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To date, nuclear magnetic resonance (NMR)-based formation evaluation interpretation models are developed and/or calibrated with laboratory core analysis conducted at ambient conditions, partially because high temperature laboratory NMR measurements are limited by instrument capabilities and also costly. Currently, the underlying assumption is that the difference between NMR relaxation time distributions measured at ambient conditions and those obtained from in-situ logs is negligible. While this may be true for reservoirs with major contrasts in their NMR relaxation properties, the environmental effect needs to be accurately described and properly corrected in complex carbonate reservoirs where NMR logs must detect subtle pore size variations with high sensitivity.
This paper describes data analytics approaches to derive a temperature correction model for T2 distribution data using laboratory NMR core measurements at four temperatures. Even though, ideally, a data analytics approach requires a great number of experiments, the simple method employed for the carbonate field in this study works well with a limited number of samples (28). With the temperature correction models, the laboratory developed NMR-based interpretation models can be adjusted to in-situ reservoir temperatures, thereby applicable to NMR logging interpretations. To the knowledge of the authors, this is the first systematic attempt in the industry to develop a spectral NMR temperature correction model for in-situ carbonate formation interpretation.
The temperature dependence of NMR relaxation times of fluids in porous media remains an unsolved challenge. Theoretical and experimental NMR studies in carbonates (Godfrey, et al, 2001a; Godfrey, et al, 2001b; Straley, 2002; Kwak et al., 2016) identified several mechanisms leading to different, if not opposite, temperature dependencies. In complex pore systems, such as carbonate rocks, the overall temperature dependence is likely governed by multiple underlying mechanisms with no straightforward first principle-based description. Therefore, no comprehensive temperature correlations of key NMR logging deliverables, such as the distributions and geometric means of T1 or T2, are available.
We address several practical and common issues regarding applying machine learning (ML) methods for formation evaluation with logging data. It is normal that the available training data are far from “big” and require using more general ML algorithms such as neural network, thus the selection of ML algorithm becomes important. Second, laboratory core measurements are usually used as training data, but the applications are for logging data. The discrepancy between the two may involve instrument limitations, environment, and/or fluid states inside the pores. These differences should be taken into account to make the model work better. Furthermore, physical constraints can be applied for those petrophysical parameters that are intrinsically correlated but could be predicted optimistically with different ML algorithms for individual parameters to obtain self-consistent, robust petrophysical parameter sets.
Carbonate lithology is known to be complex resulting in highly heterogeneous pore systems. Primary and secondary pores commonly coexist at the same depth, and the various degrees of post-deposition diagenesis processes have progressed, which results in poor performances from many conventional carbonate log interpretation models. The traditional model development approach of forward modeling and inversion is fundamentally challenging for carbonates due to the difficulties in modeling the tool response to the pore system heterogeneities.
For Middle East carbonate reservoirs, pore typing, permeability, and pore throat size distribution are the key factors that are frequently used to assess reservoir quality, to select the perforation points, and for designing production strategies. Nuclear magnetic resonance (NMR) logging, in principle, may respond to all these parameters with different degrees of difficulty; empirical correlations have been used that work better for certain wells but not as well for others, again because of the range of heterogeneities which has not been addressed in some of the empirical equations.
To illustrate the significance of heterogeneity, the histogram of a simple, commonly used macro and micro pore cutoff values model is shown in Figure 1.
Capillary pressure is a crucial step in reservoir properties definition and distribution during static and dynamic modelling. It is a key input into saturation height modelling (SHM) process, understanding the fluid distribution and into reservoir rock typing process. Capillary pressure models provide an insight into field dynamic for the identification of swept zones and provide another calibration besides the log calculated saturation. Capillary pressure curve tends to be more complex in carbonates in comparison to sandstone reservoirs because of post deposition processes that impact the rock flow properties, hence complex pore throat size distribution (uni-modal, bi-modal or tri-modal). Therefore, accurate determination of this property is the cornerstone in the reservoir characterization process.
Capillary pressure can be obtained using several experimental techniques, such as mercury injection (MICP), centrifuge (CF) and porous plate (PP). Each method has its own inherited advantages and disadvantages. The MICP method tends to be faster, cheaper and provides a full spectrum of pore throat size of a plug. Whereas, the PP method can be carried out at reservoir conditions with minimum required corrections.
In this paper, a detailed workflow for quality control capillary pressure is discussed. The workflow is sub-divided into three main parts: Instrumental and experimental level, core measurement level and logs level. Experimental level starts with proper designing the actual procedure of the capillary pressure experiment. Parameters such as pore volume, bulk volume and grain density are investigated at core measurement level. In geological-petrography montage, all petrography data; X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM), thin section and computed tomography scan (CT) are used along with the capillary pressure curve for assessment. Comparing various methodologies of experimental technique carried out on twin plugs, if exist, are also investigated. The capillary pressure that passes the previous QC steps is used as input into saturation-point comparison as a logs level QC. The saturation calculated from capillary pressure is compared to log-derived water saturation eliminating any issues with porosity and permeability of the trims and provides insight to the uncertainty level in the model. As an additional step, the MICP measurements are fitted with bi-modal Gaussian basis functions with two practical benefits. First, the quality of this fitting is a useful indicator for the evaluation of pore structure complexity and the identification erroneous measurements. Second, the fitting parameters are useful inputs for geological interpretation, rock typing and SHM. This rapid and automated workflow is a useful tool for screening, processing and integration of large-scale capillary pressure data sets, a key step in integrated reservoir description, characterization and modelling.
Unconventional tight reservoir sands have low porosity and very low permeability (mostly less than 0.1mD) due to their fine grain size and poor grain sorting that is often exacerbated by extensive diagenetic effects such as cementation and compaction. Petrophysical evaluation in these formations is very challenging. Conventional downhole logs such as density, neutron, sonic, gamma ray and resistivity measurements provide limited information on pore size variations and often missed Key geological features especially at the early stages of reservoir development. Fluid characterization at the earliest possible stage is paramount to guide the development of these reservoirs where tight well spacing, stimulation (fracturing) and or horizontal well completion is usually required. The main objective of this paper is to show a process of fluid characterization in unconventional tight sand that guides reservoir stimulation.
Porosity partitioning using nuclear magnetic resonance (NMR) logging data helps address these challenges in three distinct steps. First, the 1-dimensional (1D) NMR T2 spectrum quantifies the amount of bound and free fluids pore space and reveals reservoir quality with unique sensitivity. In this step, the NMR fluid substitution method was utilized to ensure consistency between NMR logs in oil-based mud (OBM) and water-based mud (WBM) systems. Second, the free fluids are further subdivided into hydrocarbon and water phases using a 2-dimensional (2D) NMR T1/T2 processing technique. Third, the hydrocarbon phase is subdivided again into liquid and gas phases where a gas flag is turned on whenever the NMR gas signal significantly exceeds measurement uncertainty. This enables detection of live hydrocarbons with high gas-oil ratio (GOR).
This paper presents the integration of NMR analysis into petrophysical evaluation of an unconventional tight sand reservoir. The evaluation helped optimize the best interval for stimulation. Fluid sample acquired with formation tester correlated very well with NMR log-based fluid prediction.
Integrated NMR analysis, including bound fluid vs. free fluid analysis and 2D NMR-based fluid characterization, including gas indicator flag, was applied to establish the presence and type of hydrocarbon in tight sands and select the best representative interval for stimulation. The continuous reservoir quality and fluid distribution profiles provided by these logs were beneficial for the geological understanding and complex formation testing operations in this challenging reservoir.
Formation evaluation studies suggest that high in the hydrocarbon column, the resistivity logs can precisely quantify fluid saturation due to the large contrast in the resistivities of hydrocarbon-bearing and water-bearing formations. However, in the transition zone where water and oil reside in more or less equal volumes, the determination of hydrocarbon saturation by resistivity value becomes challenging. Some of these intervals exhibit low resistivity pay (LRP) characteristics where resistivity-based log analysis predicts high water saturation, yet they can produce little or no water-cut.
Conventional log-based saturation and rock quality evaluation in a low permeability carbonate reservoir is difficult due to the lack of the input measurement's sensitivity to pore size and the amount of pore-filling fluids. Pore size information provided by Nuclear Magnetic Resonance (NMR) logs from this LRP provides good sensitivity, but it needs to be calibrated for quantitative use. The objective of this study is to determine a height-based NMR Bulk Volume Irreducible (HBVI) cutoff to distinguish and quantify the amounts of reservoir fluids across a wellbore using NMR logs.
The procedure consists of two-part workflow. The first part describes the acquisition of a data base that includes high-quality laboratory NMR and capillary pressure measurements to determine the pore aspect ratio and the effect of temperature on the formation's NMR properties using core samples from the target reservoir. These measurements are then used to underpin the mathematical description of the HBVI cutoff as a function of displacement pressure that is translated to height above the free water level (HAFWL). The second part of the workflow is a well-log processing scheme where the new formula is implemented to calculate a continuous fluid saturation profile across the well using NMR logs.
The laboratory measurements suggest a good agreement between the capillary pressure and NMR T2 measurements. Both data sets indicate a well sorted pore size distribution. The T2 relaxation time increases with temperature, which is then considered in the downhole implementation of the HBVI model. The NMR-based saturation log is consistent with wireline formation testing (WFT) observations and mercury injection capillary pressure (MICP)-based saturation height modeling results in a low resistivity pay reservoir.
The results of this study suggest that the laboratory calibration and NMR log processing workflows described herein provide a viable alternative for the calculation of fluid saturations in complex reservoirs where the conventional log-based saturation evaluation faces uncertainties.
This paper presents the first while-drilling acquisition of nuclear magnetic resonance (NMR) polarization buildup data in slim boreholes drilled in complex clastic and carbonate reservoirs. NMR logging data is paramount to the petrophysical evaluation of complex rocks such as silty sands, heterogeneous carbonates and reservoirs with variable hydrocarbon viscosity in many fields of the region. Fractionalized porosity obtained by NMR logs can discern bound fluids and free fluids, reveal otherwise hidden pore size variations and determine hydrocarbon composition and viscosity with unique sensitivity. The real-time availability of this valuable information from logging while drilling (LWD) measurements significantly improves drilling decisions to place the well into favorable zones. In addition, under some circumstances, it is safer to perform logging operations with sensors mounted on a bottom-hole assembly (BHA) than with pipe-conveyed wireline tools.
Most NMR logging tools, including wireline and LWD devices, record the transverse magnetization signal and its decay rate (T2), because of the simplicity and rapidity of the measurement. Other instruments observe the formation’s magnetization buildup rate (T1) upon exposure to a permanent magnetic field. While this acquisition mode is more time consuming, it requires less electrical power and data storage to obtain the same petrophysical information. The T1 measurement is insensitive to tool motion associated with drilling. The new tool discussed in this paper is the industry’s first LWD NMR sensor that performs T1 measurements in boreholes with diameters ranging from 5⅞” to 6¼”.
The verification of the new tool followed a three-step testing plan to ensure hardware integrity and data quality. The first testing step checked the consistency between while drilling and relog datasets, including the T1 spectra and the volumetric deliverables such as total and bound fluid porosity. Real-time logs were compared with post-processed memory data to evaluate downhole processing and data transmission capabilities. The second testing objective was to monitor the consistency among density, neutron and NMR porosities in known lithology (e.g., clean limestones) for the evaluation of tool calibration, activation and echo-level pre-processing. Finally, the new tool was run back to back with a wireline NMR logging tool with high-quality T1 logging capabilities to validate the accuracy of the LWD T1 spectrum and partial porosities.
The new tool is the latest addition to the industry’s LWD NMR technology. It was run in three wells with hole sizes of 6⅛” in three different fields. Two of the wells were drilled in carbonate reservoirs, whereas, the last test was conducted in sandstone. In the carbonate wells, real-time NMR logs provided pore size information in both limestones and dolomitic intervals and helped optimize subsequent formation testing operations for which results were in agreement with the logs. In the sandstone well, the tool revealed grain size variations and provided total porosity, bound water volume, and reservoir permeability. These were key inputs for petrophysical interpretation, model calibration, and completions design.
Nuclear magnetic resonance (NMR) logging has been frequently used in situ to detect and classify pore size for carbonate reservoirs by partitioning the T2 distribution spectra. However, there are a few challenges for measuring dolomite pore size with current carbonate NMR interpretation techniques. For wireline NMR logging, its T2 distributions are significantly shortened by the tool gradients; for logging while drilling (LWD) NMR logging, its T2 distributions can also be distorted because of the drilling-induced lateral vibration.
This paper discusses two solutions for identifying and quantifying macroporous dolomites for wireline and LWD NMR logging. For wireline NMR, an inversion-forward modeling-inversion (IFMI) technique computes both T2,int and T2,app distributions for any tool gradient and any inter-echo time. For LWD NMR, the best solution is to acquire a T1 log with a broadband saturation pulse followed by narrow-band excitation and refocusing pulses.
Both NMR carbonate interpretation methods are tested in a well from a complex carbonate reservoir where both wireline and LWD NMR logs were run back-to-back. Compared to a standard T2,app log, both methods increased sensitivity to macroporosity in dolomitic intervals.
The methods presented in this paper successfully reduced fluid diffusion effects to accentuate pore size variations particularly in macroporous dolomitic intervals. The diffusion-free NMR spectra will help pore typing and rock quality evaluation in carbonate reservoirs where large macropores or vugs are present.
Identifying macroporous or vuggy carbonates and quantifying their pore size is a key step to characterize certain carbonate reservoirs where the dolomitization and vug sizes vary from well to well and even depth to depth within a well. Even though comparing NMR porosity and apparent density porosity computed with the limestone matrix density can easily identify dolomite, in-situ dolomite pore size is only detectable with NMR logging.
Due to the shallow depth of investigation of logging tools such as Nuclear Magnetic Resonance (NMR), the signal interpretation of the flushed zone must be performed carefully. Understanding invasion effects on the logs is an important prerequisite for any petrophysical evaluation. While it is relatively easier to evaluate and correct for the effect of filtrate invasion in basic logs, such as triple combo, special care must be taken for advanced logging techniques such as NMR. For example, it is generally assumed that the volume of remaining wetting fluid in the flushed zone equals to the volume of micropores that do not contribute to flow when the well is produced. The amount of these immobile fluids is estimated using the NMR bound fluid log, a key input for the prediction of rock quality and well performance, especially in complex clastic and carbonate pore systems. In certain formations, NMR bound fluid logs exhibit some differences between adjacent wells drilled with oil-based (OBM) and water-based muds (WBM). This paper summarizes the lessons learnt from a laboratory NMR study of oil-based mud filtrate (OBMF) invasion as a function of rock mineralogy and microstructure, mud chemistry and displacement/flow pressure.
In this work, we studied the effect of a commercial surfactant usually added in OBM formulations. We investigated the effect of different surfactant concentrations on the fluid-fluid interfacial tension (IFT) properties and on the fluid-solid interaction properties, using contact angle measurements on both sandstone and carbonate model surfaces. Furthermore, we investigated the effect of the additive on the capillary pressure properties and remaining water saturations on sandstone and carbonate rocks. To maximize the generality of the results we used two very different driving mechanisms for the fluid displacement: centrifuge and flow-through.
The data showed that carbonate and clastic rocks behaved differently over a wide range of flow mechanisms and water saturations, proving that mineralogy plays a crucial role in the fluid displacement. Under the measurement uncertainties, the irreducible water saturation, however, remained constant regardless of the OBMF composition or driving mechanism.
We showed how sandstone and carbonate rocks behave in respect to wettability alteration due to a surfactant used in OBM formulations. The systematic difference, whatever the driving mechanism is, strongly suggests that the differences in NMR responses between sandstone and carbonate originate from chemical composition and surface properties rather than microstructural differences between sandstone and carbonate rocks.
We provide experimental findings proving that the NMR T1/T2 ratio of the oil phase in a mixed-saturated rock strongly correlates with wettability. NMR laboratory data acquired using refined oil (Soltrol) demonstrate a linearly decreasing trend between the T1/T2 ratio of the oil phase and the rock wettability measured by the USBM technique. For downhole applications, the main challenge is to separate the NMR signals of oil and water. To address this challenge, a two-step workflow is used. First, the NMR signal is separated into those from the oil phase and water phase using 2D diffusivity vs. T2 analysis where the overall T2 distributions of oil and water are determined. These distributions guide the subdivision of individual slices of a 3D D-T2-T1/T2 cube into water and oil areas and the calculation of their partial pore volumes. The T1/ T2 ratio for each fluid is calculated as an average T1/T2 weighted by its partial porosity at each T1/T2 slice. This T1/ T2 ratio is converted to rock wettability by the laboratory correlation. We also discuss data acquisition limitations and potential improvements of the workflow.
Wettability is one of a few critical reservoir properties that is fundamental to reservoir description and engineering, as exemplified by Morrow (1990). There are many established laboratory wettability testing methods (Amott, 1959; Donaldson et al., 1969; Ma et al., 1999) but each one has its inherent advantages and disadvantages. The biggest concern for any laboratory wettability test is that people are not sure how representative the testing samples are to the targeted reservoir, even with great effort in core preservation or wettability restoration (Ma and Amabeoku, 2014).
It was proposed that reservoir wettability may be characterized downhole using measurements, such as reservoir pressure profiling with a formation tester (Desbrandes and Gualdron, 1988). Its application has been rare, possibly due to the difficulties to reduce the noise around in-situ capillary pressure measurement caused by pressure gauges, mud properties, the extent of filtrate invasion, and all other issues related to pretest pressure measurements (Proett et al., 2015).
This paper presents a new methodology for performing a cutoff analysis that uses a T1/T2 ratio distribution obtained from two-dimensional (2D) nuclear magnetic resonance (NMR) T1 and T2 measurements. The ability to classify pores and their effect on permeability is noticeably improved compared to a T2-based approach, of which T2 cutoff values vary from a few tens of milliseconds to a few seconds. Based on mercury injection capillary pressure results, the T1/T2 ratio-based cutoff is used to differentiate porosity with a pore throat radius larger than 2 µm from smaller pore throats. The T1/T2 cutoff ranges narrowed to within 1.4 to 1.7 for 100% water-saturated carbonate cores. In addition, the empirical models of NMR-based permeability are enhanced by incorporating the porosity, T1/T2 ratio cutoff, and T2 geometric mean. For the studied data set of 49 carbonate rock samples with a permeability range spanning six orders of magnitude, an excellent correlation coefficient of R2 = 0.9 was observed between the NMR predicted permeability and that measured in the laboratory. This improved permeability prediction technique has the potential to be implemented in applications of downhole NMR logging.