Cements and shales play a vital role in the construction and energy sectors. Here, we use a set of advanced NMR methods to characterize the porous networks and dynamics of fluids in hydrated cement and shale samples. We compare the properties of cements from two different manufacturers, BASF and Portland, as well as shales brought from USA and China. 129Xe NMR spectra of xenon gas adsorbed in the samples indicate that the capillary mesopores are smaller and the exchange between free and confined gas is slower in the Portland than in the BASF cement samples. The pores probed by xenon in the shale samples from USA are significantly smaller than in the cement samples, partially in the micropore region. There is a substantial difference in between the 129Xe spectra of shales from USA and China. Whereas the latter show a clear signature of paramagnetic impurities by exhibiting large negative 129Xe chemical shifts (referenced to the free gas), the samples from USA lack the negative chemical shifts but feature large positive shift values, which may indicate the presence of micropores and/or paramagnetic defects. 1H NMR cryoporometry measurements using acetonitrile as probe liquid allowed the observation of mesopores in the shale samples as well, and T2-T2 relaxation exchange experiment enabled the quantification of the exchange rates between free and confined acetonitrile.
Nuclear magnetic resonance T1-T2 maps are popular for characterizing fluids in shale. The characterization, however, is often done manually, which is difficult for shale due to its complicated nature. This work investigates the clustering approach for fluid characterization on T1-T2 maps by comparing 6 different algorithms: k-means, Gaussian mixture model, spectral clustering, and 3 hierarchical methods. T1-T2 maps are collected on shale samples at as-received and dried conditions. We propose two cluster validity indices to select the optimal cluster number and best algorithm. Our results validate the capability of the two indices. Gaussian mixture model is found to be the best algorithm in most of the cases, as its fluid partitioning shows the highest consistency with theoretical fluid boundaries. In addition, 5 fluid components are identified from Gaussian mixture model, and their values are qualitatively validated by comparing with those in literature. Results also indicate that clustering is sensitive to the fluid distribution. Drying the sample producing better clustering by revealing the footprint of organic matter. This work provides a practical guide for applying cluster analysis in fluid characterization in Nuclear magnetic resonance T1-T2 maps.
Characterizing the microstructure of shales is challenging due to its extremely small scale, and generally involves manual interpretation of image data. Here, we present a completely automatic machine learning method for quantifying the preferential mineral-microfracture relationships in intact and deformed shales. This new method is innovative because it allows automated analysis of large volumes of image data, which is typically very time- and labor-intensive with existing techniques. Using automatic object detection algorithms, 225 images, including energy-dispersive X-ray spectroscopy images and backscattered electron images, were analyzed. These images were obtained from deformed and intact samples of a carbonate-rich organic shale and a siliceous organic shale. We quantitatively characterized the location and size of microfractures and their preferential association with particular minerals. The results show that compression created microfractures at the grain scale. More than 90% of microfractures developed within organic matter (OM), and that the microfractures tended to develop along grain/mineral boundaries. In addition, we found that the fabric of the rock plays an important role in microfracture generation, with laminated OM and clay tending to favor microfracture development while more massive minerals inhibited it. This quantitative analysis helps to improve understanding of the micromechanics of deformation during hydraulic fracturing. In addition, this approach is completely automatic, which could increase work efficiency and reduce the effects of subjective decision-making. The work presented here will greatly improve future studies of quantifying fracture and mineral properties, and can provide guidance for hydraulic fracturing and production strategies.
Paramagnetic and magnetic nanoparticles may be used as contrast agents in porous media to improve NMR measurements. In rocks, magnetic and paramagnetic cations in pore fluid may interact with nanoparticles and affect the magnetic resonance properties of the nanoparticles, which in turn makes interpretation of NMR measurements difficult if this effect is not accounted for. In this work, two groups of zirconia nanoparticles: one with positive surface charge, the other with negative surface charge; and a group of silica nanoparticles coated with poly(ethylene glycol) (PEG) and negatively charged were mixed with Fe(III) solution for interaction and/or adsorption to occur. T1 values of fluid with different concentrations of Fe(III) ranging from 9 mg/L to 50 mg/L were obtained, and we confirmed a linear relationship between Fe concentration and T1 values at fixed pH conditions. The T1 values of zirconia and silica nanoparticle dispersions before and after mixing with various Fe(III) solutions were measured and compared. Adsorption of iron onto zirconia nanoparticles was confirmed based on measurements of aqueous Fe remaining in supernatants. The response of silica nanoparticles mixed with Fe solutions was also consistent with adsorption of Fe ions on the silica nanoparticle surfaces. Adsorbed iron increases zirconia nanoparticles’ surface relaxivity by more than 50 fold, and the relaxation rate of zirconia nanoparticles increased with the amount of adsorbed Fe(III).
Natural gas hydrate may be buried with sediments until it is no longer stable at a given pressure and temperature, resulting in conversion of hydrate into free gas. This gas may migrate upward and recycle back into the hydrate stability zone to form hydrate. As of yet, however, no quantitative description of the methane recycling process has been developed using multiphase flow simulations to model burial-driven gas hydrate recycling. In this study, we present a series of 1D multiphase transport simulations to investigate the methane recycling process in detail. By invoking the effects of capillary phenomena on hydrate and gas formation in pores of varying size, we find that a free gas phase can migrate a significant distance above the bulk base of hydrate stability. Since the top of the free gas occurrence is often identified as the base of the hydrate stability zone from seismic data, our results demonstrate that not only could this assumption mischaracterize a hydrate system, but that under recycling conditions the highest hydrate saturations can occur beneath the top of the free gas occurrence. We show that the presence of pore size distributions requires a replacement zone through which hydrate saturations progressively decrease with depth and are replaced with free gas. This replacement zone works to buffer against significant gas buildup that could lead to fracturing of overlying sediments. This work provides a framework for simulating flow and transport of methane within the 3-phase stability zone from a mass conservation perspective.
The evolution of pores and fluids due to thermal effects is a key factor for predicting shale gas production. However, different fluid types and a wide range of pore sizes pose difficulties for characterization. We experimentally changed the fluid distribution and maturity of shales by pyrolysis on an Eagle Ford sample and a northern Rocky Mountains sample. Initial fluid conditions of shale samples were determined by NMR T1-T2 measurement. The samples were heated at 110°C, 250°C, 450°C, and 650°C, and T1-T2 measurements were performed after each level. The obtained T1 and T2 distributions were mapped to T1/T2 ratio (R) and secular relaxation time (Ts) for better characterization of different fluid distributions. Further, a difference index was used to quantify the overall distribution difference in R-Ts space.
According to the results, the Eagle Ford sample is dominated by an oil signal, whereas the northern Rocky Mountains sample has a mixture of oil, water and organic matter signal. Fluid volume decreases with increasing temperature. Heating at 110°C or 250°C reduces the fluid volume through the course of evaporation of water and hydrocarbon. The signal of OM is also revealed due to the fluid evaporation. Heating at 450°C and 650°C will alter the maturity of OM, resulting the change of distribution shape of T1-T2 due to change of pore structure. The thermal effects lead two samples to have a similar evolution pattern during thermal maturation.
Methane hydrates in fine‐grained continental slope sediments often occupy isolated depth intervals surrounded by hydrate‐free sediments. As they are not connected to deep gas sources, these hydrate deposits have been interpreted as sourced by in situ microbial methane. We investigate here the hypothesis that these isolated hydrate accumulations form preferentially in sediments deposited during Pleistocene glacial lowstands that contain relatively large amounts of labile particulate organic carbon, leading to enhanced microbial methanogenesis. To test this hypothesis, we apply an advection‐diffusion‐reaction model with a time‐dependent organic carbon deposition controlled by glacioeustatic sea level variations. In the model, hydrate forms in sediments with greater organic carbon content deposited during the penultimate glacial cycle (~120–240 ka). The model predictions match hydrate‐bearing intervals detected in three sites drilled on the northern Gulf of Mexico continental slope, supporting the hypothesis of hydrate formation driven by enhanced organic carbon burial during glacial lowstands.
The southern Alaskan margin offshore the St. Elias Mountains has experienced the highest recorded offshore sediment accumulation rates globally. Combined with high uplift rates, active convergence and extensive temperate glaciation, the margin provides a superb setting for evaluating competing influences of tectonic and surface processes on orogen development. We correlate results from Integrated Ocean Drilling Program (IODP) Expedition 341 Sites U1420 and U1421 with regional seismic data to determine the spatial and temporal evolution of the Pamplona Zone fold-thrust belt that forms the offshore St. Elias deformation front on the continental shelf. Our mapping shows that the pattern of active faulting changed from distributed across the shelf to localized away from the primary glacial depocenter over ∼300–780 kyrs, following an order-of-magnitude increase in sediment accumulation rates. Simple Coulomb stress calculations show that the suppression of faulting is partially controlled by the change in sediment accumulation rates which created a differential pore pressure regime between the underlying, faulted strata and the overlying, undeformed sediments.
Imaging tools are widely used in the petroleum industry to investigate structural features of reservoir rocks directly at multiple scales. Quantitative image analysis is often used to determine various rock properties, but it requires significant time and effort, particularly to analyze a large number of samples. Automated object detection represents a potential solution to this efficiency problem. This method uses computers to efficiently provide quantitative information for thousands of images. Automated fracture detection in scanning electron microscope (SEM) images is presented as an example to show the workflow of using advanced deep-learning tools for quantitative rock characterization. First, an automatic object-detection method is presented for fast identification and characterization of microfractures in shales. Using this approach, we analyzed 100 SEM images obtained from deformed and intact samples of a carbonate-rich shale and a siliceous shale with the goal of analyzing the abundance and characteristics of microfractures generated during hydraulic fracturing. Most of the fractures are detected with about 90% success rate relative to manual picking. Second, we obtained statistics of length and areal porosities of these fractures. The experimentally deformed samples had slightly more detectable microfractures (1.8 fractures/image on average compared to 1.6 fractures/image), and the microfractures induced by shear deformation tend to be short (<50 μm) in the Eagle Ford and long in the siliceous samples, presumably because of differences in rock fabric. In future work, this approach will be applied to characterize the shape and size of mineral grains and to analyze relationships between fractures and minerals.
Previous work on Pickering emulsions has shown that bromohexadecane-in-water emulsions (50% oil) stabilized with fumed and spherical particles modified with hexadecyl groups develop a noticeable zero shear elastic storage modulus (G′0) of 200 Pa and 9 Pa, respectively, while in just 50 mM NaCl. This high G′0 can be problematic for subsurface applications where brine salinities are higher and on the order of 600 mM NaCl. High reservoir salinity coupled with low formation pressure drops could prevent an emulsion with a high G′0 from propagating deep into formation. It is hypothesized that G′0 of an emulsion can be minimized by using sterically stabilized silica nanoparticles modified with the hydrophilic silane (3-glycidyloxypropyl)trimethoxysilane (glymo).
Leak-off tests (LOTs) are performed to determine the strength of a newly drilled formation below a cased interval and to characterize the upper bound of mud weight that can be safely used while drilling the next section, without risk of formation breakdown and lost circulation. In an LOT, drilling mud is pumped into the wellbore, causing the wellbore pressure to increase and exceed the formation pore pressure. During the initial LOT build up, excess pressure in the wellbore causes the surrounding rock to deform and mud filtrate to invade into the formation via porous flow. In this paper, change in formation resistivity around a wellbore during initial LOT build-up has been investigated. Invasion is modeled assuming two-phase radial Darcy flow and deformation using a 3D finite element model. Invasion may result in an exchange of conductive ions between water-based drilling mud and formation water both by diffusion in the direction favored by the concentration gradient of the ions and by convective transport. This process is incorporated into the model by solving the radial convection-diffusion equation for the aqueous phase using a finite difference method. Archie's law is used to determine the formation resistivity. Findings show that the direct effect of deformation on porosity, therefore on formation resistivity during an LOT, is negligibly small even when the formation rock is highly compressible with compressibility in the order of 10−3 psi−1. While salinity solely controls formation resistivity during an LOT conducted in a fully water-saturated interval, water saturation change and salinity change compete to produce a compound effect on formation resistivity of an oil-bearing zone where water saturation varies dynamically due to displacement of formation fluids. Unlike compressibility, the effect of permeability on formation resistivity response is found to be evident and readily observable. While analyzing the formation resistivity responses at various depths of investigation (DOIs), it is found that the effect of DOI on resistivity response can be useful in studying invasion and assessing formation damage during an LOT. In addition to this, through comparing time-lapse resistivity logs at multiple DOIs during an LOT with numerically synthesized resistivity responses, the model promises a novel approach towards determining the permeability of a freshly drilled and unaltered interval.
Characterizing the pore structure of shale is essential to understanding fluid transport through the matrix and optimizing any stimulation plan. Organic shales are heterogeneous at multiple scales, and the characteristic length scales or correlation lengths are often longer than the scale of samples used for laboratory analysis. Using laboratory data to make predictions at the wellbore scale therefore requires careful upscaling. Using samples of Barnett and Eagle Ford shales, and a siliceous, oil-bearing shale from the northern Rocky Mountains, we performed high- pressure mercury intrusion (HPMI) and low-pressure nitrogen sorption. We determined the properties of the pore network (size distribution, connectivity, spatial correlation) by constructing representative pore networks that allowed reconstruction of the HPMI and nitrogen sorption data. We then upscaled the results determining the correlation length with a percolation-based scaling function. Based on the HPMI and nitrogen sorption measurements, pore networks tend to be very poorly connected at the micron scale, with average coordination numbers between 2 and 3. Clusters of connected pores are typically a few hundred microns in size. Our work has significant implications for using laboratory measurements to predict reservoir properties. While samples are relatively homogeneous at the scale of SEM images or HPMI/nitrogen sorption measurements, organic-rich samples in particular have longer-range correlations that are not captured at this scale and yet exert significant control on transport properties. This will affect production from a fracture-stimulated well since induced microcracks and their interactions with the in situ pore structure are extremely important for moving hydrocarbons toward the main induced fracture system, as demonstrated by previous researchers. Multi-scale characterization is therefore necessary to gain a full understanding of the shale matrix.
Application of nanoparticles in the subsurface typically requires the use of surface coatings to maintain stability in dispersion and to provide particular functionality. However, the presence of surface coatings may hinder or mask properties of the bare nanoparticle cores, which may be a concern in nuclear magnetic resonance (NMR) applications. In this study, we used different amounts of 3-aminopropyltriethoxysilane (APTES) coating on Fe3O4 magnetic nanoparticles (A-MNPs). We measured the longitudinal relaxation time (T1) values of those A-MNPs suspensions, and computed and compared the surface relaxivities of A-MNPs with different amounts of APTES coating. Our results showed that when the mass percentage of APTES coating increased from 1.60 to 4.22 wt%, the A-MNPs’ surface relaxivity decreased by 26.1%. To determine the surface relaxation mechanism(s), we also used various volume fractions of D2O to dilute A-MNP dispersions to two concentrations: 0.01 and 0.002 g/L Fe. In the final mixtures, the volume fractions of D2O were fixed as 0-, 30-, 50-, and 70-vol%. The NMR measurements indicated that, at relatively high Fe concentration (0.01 g/L), electron-proton interaction dominates surface relaxation, and the hydrogen atoms in the APTES did not significantly alter the surface relaxation mechanism of the nanoparticles. At a lower Fe concentration (0.002 g/L), proton-proton relaxation, due to the APTES, also played a role in the overall relaxation mechanism on nanoparticle surfaces. A-MNPs with more APTES coating showed lower apparent surface relaxivities with higher D2O volume fractions in the mixture, indicating a greater amount of proton-proton relaxation on the nanoparticle surfaces.
In well logging applications, nuclear magnetic resonance (NMR) is recognized as a powerful tool to differentiate various fluids inside porous media. However, it can be challenging to do so in complex situations, e.g. when fluid peaks overlap with each other. Water-soluble contrast agents, such as MnCl2 or Gd-EDTA, have been proposed for use to accelerate the water relaxation, thus separating the water signal from those of other fluids. Together with these contrast agents, the log-inject-log method is used and the difference between the two logs is attributed to the doped water phase. This application only works with water-based mud. To extend its use to oil-based mud (OBM), it is desirable to find alternatives to water-soluble contrast agents that are compatible with OBM.
In this work, we introduce a new group of doping agents: oil-soluble contrast agents. We selected several iron-based complex compounds that are oil-soluble, and tested and evaluated their effects on oil signal relaxation using a laboratory NMR apparatus. We also tested hydrophobic iron oxide nanoparticles as a contrast agent. The results showed that both the complex compounds and nanoparticles were able to reduce the transverse relaxation time of oil from longer than 2 s to less than 20 ms. To demonstrate their applicability in porous media, we injected doped oil into gas-saturated Berea sandstone and limestone core plugs. With conventional 1D NMR measurements or the use of water-soluble contrast agents, it is not straightforward to discern the gas signal from the OBM signal. Our experiments showed that the gas signal could be easily identified in the presence of doped oil via simple T2 scans. We also performed experiments to demonstrate that the peaks of water and doped oil could be readily differentiated.
The use of oil-soluble doping agents can significantly enhance the contrast of the NMR signals originated from different formation fluids, thus facilitating the fluid typing process. It provides a key alternative to the current water-doping technique. It is particularly advantageous when changing the oil relaxation is desired, such as for eliminating signal interferences from OBM invasion and differentiating heavy oil from clay-bound water. It also provides the possibility of speeding up the logging process by dramatically reducing the oil relaxation time. In addition, they can be employed in the laboratory for various purposes such as water saturation determination and fluid displacement monitoring. These contrast agents can also be of useful when oil-soluble contrast agents are desired in other fields, such as medical imaging applications.
We tested how different emulsion characteristics would affect transport through sandstone cores and recovery of residual oil. Our results show that the behavior of nanoparticle-stabilized emulsions flowing through porous media can be described in terms of filtration theory and electrostatic and van der Waals interactions. Residual oil recovery was enhanced by optimizing em—ulsion characteristics such as salinity, method of generation, and zeta potential. We emulsified widely available, low-cost natural gas liquids in brine using polyethylene glycol-coated silica nanoparticles. Emulsions were generated via sonication at varying salinities and zeta potentials for observations of emulsion characteristics. We conducted corefloods in Boise sandstone to assess the effects of different emulsion properties on residual oil recovery of heavy oils, effective permeability reduction capabilities (i.e. conformance control), and in-situ emulsion stability. Emulsions with high salinity content resulted in better in situ emulsion stability and up to 89% recovery of residual mineral oil at low injection rates. By increasing the salinity, the magnitude of the repulsive electrostatic force between emulsion droplets and grain surfaces is decreased, leading to increased droplet interception on grain surfaces. This results in more extensive droplet-pore throat blockage, redirecting the displacing fluid into less permeable zones. Increasing the magnitude of the droplet zeta potential of injected emulsions marginally increased in oil recovery, significantly reduced permeability, and increased in situ emulsion stability. The best residual oil recovery occurs when emulsion droplets can persist without coalescence under the pressures required to push them into small pore throats, while simultaneously moving through the larger pore throats rather than being mechanically or electrostatically retained. Proper emulsion flood design, therefore, must incorporate characterization of both the pore structure and the electrostatic properties of reservoir rocks and how these will interact with the emulsions.
Real-time drilling optimization is a topic of significant interest because of its economic value, and its importance increases particularly during periods of low oil prices. This paper evaluates different optimization strategies and algorithms for real-time optimization of an objective function (function to be optimized) specific to drilling. The objective function optimized here is derived from a data-driven (or machine-learning) model with an unknown functional form. A data-driven model has been used to calculate the objective function [rate of penetration (ROP)] because it has been shown to be more efficient in ROP prediction relative to deterministic models (Hegde and Gray 2017). The data-driven ROP model is built using machine-learning algorithms; measured drilling parameters [weight on bit (WOB), revolutions per minute (rev/min), strength of rock, and flow rate] are used as inputs to predict the ROP.
Real-time drilling optimization that is data-driven is challenging because of run-time constraints. This is perceived as a handicap for data-driven models because their functional form is unknown, making them more difficult to optimize. This paper evaluates algorithms depending on their ability to best maximize the objective (ROP) and their time effectiveness. Two simple yet robust algorithms, the eyeball method and the random-search method, are presented as plausible solutions to this problem. These methods are then compared with popular metaheuristic algorithms, evaluating the tradeoff between improvement in the objective (search for a global optimal) and the computational time of run.
Using results from the simulations conducted in this paper, we concluded that data-driven models can be used for real-time drilling despite their computational constraints by choosing the right optimization algorithm. The best tradeoff in terms of ROP increase as well as computational efficiency evaluated in this paper is the simplex algorithm. The ROP was improved by 30% on average with a variance of 2.5% in the test set over 14 formations that were tested.
The matrix permeability of shale is controlled by the microstructure of shale pore network. Therefore, a thorough understanding of shale pore structure is fundamental to the prediction of shale permeability. We constructed a physically representative pore network model for two Barnett Shale samples. We predicted a Darcy permeability of 7.19 nanodarcies (nd) and apparent permeability of 57.34 nd for these samples. We explored the pore structure of shale matrix with low-pressure nitrogen adsorption/desorption isotherms. The pore size distribution, the network connectivity, as well as pore spatial arrangement are determined for the organic matter and the whole matrix. We separated pores in the network model into two groups: the affiliated pores and the dominant pores. For these samples, the affiliated pores are pores with diameters smaller than 8 nm and the dominant pores are with diameters larger than 8 nm. The affiliated pores develop on the walls of the dominant pores. The cutoff value between affiliated pores and dominant pores may differ for different samples. The pore spatial arrangement is validated against previous literature resultsand a diagenetic explanation for development of shale pore system. The network model can be used to predict shale permeability and other petrophysical properties.
Microfractures are important mechanical discontinuities in shales and are important for fluid flow during production. Understanding their properties is crucial for accurate shale production prediction and implementing effective stimulation strategies. Scanning electron microscope (SEM) images are useful for characterizing shale microstructure, but manual image analysis is often challenging and time consuming. We present an alternative method for quickly characterizing microfractures and obtaining pore structure information from SEM images using machine learning algorithms and image processing. Using this approach, SEM images were obtained from deformed and intact samples of a carbonate rich shale and a siliceous shale with the goal of identifying microfractures. Support vector machine, convolutional neural networks, and four pretrained convolutional neural networks were implemented to differentiate SEM images containing fractures (frac-images) and SEM images containing no fractures (non-frac-images). Images containing fractures were identified with 92% training accuracy and 88% testing accuracy. A pretrained convolutional neural network with 16 layers (vgg16) was shown to perform best for this image classification task.
Water-in-water (w/w) emulsions are known for their low interfacial tensions (IFT) which makes their stability to shear questionable. This is because of low particle attachment energies, which can be just a few kT. Therefore, emulsions stabilized with larger particles should display greater stability to shear because of larger attachment energies (10–100 or more kT). This is typically not an issue with traditional oil-in-water Pickering emulsions because particle attachment energies are much larger due to higher interfacial tensions, even when very small particles are used.
Single-phase permeability k has intensively been investigated over the past several decades by means of experiments, theories and simulations. Although the effect of surface roughness on fluid flow and permeability in single pores and fractures as well as networks of fractures was studied previously, its influence on permeability in a random mass fractal porous medium constructed of pores of different sizes remained as an open question. In this study, we, therefore, address the effect of pore–solid interface roughness on single-phase flow in random fractal porous media. For this purpose, we apply a mass fractal model to construct porous media with a priori known mass fractal dimensions 2.579≤Dm≤2.893 characterizing both solid matrix and pore space. The pore–solid interface of the media is accordingly roughened using the Weierstrass–Mandelbrot approach and two parameters, i.e., surface fractal dimension Ds and root-mean-square (rms) roughness height. A single-relaxation-time lattice Boltzmann method is applied to simulate single-phase permeability in the corresponding porous media. Results indicate that permeability decreases sharply with increasing Ds from 1 to 1.1 regardless of Dm value, while k may slightly increase or decrease, depending on Dm, as Ds increases from 1.1 to 1.6.