wherein the mask is defined based at least in part on a mathematical equation or shape that relates the one or more nuclear magnetic resonance measurements to each other, wherein the mask is configured to exclude a portion of the one or more nuclear magnetic resonance measurements and a portion of the normative kernel.

5. The method of claim 1, wherein the one or more nuclear magnetic resonance measurements are obtained based at least in part on data acquisition parameters related to the mask.

6. The method of claim 1, wherein the kernel data is a modified kernel, wherein the modified kernel is based at least in part on longitudinal magnetization within a detector section of the one or more downhole logging tools and the standard kernel.

7. The method of claim 1, wherein the mask is determined based at least in part on a known composition of the geological formation.

8. The method of claim 1, wherein the one or more nuclear magnetic resonance measurements comprise two-dimensional nuclear magnetic resonance measurements of T1 and T2.

9. The method of claim 1, wherein the one or more nuclear magnetic measurements are obtained from one or more mobile downhole logging tools.

10. An article comprising a tangible, non-transitory, machine-readable medium comprising instructions that when executed by a processor cause the processor to:

obtaining one or more nuclear magnetic resonance measurements from a geological formation using one or more downhole logging tools, wherein each of the one or more nuclear magnetic resonance measurements includes corresponding data;

determining a modified kernel related to the one or more downhole logging tools, wherein the modified kernel is based at least in part on a sensitivity of the one or more nuclear magnetic resonance measurements;

applying a mask to the respective data of the one or more nuclear magnetic resonance measurements to generate mask data;

inverting the mask data using the modified kernel to generate inverted mask data; and is

A distribution function is determined using the inverted mask data.

11. The article of manufacture of claim 10, wherein the modified inner core of the one or more downhole logging tools is associated with a mobile logging tool.

12. The article of claim 10, wherein the one or more nuclear magnetic resonance measurements comprise multi-dimensional nuclear magnetic resonance measurements comprising at least T1 and T2.

13. The article of claim 10, wherein the one or more nuclear magnetic resonance measurements comprise a multi-dimensional nuclear magnetic resonance measurement comprising at least a diffusion coefficient (D) and T2.

14. The article of claim 10, wherein the mask corresponds to data indicating the presence of water, gas, oil, pitch, or any combination thereof.

15. The article of claim 10, wherein the mask is drawn by an operator.

16. The article of claim 10, wherein the mask is selected from a plurality of previously defined masks based at least in part on data exceeding a defined threshold.

17. The article of claim 10, wherein the mask is selected based at least in part on data of the one or more nuclear magnetic resonance measurements exceeding a defined threshold.

18. A system, the system comprising:

a downhole logging tool configured to obtain one or more nuclear magnetic resonance measurements from a geological formation; and

a data processing system comprising a processor, wherein the downhole logging tool is configured to receive the one or more nuclear magnetic resonance measurements, and wherein the processor is configured to:

determining a kernel associated with the downhole logging tool, wherein the kernel is based at least in part on a sensitivity of the one or more nuclear magnetic resonance measurements;

applying a mask to the one or more nuclear magnetic resonance measurements to generate mask data;

inverting the mask data using the kernel to generate inverted mask data; and is

Determining a distribution function representing a composition of the geological formation using the inverted mask data.

19. The system of claim 18, wherein the kernel is further based at least in part on a profile of longitudinal magnetization within a detector region of the downhole logging tool.

20. The system of claim 18, wherein the mask is selected based at least in part on data of the one or more nuclear magnetic resonance measurements exceeding a defined threshold.

Background

The present disclosure relates to rapidly obtaining and interpreting downhole multi-dimensional Nuclear Magnetic Resonance (NMR) measurements in geological formations.

This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present technology that are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. It should be understood, therefore, that these statements are to be read in this light, and not as admissions of any form.

The production of hydrocarbons from a wellbore drilled into a geological formation is a very complex task. In many cases, decisions involved in hydrocarbon exploration and production may be informed by measurements from downhole logging tools that are conveyed deep in the wellbore. The measurements may be used to infer properties and characteristics of the geological formation surrounding the wellbore.

One type of downhole logging tool uses Nuclear Magnetic Resonance (NMR) to measure the response of nuclear spins in formation fluids to an applied magnetic field. Many NMR tools have permanent magnets that generate a static magnetic field at the desired test location (e.g., where the fluid is located). The static magnetic field produces an equilibrium magnetization in the fluid that is aligned with the magnetization vector in the direction of the static magnetic field. The transmitter antenna generates a time-dependent radio frequency magnetic field perpendicular to the direction of the static field. The rf magnetic field produces a torque on the magnetization vector that causes it to rotate about the axis of the applied rf magnetic field. The rotation causes the magnetization vector to form a component perpendicular to the direction of the static magnetic field. This causes the magnetization vector to align with the component perpendicular to the direction of the static magnetic field and precess around the static field.

The time at which the magnetization vector realigns with the static magnetic field is referred to as the longitudinal magnetization recovery time or "T1 relaxation time". Due to the precession of the magnetization vector, the spins of adjacent atoms precess in series with each other in synchronism. The time at which the spin precession of adjacent atoms ceases to be synchronized is referred to as the transverse magnetization decay time or "T2 relaxation time". Thus, measurements obtained by the downhole NMR tool may include a distribution of the first relaxation time T1, the second relaxation time T2, or the molecular diffusion coefficient (D), or a combination of these. For example, the downhole NMR tool may measure only the T2 distribution, or the tool may measure the combined T1-T2 distribution or the T1-T2-D distribution.

Any movement of the downhole tool in the wellbore affects the accuracy of the measurements. To improve the accuracy of the measurements, the downhole NMR tool may be moved to a fixed station, or may be moved relatively slowly through the wellbore. However, the slower the downhole NMR tool is moved through the wellbore, the longer it takes to complete the measurement. Thus, there may be an undesirable tradeoff in deciding whether to obtain a faster but less accurate or more accurate but slower downhole NMR measurement. In practice, many implementations of this measurement may be very slow, and the resulting logging speed may be quite low, such as slower than 300 ft/hr.

Disclosure of Invention

A summary of certain embodiments disclosed herein is set forth below. These aspects are presented merely to provide the reader with a summary of these certain embodiments and are not intended to limit the scope of the disclosure. Indeed, the present disclosure may encompass a variety of aspects that may not be set forth below.

To perform downhole NMR measurements more quickly, the present disclosure describes NMR systems and methods that may allow interpretation of NMR measurements obtained by a downhole NMR tool that is moving rather than stationary. To this end, the interpretation of the NMR data obtained from the movement data may take into account movement models, characterization, or calibration of the downhole NMR tool. Additionally or alternatively, the partial interpretation mask (mask) may exclude the interpretation of certain regions of the data that are not expected to likely describe the downhole material of interest (e.g., T1-T2 data points or diffusion coefficient-T2 data points).

Various refinements of the features noted above may be made in relation to various aspects of the present disclosure. Other features may also be incorporated into these various aspects as well. These refinements and additional features may exist individually or in any combination. For example, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure, alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

Drawings

Aspects of the disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:

FIG. 1 is a schematic diagram of a logging system that can quickly obtain and/or interpret Nuclear Magnetic Resonance (NMR) logging measurements, according to an embodiment;

fig. 2 is a flow diagram of a method for using the system of fig. 1, according to an embodiment;

FIG. 3 is a T1-T2 map of different pore fluids and a table showing corresponding T1/T2 ratios for different pore fluids, according to an embodiment;

fig. 4 is a T1-T2 graph illustrating an exemplary response of a first shale sample, according to an embodiment;

fig. 5 is a T1-T2 graph illustrating an exemplary response of a second shale sample, according to embodiments;

fig. 6 is a T1-T2 graph illustrating an exemplary response of a third shale sample, according to embodiments;

fig. 7 is a timing diagram illustrating a carl-poisel-mebbmm-gill (CPMG) sequence for NMR measurements, according to an embodiment;

fig. 8 is a timing diagram illustrating composite results of multiple CPMG pulse sequences for simultaneous T1-T2 two-dimensional (2D) NMR measurements, according to an embodiment;

fig. 9 is a diffusion coefficient-T2 map illustrating an exemplary response in a gas zone, according to an embodiment;

fig. 10 is a diffusion coefficient-T2 map illustrating an exemplary response in a water zone, according to an embodiment;

FIG. 11 is a schematic diagram of a mobile NMR logging tool according to an embodiment;

FIG. 12 is an exemplary graph of a core for the mobile NMR logging tool of FIG. 11, according to an embodiment;

FIG. 13 is a graph of an example of a partial kernel for the mobile NMR logging tool of FIG. 11, according to an embodiment;

FIG. 14 is an example of a full mask for a T1-T2 distribution according to an embodiment;

fig. 15 is an example of a partial mask for a T1-T2 distribution that does not account for values below T1/T2-1, according to an embodiment;

fig. 16 is an example of a partial mask for a T1-T2 distribution that does not account for values below T1/T2 of 1 or regions that rarely depict a fluid of interest, according to an embodiment;

fig. 17 is an example of a partial mask for a T1-T2 distribution that does not account for values below T1/T2 of 1 or is less likely to describe larger regions of a fluid of interest, according to an embodiment;

FIG. 18 is an example of a partial mask for diffusion coefficient-T2 distribution that does not account for certain values that are unlikely to contain a fluid of interest, according to an embodiment;

FIG. 19 is a first exemplary NMR log that may be obtained more quickly using the systems and methods of the present disclosure; and is

FIG. 20 is a second exemplary NMR log that may be obtained more quickly using the systems and methods of the present disclosure.

Detailed Description

One or more specific embodiments of the present disclosure will be described below. These described embodiments are examples of the presently disclosed technology. In addition, certain features of an actual implementation may not be described in the specification in order to provide a concise description of these embodiments. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present disclosure, the articles "a," "an," and "the" are intended to mean that there are one or more of the elements. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements. In addition, references to "one embodiment" or "an embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

The present disclosure describes systems and methods that may be used to more quickly record and interpret measurements obtained by a downhole Nuclear Magnetic Resonance (NMR) tool. In particular, multi-dimensional NMR measurements, e.g., two-dimensional NMR measurements, (and/or, in some cases, one or more well log measurements related to total organic carbon) may be used to estimate various formation properties, such as downhole fluid volumes of bitumen, light hydrocarbons, kerogen, and water, and/or Reservoir Productivity Index (RPI), among others. To perform downhole NMR measurements more quickly, the present disclosure describes NMR systems and methods that may allow interpretation of NMR measurements obtained by a downhole NMR tool that is moving rather than stationary. To this end, the interpretation of the NMR data obtained from the movement data may take into account movement models, characterization, or calibration of the downhole NMR tool. Additionally or alternatively, the partial interpretation mask (mask) may exclude the interpretation of certain regions of the data that are not expected to likely describe the downhole material of interest (e.g., T1-T2 data points or diffusion coefficient-T2 data points).

With this in mind, FIG. 1 illustrates a logging system 10 in which the systems and methods of the present disclosure may be employed. The logging system 10 may be used to convey a downhole tool 12 through a wellbore 16 through a geological formation 14. The downhole tool 12 may be conveyed on a wireline 18 by a logging winch system 20. Although the logging winch system 20 is schematically illustrated in fig. 1 as a mobile logging winch system carried by a truck, the logging winch system 20 may be substantially stationary (e.g., a long-term installation that is substantially permanent or modular). Any suitable cable 18 for logging may be used. The cable 18 may be spooled and unspooled on a spool 22, and an auxiliary power source 24 may provide power to the logging winch system 20 and/or the downhole tool 12.

Further, while the downhole tool 12 is described as a wireline downhole tool, it should be understood that any suitable conveyance device may be used. For example, the downhole tool 12 may alternatively be conveyed as a Logging While Drilling (LWD) tool, as part of a Bottom Hole Assembly (BHA) of a drill string, on a wireline or through coiled tubing, and so forth. For purposes of this disclosure, the downhole tool 12 (e.g., the downhole NMR tool 12) may be any suitable measurement tool that obtains NMR logging measurements through the depth of the wellbore 16.

Many types of downhole tools may obtain NMR logging measurements in the wellbore 16. These include, for example, Nuclear Magnetic Resonance (NMR) tools such as a Combination Magnetic Resonance (CMR) tool, a magnetic resonance scanner (MRX) tool, and a provisionon tool manufactured by schlumberger technology Corporation. Generally, an NMR tool may have a permanent magnet that generates a static magnetic field at a desired test location (e.g., where a fluid is located). The static magnetic field produces an equilibrium magnetization in the fluid that is aligned with the magnetization vector in the direction of the static magnetic field. The transmitter antenna generates a time-dependent radio frequency magnetic field perpendicular to the direction of the static field. The rf magnetic field produces a torque on the magnetization vector that causes it to rotate about the axis of the applied rf magnetic field. The rotation causes the magnetization vector to form a component perpendicular to the direction of the static magnetic field. This causes the magnetization vector to align with the component perpendicular to the direction of the static magnetic field and precess around the static field.

The time at which the magnetization vector realigns with the static magnetic field is referred to as the longitudinal magnetization recovery time or "T1 relaxation time". Due to the precession of the magnetization vector, the spins of adjacent atoms precess in series with each other in synchronism. The time at which the spin precession of adjacent atoms ceases to be synchronized is referred to as the transverse magnetization decay time or "T2 relaxation time". Thus, the measurements obtained by the downhole tool 12 may include a distribution of the first relaxation time T1, the second relaxation time T2, or the molecular diffusion coefficient D, or a combination of these. For example, the downhole NMR tube 12 may measure only the T2 distribution, or the downhole NMR tool 12 may measure a combined T1-T2 distribution or T1-T2-D distribution.

For each depth of the wellbore 16 measured, the downhole NMR tool 12 may generate NMR logging measurements that include a distribution of magnitudes of T2 relaxation times, T1 relaxation times, diffusion coefficients, or a combination thereof. This list is intended to present some examples and is not intended to be exhaustive. Indeed, any suitable downhole tool 12 that obtains NMR logging measurements may benefit from the systems and methods of the present disclosure.

The downhole tool 12 may provide the logging measurements 26 to the data processing system 28 by any suitable telemetry, such as by electrical signals pulsed through the geological formation 14 or by mud pulse telemetry. The data processing system 28 may process the NMR logging measurements 26 to identify patterns in the NMR logging measurements 26. The pattern in the NMR logging measurements 26 may indicate certain properties (e.g., viscosity, porosity, permeability, relative proportions of water and hydrocarbons, etc.) that may otherwise be indistinguishable by an operator of the wellbore 16.

To this extent, the data processing system 28 can thus be any electronic data processing system that can be used to perform the systems and methods of the present disclosure. For example, data processing system 28 may include a processor 30, which processor 30 may execute instructions stored in a memory 32 and/or a storage device 34. As such, the memory 32 and/or storage 34 of the data processing system 28 may be any suitable article of manufacture that may store instructions. The memory 32 and/or storage 34 may be, for example, ROM memory, Random Access Memory (RAM), flash memory, optical storage medium, or a hard disk drive. The display 36 (which may be any suitable electronic display) may use the NMR logging measurements 26 to provide a visualization, log, or other indication of the property in the geological formation 14 or wellbore 16.

The flow chart 50 of fig. 2 describes a method for rapidly estimating downhole fluid volume from NMR measurements even in shale reservoirs. That is, the downhole NMR tool 12 may be placed in the wellbore 16 (block 52), and multi-dimensional NMR measurements of the wellbore 16 (e.g., T1, T2, and/or diffusion coefficient (D) measurements) may be obtained while the downhole NMR tool 12 is moved (block 54). The data processing system 28 may interpret the multi-dimensional NMR measurements using a kernel based on characteristics of the mobile downhole NMR tool and/or using a partial multi-dimensional NMR map that reduces computations on uninteresting multi-dimensional NMR measurements (block 56). Interpretation of the NMR measurements may be used to identify formation properties using any suitable technique (block 58). The identified formation properties may be output onto a log (block 60), which may enable a decision maker to make production and production decisions appropriate for the conditions of the geological formation 14.

Next, NMR measurements will be discussed. The hydrocarbon liquids normally encountered in oil fields predominantly pass through ¹The dipolar coupling between the H spin nuclei effects NMR relaxation. Other mechanisms include interactions between spin-bearing nuclei with unpaired electrons, which may be dominant in view of the much larger magnetic moment of the electrons. A common source of such electron spins is paramagnetic ions or free radicals found in crude oil or surrounding rock minerals. Methane natural gas undergoes relaxation in its bulk state by the mechanism of spin rotation. The NMR relaxation times of liquid hydrocarbons can be divided into different additional rates as given below:

intramolecular interactions

Due to intermolecular relaxation by interaction with other nuclear spins in the same molecule, or due to local rotational motion

Due to interaction between spins in different molecules, and

due to the dipolar interaction of nuclear spins with unpaired electrons. To better understand the sensitivity of relaxation to molecular motion, T can be used ₁And T ₂Described as a function of their spectral density. The relaxation times for the homonuclear interactions are given as follows:

wherein, mu ₀Is the vacuum permeability, I is the spin number (for proton nuclei, I ═ 1/2), γ is the gyromagnetic ratio, h is the planck constant over 2 π, and r is the internuclear distance. The spectral density I (ω) can be determined by the autocorrelation function g (t) ═ t<B(τ)B(τ+t)>Is obtained, the autocorrelation function describes a time-dependent fluctuation of the local magnetic field b (t). T is ₂The relaxation time is dominated by the I (ω ═ 0) term and is therefore very sensitive to low frequencies or slow motion. T is ₁The relaxation time is sensitive to higher Larmor (Larmor) frequencies (ω and 2 ω) and hence to the applied magnetic field (B) ₀) And (4) sensitivity. Longitudinal relaxation time T obtained in the limit of very low Larmor frequency due to the dominance of the I (ω) term ₁(ωτ<<1) And T ₂(ω) is proportional. Thus, T ₁-T ₂The spectra are sensitive to molecular motion in the frequency range between the measured larmor frequency and the very low frequency. This indicates the use of T ₁/T ₂The ratio as a parameter to reflect the molecular movement of a fluid in its bulk state or when constrainedImportance of mobility. FIG. 3 summarizes the general T of all the different components of gas and dense oil shale at 2MHz Larmor frequency ₁-T ₂And (4) mapping.

Different materials may appear in different locations on a multi-dimensional NMR spectrum, such as a T1-T2 spectrum. FIG. 3 illustrates a variety of different types of materials that may be classified based on location in the T1-T2 map 220. The T1-T2 map 220 shows the synthetic NMR measurements of T1 relaxation time (ordinate 72) and T2 relaxation time (abscissa 74), each expressed on a logarithmic scale. The T1-T2 map 220 includes lines representing different T1/T2 ratios across the T1-T2 map 220. Specifically, the T1-T2 map 220 shown in FIG. 10 includes a line 222 showing a T1/T2 ratio of 1, a line 224 showing a T1/T2 ratio of 2, a line 226 showing a T1/T2 ratio of 5, and a line 228 showing a T1/T2 ratio of 10. Visualization of NMR measurements along different T1/T2 ratios, and thus crossovers 222, 224, 226, and 228, may be one way to identify the type of pore fluid that has been detected in the NMR measurement. In addition, some pore fluids may be visible in low field NMR (T2 value is above threshold 230, which may be a signal greater than about 2MHz in some examples).

The different pore fluids located on the T1-T2 pattern 220 include kerogen 232, bitumen 234, clay bound water 236, immobile oil 238 in Organic Pores (OP), mobile oil 239 in Organic Pores (OP), oil 240 in Inorganic Pores (IP), gas 241 in Organic Pores (OP), water 242 in Inorganic Pores (IP), gas 243 in inorganic pores (OP), oil 244, water 246, and gas 248. The corresponding T1/T2 ratios are shown in Table 250. The T1/T2 ratio for bulk or macroporous fluids is close to 1. As the pore size becomes smaller, T2 becomes shorter and the T1/T2 ratio becomes higher. The T1/T2 ratio of hydrocarbon is higher than the T1/T2 ratio of water. Thus, for tight oil reservoirs, it is possible to separate water and oil signals using identification based on appropriate T2 and T1/T2 ratios. These may be done in the manner discussed above or using any other suitable technique.

Multidimensional NMR in shale gas formations

Natural gas (predominantly methane) molecules in bulk state relax primarily through spin rotation mechanisms and have a T ₁＝T ₂And is given byAnd (3) discharging:

wherein tau is _FIs the correlation time of the rotation, k is the Boltzmann constant, I ₁Is the moment of inertia of a spherical molecule, T is the temperature, C _||And C _⊥Are the main components (parallel and perpendicular) of the spin rotation tensor. Correlation time tau _FInversely proportional to the viscosity of the fluid. It has also recently been shown that the spin rotation mechanism continues to dominate the relaxation of bulk methane gas, even up to 10,000psi (density of 0.307 g/cm) ³)。

The natural gas in the shale gas is mainly hosted in the organic kerogen pores in the form of free gas and adsorbed gas. The size of these pores is typically in the nanometer to micrometer range, resulting in high surface to volume ratios and thus increased interaction between gas molecules and the pore surfaces. The adsorbed gas molecules typically have a longer residence time on the pore surface, resulting in enhanced relaxation due to mechanisms such as translational diffusion mediated Reorientation (RMTD). In addition, intermolecular dipolar interactions between the adsorption phase and the core in organic kerogen will result in additional relaxation. And NMR T ₂Compared to the relaxation time, the free gas molecules and adsorbed gas molecules undergo rapid exchange, resulting in a single relaxation distribution.

The T of a gas shale sample saturated with methane gas at 5000psi is shown in FIG. 4 ₁-T ₂Map 260. The T1-T2 map 260 shows NMR measurements of T1 relaxation time (ordinate 72) and T2 relaxation time (abscissa 74), each expressed on a logarithmic scale. The T1-T2 map 260 includes lines representing different T1/T2 ratios across the T1-T2 map 260. Specifically, the T1-T2 map 260 shown in FIG. 4 includes line 262 showing a T1/T2 ratio of 1, line 264 showing a T1/T2 ratio of 2, line 266 showing a T1/T2 ratio of 2.6, the latter two having been subjected to NMR measured local peaks on the T1-T2 map 260. T of methane gas in organic pores ₁-T ₂A ratio of about 2.6, T ₂Ranges from a few milliseconds to a few tens of milliseconds and thus may overlap the bound water signal. In the ring hole of the sample holderIs shown as T in FIG. 4 ₂Greater than 100ms and T ₁/T ₂The ratio is 2. In other words, T of methane gas in the organic pores ₂Is 10ms, and T ₁/T ₂T of 2.6, and annular gas (above and around the cylindrical sample) ₂A value greater than 100ms, and T ₁/T ₂The ratio is 2.

Multi-dimensional NMR in tight/shale oil formations

Light oils in the bulk state relax due to intermolecular and intramolecular dipole relaxation, which is generally proportional to their chain length. In the case of bitumen and other heavy oils, relaxation behavior is more complex due to the presence of asphaltenes. Maltenes or lighter fraction oils relax by both proton-proton intermolecular interactions, modulated by slow motion due to their interaction with asphaltenes, and proton-electron interactions with paramagnetic species and free radicals in asphaltenes. In addition, due to wettability, the relaxation mechanism of oils in organic pores is different from those in inorganic pores, and thus NMR T ₁-T ₂The spectra can be used exclusively as probes for the separation of oil-filled pores into organic kerogen pores versus inorganic mineral lodging. The inorganic pores in the dense oil shale are wet mixed, resulting in a reduced relaxation time of the oil and a T ₁-T ₂The ratio is about 1.2 to 1.5.

FIG. 5 shows NMR T in dense oil shale ₁-T ₂An example of a map 270. The T1-T2 map 270 shows NMR measurements of T1 relaxation time (ordinate 72) and T2 relaxation time (abscissa 74), each expressed on a logarithmic scale. The T1-T2 map 270 includes lines representing different T1/T2 ratios across the T1-T2 map 270. Specifically, the T1-T2 map 270 shown in FIG. 5 includes a line 272 showing a T1/T2 ratio of 1, a line 274 showing a T1/T2 ratio of 2, a line 276 showing a T1/T2 ratio of 5, and a line 278 showing a T1/T2 ratio of 16, each of which passes through a local peak on the T1-T2 map 270 as measured by NMR. By comparing the measured peak positions of the T1 and T2NMR measurements to previously identified positions of various pore fluids (e.g., as shown in fig. 3), a T1-T2 map 270 may be shown with identified bitumen, heavy oil, and bound water in region 280 and in region 282There is oil in the organic pores and in region 284 there is oil in the inorganic pores.

FIG. 6 shows NMR T in dense oil shale ₁-T ₂An example of an atlas 290. The T1-T2 map 290 shows NMR measurements of T1 relaxation time (ordinate 72) and T2 relaxation time (abscissa 74), each expressed on a logarithmic scale. The T1-T2 map 290 includes lines representing different T1/T2 ratios across the T1-T2 map 290. Specifically, the T1-T2 map 290 shown in FIG. 6 includes a line 292 showing a T1/T2 ratio of 1, a line 294 showing a T1/T2 ratio of 2, a line 296 showing a T1/T2 ratio of 5, and a line 298 showing a T1/T2 ratio of 16, each of which passes through a local peak on the T1-T2 map 290 as measured by NMR. By comparing the measured peak positions of the T1 and T2NMR measurements to previously identified positions of various pore fluids (e.g., as shown in fig. 3), the T1-T2 map 290 can be shown as having identified bitumen, heavy oil, and bound water in region 300, oil in organic pores in region 302, and oil in inorganic pores in region 304.

Indeed, FIG. 5 shows data from a natural shale sample, where T ₁/T ₂Ratio and T ₂Together enabling separation of the bound water and bitumen signals from the signals of the oil in the organic pores is a natural shale block in which bitumen and bound water peak at a short T2 and the oil in the organic pores peaks through a T2 of between 1ms and 20 ms. In this case, no fluid is present in the inorganic pores or natural fractures. This shows that pressure drop during core retrieval can result in large fractions of producible light oil and water escaping.

Fig. 6 shows data from a re-saturated shale sample in which crude oil signal in inorganic and organic pores is significantly increased compared to the natural sample. This shows T ₁/T ₂Such as how to achieve separation of different fluid components from the environment. In fact, in the case of the oil-re-saturated dense oil shale of fig. 6, the increase of oil in the organic (kerogen) pores can be clearly seen. In addition, at longer T2 values (C>50ms) also exists, corresponding to the oil re-saturating in the organic pores and natural fractures.

NMR T1-T2 Experimental methods

As noted above with reference to FIG. 2, the present disclosure describes a method for estimating properties of subsurface geology from NMR T1-T2 well log measurements. The method comprises the following steps:

(1) acquiring logging data sensitive to T1 and T2 relaxation time distributions of formation fluids using a downhole NMR logging tool;

(2) estimating a T1-T2 map from the logging data using an inversion method that accounts for effects of logging tool motion; and

(3) subsurface properties were estimated from T1-T2 maps.

The T1 and T2 data contain information about the molecular motion of the fluid. The T2 relaxation time is dominated by low frequency molecular motion, while T1 is dominated by fast molecular motion driven by larmor frequency fluctuations. Thus, simultaneous measurement of the T1 and T2 relaxation time distributions may provide information about the formation fluid type and its volume. Additionally, formation properties such as porosity, permeability may also be estimated.

NMR logging measurements are acquired using a specially designed data acquisition scheme (called a pulse sequence) that describes the timing of transmission and reception of electromagnetic signals. The pulse sequence used to measure the T2 relaxation time distribution is called the CPMG echo train and is shown in fig. 7. The CPMG echo train includes an initial idle time or wait time 306 that may be appropriate for nuclei in the formation fluid to reach equilibrium with the magnetic field induced by the permanent magnets of the tool. Then, a series of radio frequency pulses (e.g., first pulse 308B) are applied using the antenna ₀Followed by two pulses 310 and 312). In the middle between the two RF pulses, an NMR signal called echo 314 may be formed, which may be measured using a suitable device (e.g., via an antenna). The amplitude of the echo 314 decays or diminishes over time. By fitting the echo amplitudes to a multi-exponential model, a T2 distribution can be obtained. The time between adjacent 180 degree pulses is the echo interval (TE). The initial latency (WT) is typically long enough to fully polarize the system.

In such embodiments, such as those described above, a chain of echo signals is acquired. The signal amplitude S is measured as the echo time t _echo(Return from the first 90 degree pulseWave time) of the wave to be generated,

t _echo＝n*TE (5)

where n is the number of echoes and TE is the echo spacing (time between two adjacent 180 degree pulses such as 310 and 312). The signal amplitude then follows an exponential decay pattern, which can be expressed as:

for the sample with a single T2. For many samples where a range of T2 exists, the total signal is the sum of all T2 components,

where f (T2) is the T2 distribution function.

Measurement of the T1-T2 relaxation time distribution is achieved by acquiring a set (e.g., a group, a plurality, etc.) of CPMG echo chains with varying latencies, as shown in fig. 8. FIG. 8 shows a schematic 316 (e.g., timing diagram) of pulse sequences 318, 320, 324, and 326 for simultaneous T1-T2 measurements. In the equation, the symbol t is the latency WT. As the latency WT increases, the amplitude of the echo increases according to the T1 relaxation time of the fluid.

In such an embodiment as shown in fig. 8, the signal growth (e.g., single line 328) may be represented by the following equation, which is a function of the latency WT:

assume that the initial signal is zero at zero WT. The relevant relaxation involved in this section is T1, the spin-lattice relaxation time. Therefore, WT and echo time t _echoThe signal dependence of (a) is:

by fitting the data to a two-dimensional exponential model, a simultaneous T1-T2 distribution f (T1, T2) can be obtained. Such as Song, Y. -Q. (2013), Magnetic Resonance of Ports Media (MRPM): the inversion is described in A permanent. journal of magnetic Resonance,229, 12-24.

As one of ordinary skill in the art will appreciate, one important parameter in the design of NMR pulse sequences is logging speed, which refers to the speed at which a logging tool traverses a wellbore. Higher logging speeds save rig time substantially and reduce the risk of logging tools getting stuck in the wellbore or slipping. The quality of measurement (resolution) increases as the number of echo trains in the set increases, and the total time required to acquire the set of CPMG echo trains for each depth T1-T2 measurement indicates the logging speed. The shorter the acquisition time, the higher the logging speed at which the same vertical resolution data can be acquired. Thus, the design of the pulse sequence may involve a trade-off between resolution and logging speed. To improve data quality without sacrificing logging speed, the method disclosed herein involves acquiring several echo trains with short latency (e.g., less than 100 ms). This data acquisition scheme provides several advantages. For example, short latency shortens the overall data acquisition time, resulting in higher logging speeds. In addition, echo trains with short latencies may be repeated in rapid succession to improve the signal-to-noise ratio (SNR) of the measurements. High SNR improves the resolution of features obtained from the data. In addition, by acquiring echo chains with short latency, the resolution of short T1 and T2 relaxation times can be greatly enhanced. The enhanced resolution of short T1 and T2 relaxation times is valuable for characterizing fluids in shale formations containing very small sized pores. In one embodiment, the pulse sequence for simultaneous measurement of T1-T2 relaxation times involves a set of 6 CPMG echo trains. The log latency interval for the first 5 echo trains is between 1ms and 100 ms. The latency of the 6 th echo train is chosen to be long enough to ensure that the nuclei are almost completely polarized by the magnetic field, thereby providing an accurate porosity measurement. Examples of pulse sequence parameters are shown in table 1.

TABLE 1-examples of sequence parameters for T1-T2 measurements

As will be appreciated by one of ordinary skill in the art, two-dimensional (2D) or multi-dimensional (MD) maps are not limited to T1-T2, but may include diffusion coefficient-T2, T1-T2-D, diffusion coefficient-diffusion coefficient correlations. For example, FIG. 9 is a diffusion coefficient-T2 map 330 according to an exemplary response shown in a gas region. FIG. 10 is a diffusion coefficient-T2 map 332 illustrating an exemplary response in a water zone. Specifically, the diffusion coefficient-T2 map 330 illustrates a gas profile 331 and a water profile 334. While the diffusion coefficient-T2 map 332 does not show the gas profile 331, it shows the water profile 333. In addition, diffusion coefficient- T2 maps 330 and 332 include lines 335 and 336, which represent approximate regions of water and gas, respectively.

One embodiment of the present disclosure is directed to more efficiently obtaining 2D or multi-dimensional (MD) measurements. In some 2D or MD experiments, more scans than necessary may be performed to obtain a data set. Due to the multiple scans, the downhole NMR tool 12 may remain in approximately the same location in the formation 14, which reduces logging speed. Additionally, movement of the downhole NMR tool 12 before and during data acquisition may affect the response, and therefore the acquired data. The present disclosure provides systems and methods that can reduce the number of scans to improve the logging speed of MD experiments.

In some MD experiments, movement of the tool before and during data acquisition can affect the response, and therefore the data acquired. Such movement may change the signal equation away from equation 9. To address this problem, the motion effects may be analyzed to identify a corrected kernel function.

The signal amplitude can be readily obtained in a homogeneous field and at zero logging speed. Assuming that the sample has reached a thermal equilibrium magnetization M _oApplication of the CPMG sequence will produce a transverse magnetization signal given by:

here, k _T1T2(t _echoWT) is a description of the measurement versus the relaxation time T ₁And T ₂A kernel of known sensitivity of f (T), and ₁,T ₂) Is the two-dimensional distribution function of interest. As described above, T ₁-T ₂A standard implementation of NMR measurements consists of a series of CPMG sequences separated by a wait time WT. For a static sample in a homogeneous field, then the kernel is given by:

to determine the distribution function f (T) ₁,T ₂) For different parameters t _echoAnd the WT collects a set of measurements. Based on equation 10, this data is inverted to extract f (T) ₁,T ₂). For the kernel given in equation 11, then the inversion algorithm is essentially an inverse laplacian transform. Note that the kernel has a simple separable form: the first term depends on WT and T ₁And the second term depends on t _echoAnd T ₂. The details of the inversion will be discussed in detail below.

When these measurements are performed with a mobile logging tool, two effects modify the standard kernel given in equation 11: (i) heterogeneity of a magnetic field applied to the sample, and (ii) relative motion between the logging tool and the sample. At a given time, a small portion of the sample is at resonance and undergoes a perfect pulse. Therefore, the off-resonance effect and the time dependence are important factors and influence the spin dynamics. The modified kernel may be more complex than equation 11, as it may depend on the logging speed and the characteristics of the logging tool; in particular, on the field profile of the static magnetic field along the tool and the RF magnetic field in the sensitive region. In general, the kernel no longer has the simple separable form of equation 11. To determine the most general case of kernels, numerical simulations can be used to accurately determineA core of a logging tool. After integration of Bloch Equations for the relevant relaxation times, latency times, echo times, and logging speeds, the results may be parameterized by a number of dimensionless parameters to obtain a useful version of the modified kernel. Some dimensionless parameters include the quantity WT/T ₁、v T ₁/L _detAnd L _prepol/L _det. Here, L _detIs the length of the sensitive region of the NMR detector, and L _prepolIs the length of the magnet segment used to polarize the sample before the sensitive zone.

A simple field contour such as that shown in fig. 11 may be considered to identify the kernel. The effect of inhomogeneity of the magnetic field in the direction perpendicular to the tool motion on the T1-T2 kernel can be mitigated by using appropriate fragmentation pulses applied at the end of the CPMG sequence. Moreover, as shown in the simplified logging tool schematic of FIG. 11, the RF field may be confined in the detector region 337 at a uniform magnitude along its length, and the magnetic field (e.g., indicated by line 340) is generally uniform along axis 338 in the detector region 337. As shown, the magnetic field 340 may be non-uniform in the pre-polarized region 342, indicated by the bump 344. The following discussion may be based on an understanding that such crusher pulses have been appended to the CPMG sequence.

For this case, the modified kernel for the mobile tool has the following structure:

term k ₁(WT,v；T ₁) And k ₂(WT,t _echo,v；T ₁) With the longitudinal magnetization profile M in the detector section at the beginning of the CPMG sequence _z(z；v,WT,T ₁) The following steps are involved:

here, z is the formation coordinate along the tool axis and direction of motion. The last term k3 (t) _echo,T ₂)＝exp{-t _ech0/T ₂Is the same as the last term in the standard kernel equation 11.

In this model, the longitudinal magnetization in the detector section at the beginning of the CPMG sequence is given by:

for the simple field profile shown in FIG. 11, the modified kernel is obtained analytically. The first part of the kernel determines the initial amplitude of the CPMG signal and passes through the dimensionless ratio T ₁(v WT/L) _det) It depends on the latency, logging speed and longitudinal relaxation. In FIG. 12, k is shown ₁(WT,v；T ₁) The result of (1).

Second part k of the kernel ₂The enhanced signal attenuation of the CPMG chain due to motion is described. FIG. 13 shows k for the model of FIG. 11 ₁And k is ₂The result of the multiplication. It shows that the enhanced signal attenuation depends not only on the logging speed and echo time, but also on T ₁And a latency WT. To robustly extract T from well log data ₁-T ₂Distribution, the term (l-exp-TW/T) in equation 9 can be replaced by a more complex function as shown in FIG. 13 ₁H) for inversion. The results of fig. 12 and 13 are illustrative examples. The exact kernel of an actual logging tool may be similar in quality to these results, but for quantitative results it may involve determining the kernel specific to a given downhole NMR tool 12 (e.g., logging tool). This may be done by numerical calculations based on known field profiles or by extensive calibration measurements with a moving downhole NMR tool 12. Usually, the exact T ₁-T ₂The determination of the kernel may be used to calibrate the downhole NMR tool 12.

Regularization based inversion

There are many ways to perform data inversion to obtain 2D and MD maps (or distributions), some of which are reviewed in Song, y. -q. (2013) Magnetic Response of Ports Media (MRPM), a permanent. journal of Magnetic response, 229,12-24, the contents of which are incorporated herein by reference in their entirety as examples, the present disclosure will be based on Song, y. -Q., venkataraman, l., H ü rliman, m.d., Flaum, m., frolla, p., & linear, C. (2002) T (1) -T (2) corrected using aft-dimensional laplanproduct, journal of Magnetic, 261, 154, the contents of which are incorporated herein by reference in their entirety as if the methods were incorporated by reference, 268, below.

The general inversion can be described as follows. Given a data set M measured under a series of parameters (e.g., tau), the goal is to determine the distribution function F such that M is K F within the statistical range of data noise. For T ₂An example of a measurement, multiple data points are obtained at different echo times tech (or tau 2). The distribution function is denoted as T ₂I.e. a function of the spin-spin relaxation time. T is ₁Discretization in matrix form can be performed, for example, from 0.001s to 10 s. This equation can be approximated in the form of a discretized matrix as follows:

M＝KF， (16)

where M is the data vector, K is called the kernel matrix, and F is the distribution vector, respectively. Only M and K are known. The positive solution F should satisfy the above equation M-K F < sigma, where M-K F is the vector norm and sigma is the noise variance. Given a limited SNR, many solutions satisfy this criterion, and this is the root cause of the nature of the laplace inversion pathology.

In general, the regularization method obtains a fit to the data by minimizing the following expression:

||M-K F|| ²+alpha||F|| ²， (17)

the difference between the first measurement and the fitted KF. The second term is the Tikhonov regularization term and its magnitude is controlled by the parameter alpha. The role of this regularization term is to select a small 2-norm F ²And thus a smooth and less peaky solution is selected. However, this may cause a deviation in the results. When alpha is chosen such that the two items are comparable, the deviation is considered to be minimal, anAnd the results are stable in the presence of noise.

The regularization method described above is also applicable to 2D inversion. The key difference is that the distribution function F is now a function of two variables, for example, the variable may be T ₁And T ₂Or T ₂And D. Of course, this can be extended to 3D or higher dimensions.

Thus, in the case of the T1-T2 experiments, the resulting distribution is a 2D map in the sense that the distribution function is defined on a two-dimensional grid of T1 and T2. Each variable (T1 or T2) may take any value within a range. For example, in certain T1-T2 experiments, T1 and T2 ranged from 0.001 to 10 s. As discussed herein, the mask 346 may be applied to the map to reduce computation time. FIG. 14 shows mask 346a filling a rectangular area in the space T1-T2. In some embodiments, a portion of the T1-T2 map is suitable for determining a property of a subterranean formation. In such embodiments, a rectangular mask 346a may be suitable. However, in other embodiments, a more complex mask 346 or multiple masks may be employed. In some embodiments, the mask may be defined by a previous measurement or by an operator during a measurement based on one or more NMR measurements.

As previously discussed, many solutions satisfy this criterion (equation 16), and this is the root cause of the nature of Laplace inversion morbidity. One of the sources of inversion pathophysiology is that the distribution F (whether one-dimensional (1D) or 2D) has many elements. For example, for a T1-T2 map of 100 points along each dimension, there are 10,000 independent elements of F. One way to improve the inversion is to reduce the total number of F elements in the inversion. In particular, when a priori knowledge of the sample under study indicates a smaller range of parameters, then it is better to size the profile by reducing the maximum T1 or T2 to match the smaller range of T1 values, T2 values. However, this method of reducing the profile is limiting and may not accurately utilize knowledge of the sample.

Equation (16) can be used for 2D inversion by the following method. The 2D distribution function F (T1, T2) may be shown in matrix form: different columns (second index) are used for different values of T2 and different rows (first index) are used for T1, as shown in the 3 x 3 example below:

by rearranging the elements, this matrix can be rewritten as a column vector

Correspondingly, data in a 2D experiment may be measured as a function of more than one variable, such as shown in fig. 5, which may be written into a 2D matrix or column vector. Thus, each element of the data vector M corresponds to a pair of experimental parameters, e.g., WT and t _echo(or tau 2).

Once the data and distributions are represented in vector form, a kernel matrix can be formulated:

K _p，q＝K(WT _p，tau2 _p，T1 _q，T2 _q)， (20)

where p and q are indices of the kernel matrix, and WT _pAnd tau2 _pWT and tau2 values for the p-th data point in M, and T1 _q、T2 _qIs that

The value of the q-th element in (1). Thus, a 2D problem can be converted to a 1D problem and the 1D algorithm used directly for the inversion. Here, the kernel (equation 20) is an example. More complex kernels may be used, such as including the velocity effect (equations 12-14). The above examples for the T1-T2 measurements are intended to describe one of many parameterizations that may be used to perform the inversion. Other parameterizations may also be used. For example, T2 and the ratio of T1 to T2 may be used as two arguments.

Full spectrum versus partial spectrum

As previously discussed, a map that can take any value of a variable can be considered a full map spectrum (e.g., as shown in fig. 14). The mask can be represented by a rectangular (or square) matrix, and the inversion is typically performed for this full spectrum. An advantage of putting this matrix into its 1D format is that a subset of the matrix elements can be selected for inversion. One example of a mask 346b may be shown in fig. 15, where matrix elements below the T1 ═ T2 line are excluded from the inversion, and elements with T1 greater than or equal to T2 are used for the inversion.

Another exemplary mask 346c is shown in fig. 16. In some embodiments, if the following a priori knowledge is present (e.g., by other measurements): a particular sample does not have any signal in the region of T1 (0.1 to 1s) and T2 (0.01 to 0.1s) (marked by white rectangles), and so can be excluded

And thus reduce the total number of elements in the inversion.

Another example of a mask 346d is shown in fig. 17. In this example, the solid regions are allowed regions (e.g., other values in the graph are not set to zero or substantially zero). The vector elements of the F-bars in the blue region are used for inversion. For shale and tight oil formations, different fluids are manifested in different regions of the T1-T2 pattern. For example, light oil and water in large pores will exhibit relatively long T1 and T2 (greater than 0.01s), and a T1/T2 ratio close to 1. On the other hand, pitch and kerogen signals show T1 and T2 below 0.01s, and can show very large T1/T2 ratios, up to hundreds at low magnetic fields, and even higher at high magnetic fields. Thus, the total elements used for inversion can be greatly reduced.

In other examples, such as D-T2, it may also be useful to define a partial pattern mask based on the properties of the survey sample. In other words, rather than defining a particular region, mask 346 may define a plurality of portions within the atlas. For example, the mask 346e for the D-T2 map shown in FIG. 18 covers several different portions of D-T2. The excluded portion may be selected based on the portion of the atlas that does not normally have the signal of interest. For example, a priori knowledge of the formation may indicate that the formation contains quantities of methane gas, oil, and water that will provide the appropriate amount of signal to be detected. These components (for example,methane gas, oil, and water) exhibit unique behavior in the D-T2 spectrum. For example, depending on the formation temperature, the diffusion constant of water is (1-5)10 ^-9m ²In the/s range and the NMR signal of the oil may show a strong correlation of D with T2. As such, the mask 346 may selectively include signals from oil, water, and gas, or any combination thereof, while excluding other portions based on such information.

Such a mode may be obtained from modeling of fluid behavior (including surface relaxation, limiting diffusion, scaling behavior of hydrocarbons, etc.), or it may be obtained from empirical considerations for a particular sample. In the case of well logging, some information about the formation and downhole fluids (crude oil, mud, water, etc.) of the well, formation or zone (basin) may be known prior to the logging experiment. For example, if the formation is known to be out of gas, the signal region corresponding to the gas may be removed from the mask to further reduce the inversion map, thereby making the calculation of the inversion easier.

These map masks highlight a few regions in the T1-T2 map (or other map of the D-T2 or MD experiment) where the elements of the T1T2 distribution are allowed to be non-zero in the inversion. Elements outside the mask map are set to substantially zero and will therefore not participate in the inversion calculation. Using equations 18 and 19 as an example, assuming F _12 and F _13 are not in the mask and are therefore excluded from the inversion, then the new distribution F _m(mask distribution) is:

and then the corresponding 1D form

Is composed of

The graph can be masked (F) _mask) Defined as the same size as matrix F, the allowed element values are 1, and the excluded elements are 0. Can be used for dredgingF and F _maskElement-by-element multiplication to obtain a mask distribution,

F _m＝F*F _mask, (23)

where "+" indicates element-by-element multiplication.

These mask regions may be rectangular, square, circular, or any other shape, and may contain one or more dots. These regions may be interconnected or disconnected. That is, one mask may be continuous or discontinuous, and further may cover multiple discrete regions within the pattern. The regions may be defined by mathematical equations (such as Tl >0.1, T2<1), or they may be drawn manually similar to drawing software. In addition, the pattern mask may be selected based on other data. For example, a pattern mask may be derived from the T1T2 pattern of an oil shale sample (fig. 3) by selecting (or allowing) those elements whose values are greater than some threshold (e.g., 1%, 2%, or 5%) of the maximum signal of the T1T2 pattern.

The spectral mask may be used in combination with NMR acquisition parameters based on knowledge of the mask. That is, in certain embodiments that use a defined mask for the data, the NMR acquisition parameters selected for logging may depend on the defined mask. Using the T1-T2 experiment as an example, a mask 346- (1) comprising multiple regions, one with a highly correlated T1T2(T2>0.1s), and the other (2) with a large T1, T2 range (T1 range 0.001 to 0.1, and T2 range 0.001 to 0.1s) can be achieved. Since the signals in region 1 show correlated T1 and T2, measurement of T2 by the CPMG chain is sufficient to define it. However, for the second region, it is appropriate to use a more independent T1 measurement. Therefore, the design of the experiment does not necessarily use the conventional parameters outlined in table 1. In contrast, the WT may focus on the second zone where Tl <0.1s, and therefore the WT is shorter and logging is faster.

TABLE 2 WT and echo spacing parameters for T1T2 experiments

The techniques of this disclosure may not use as many WTs and thus speed up measurements. This is particularly important for logging experiments to reduce overall experimental time and to increase logging speed. In an actual NMR logging experiment, many parameters may be changed, such as the echo interval, number of repetitions, and number of echoes acquired for each WT. Table 3 is an exemplary pulse sequence for the downhole NMR tool 12.

Pulse sequence parameters	short-T 1Sequence of
		Number of measurements	6
Waiting time (ms)	1、3、10、30、100、3000
		Echo interval (ms)	0.28、0.28、0.2、0.2、0.2、0.2
Number of repetitions	50、50、30、10、4、1
		Number of echoes	20、20、50、100、300、1800

TABLE 3T 1-T2 pulse sequence parameters for downhole NMR tools

FIG. 19 is an example of a well log 160 that can provide a visualization of properties obtained using NMR measurements obtained in a rapid manner according to the systems and methods described above. The log 160 includes four traces: 162. 164, 166 and 168. The first trace 164 represents feet inThe well depth of the bit. The second trace 162 includes Total Organic Carbon (TOC)170 and a measurement of its uncertainty (TOC) _S1G)172. The third trace 164 includes a density porosity 174 and an NMR porosity (MRP) 176. The fourth trace 166 includes a volume fraction 178 of light hydrocarbons, a volume fraction 180 of bitumen, a volume fraction 182 of kerogen, and a volume fraction 184 of water. By presenting the identified underlying features in a visualization such as this, the operator may be able to effectively make decisions regarding the management and/or operation of the well.

Fig. 20 provides an exemplary log 350 that may include a plurality of traces including RPI values determined based on NMR measurements quickly obtained according to the present disclosure. The log 350 includes several traces 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, and 372. These traces are intended to represent the type of information that may be present in the log, and are not intended to be exhaustive. In fact, more or fewer traces may be present in any actual well log developed in a workflow according to the present disclosure. Returning to the example log 350 of FIG. 15, the trace may present the following information:

trace 352: a depth trace.

Trace 354: according to 2D NMR T ₁-T ₂Logging by T ₂LM and T of 3.0ms ₂T derived from the cut-off value ₂Distributed to separate bound pores from available pores.

Trace 356: according to 2D NMR T ₁-T ₂Logging by T ₁T derived from LM ₁And (4) distribution.

Trace 358: porosity from 2D NMR logs was compared to porosity from core data.

Trace 360: mineralogy and fluid volume results from formation evaluation using spectroscopy and 2D NMR logging.

Trace 362: 2D NMR T's using the cut-off values shown in FIG. 3(c) ₁-T ₂And (4) logging the porosity of the fluid.

Trace 364: clay bound water porosity from 2D NMR logging compared to porosity from core data.

Trace 366: tethered hydrocarbon porosity from 2D NMR logging compared to porosity from core data.

Trace 368: use of a T of 3.0ms compared to porosity from core data ₂Cutoff is based on the effective porosity obtained from 2D NMR logs.

Trace 370: the effective water porosity from the 2D NMR log is compared to the effective water porosity calculated from the resistivity.

Trace 372: the RPI 202 (line) calculated from the workflow 200 is compared to the hydrocarbon-producible carbon weight fraction 374 (point) calculated from the core data.

Indeed, as can be seen from trace 372, RPI 202 calculated using multi-dimensional NMR measurements correlates well with core sample measurements based on carbon weight fraction 374. This indicates that RPI 202 may be used as a highly valuable addition or replacement to the core sample because RPI 202 may be calculated using downhole measurements that may more accurately capture the state of downhole fluids in the downhole environment. By generating and outputting the RPI 202 onto a log (such as log 350), an operator or other decision-maker may more efficiently make production and production decisions appropriate for the conditions of the geological formation 14.

The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.