Fine Scale Characterization Of Organic Matter Using Analytical Methods: Raman And NMR Spectroscopies

In order to assess a source rock for economical exploitation purposes, many parameters should be considered. Regarding the geochemical aspects, the most important ones are amount of organic matter (OM) and its quality. Quality refers to thermal maturity level and the nature of the OM from which it was formed that affect the ability of oil and gas generation. Vitrinite reflectance and programed pyrolysis such as Rock-Eval (RE) are common methods for such analysis. However, vitrinite is not always present in sediments and RE is providing bulk properties. Moreover, the increase in exploration and production of unconventional reservoirs and their complexity has led to the employment of new methods for resource-play assessment, such as Raman spectroscopy and nuclear magnetic resonance (NMR). In the present dissertation, two mentioned analytical methods of Raman spectroscopy and Hydrogen NMR were utilized for fine scale studying of organic matter and individual macerals to show the potential of such methods in thermal maturity level detection, prediction of elastic properties, structural evolution, and heterogeneity of organic matter

Numerical Study of Velocity and Mixture Fraction Fields in a Turbulent Non-Reacting Propane Jet Flow Issuing into Parallel Co-Flowing Air in Isothermal Condition through OpenFOAM

This research employs computational methods to analyze the velocity and mixture fraction distributions of a non-reacting Propane jet flow that is discharged into parallel co-flowing air under iso-thermal conditions. This study includes a comparison between the numerical results and experimental results obtained from the Sandia Laboratory (USA). The objective is to improve the understanding of flow structure and mixing mechanisms in situations where there is no involvement of chemical reactions or heat transfer. In this experiment, the Realizable k-ε eddy viscosity turbulence model with two equations was utilized to simulate turbulent flow on a nearly 2D plane (specifically, a 5-degree partition of the experimental cylinder domain). This was achieved using OpenFOAM open-source software and swak4Foam utility, with the reactingFoam solver being manipulated carefully. The selection of this turbulence model was based on its superior predictive capability for the spreading rate of both planar and round jets, as compared to other variants of the k-ε models. Numerical axial and radial profiles of different parameters were obtained for a mesh that is independent of the grid (mesh B). These profiles were then compared with experimental data to assess the accuracy of the numerical model. The parameters that are being referred to are mean velocities, turbulence kinetic energy, mean mixture fraction, mixture fraction half radius (Lf), and the mass flux diagram. The validity of the assumption that w߰ = v߰ for the determination of turbulence kinetic energy, k, seems to hold true in situations where experimental data is deficient in w߰. The simulations have successfully obtained the mean mixture fraction and its half radius, Lf, which is a measure of the jet’s width. These values were determined from radial profiles taken at specific locations along the X-axis, including x/D = 0, 4, 15, 30, and 50. The accuracy of the mean vertical velocity fields in the X-direction (Umean) is noticeable, despite being less well-captured. The resolution of mean vertical velocity fields in the Y-direction (Vmean) is comparatively lower. The accuracy of turbulence kinetic energy (k) is moderate when it is within the range of Umean and Vmean. The absence of empirical data for absolute pressure (p) is compensated by the provision of numerical pressure contours

Machine learning: A useful tool in geomechanical studies, a case study from an offshore gas field

For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

For a safe drilling operation with the of minimum borehole instability challenges, building a mechanical earth model (MEM) has proven to be extremely valuable. However, the natural complexity of reservoirs along with the lack of reliable information leads to a poor prediction of geomechanical parameters. Shear wave velocity has many applications, such as in petrophysical and geophysical as well as geomechanical studies. However, occasionally, wells lack shear wave velocity (especially in old wells), and estimating this parameter using other well logs is the optimum solution. Generally, available empirical relationships are being used, while they can only describe similar formations and their validation needs calibration. In this study, machine learning approaches for shear sonic log prediction were used. The results were then compared with each other and the empirical Greenberg–Castagna method. Results showed that the artificial neural network has the highest accuracy of the predictions over the single and multiple linear regression models. This improvement is more highlighted in hydrocarbon-bearing intervals, which is considered as a limitation of the empirical or any linear method. In the next step, rock elastic properties and in-situ stresses were calculated. Afterwards, in-situ stresses were predicted and coupled with a failure criterion to yield safe mud weight windows for wells in the field. Predicted drilling events matched quite well with the observed drilling reports

Evaluating Molecular Evolution of Kerogen by Raman Spectroscopy: Correlation with Optical Microscopy and Rock-Eval Pyrolysis

Vitrinite maturity and programmed pyrolysis are conventional methods to evaluate organic matter (OM) regarding its thermal maturity. Moreover, vitrinite reflectance analysis can be difficult if prepared samples have no primary vitrinite or dispersed widely. Raman spectroscopy is a nondestructive method that has been used in the last decade for maturity evaluation of organic matter by detecting structural transformations, however, it might suffer from fluorescence background in low mature samples. In this study, four samples of different maturities from both shale formations of Bakken (the upper and lower members) Formation were collected and analyzed with Rock-Eval (RE) and Raman spectroscopy. In the next step, portions of the same samples were then used for the isolation of kerogen and analyzed by Raman spectroscopy. Results showed that Raman spectroscopy, by detecting structural information of OM, could reflect thermal maturity parameters that were derived from programmed pyrolysis. Moreover, isolating kerogen will reduce the background noise (fluorescence) in the samples dramatically and yield a better spectrum. The study showed that thermal properties of OM could be precisely reflected in Raman signals

Backtracking to Parent Maceral from Produced Bitumen with Raman Spectroscopy

In order to assess a source rock for economical exploitation purposes, many parameters should be considered; regarding the geochemical aspects, the most important ones are the amount of organic matter (OM) and its quality. Quality refers to the thermal maturity level and the type of OM from which it was formed. The origin of the OM affects the ability of the deposited OM between sediments to generate oil, gas, or both with particular potential after going through thermal maturation. Vitrinite reflectance and programmed pyrolysis (for instance, Rock-Eval) are common methods for evaluating the thermal maturity of the OM and its potential to generate petroleum, but they do not provide us with answers to what extent solid bitumen is oil-prone or gas-prone, as they are bulk geochemical methods. In the present study, Raman spectroscopy (RS), as a powerful tool for studying carbonaceous materials and organic matter, was conducted on shale and coal samples and their individual macerals to show the potential of this technique in kerogen typing and to reveal the parent maceral of the examined bitumen. The proposed methodology, by exhibiting the chemical structure of different organic matters as a major secondary product in unconventional reservoirs, can also detect the behavior of solid bitumen and its hydrocarbon production potential for more accurate petroleum system evaluation