Petrophysical analysis of downhole logs requires accurate knowledge of matrix properties, commonly referred to as matrix adjustments. In organic-rich shale, the presence of abundant kerogen (solid and insoluble sedimentary organic matter) has a disproportionate impact on matrix properties because kerogen is compositionally distinct from all inorganic minerals that comprise the remainder of the solid matrix. As a consequence, matrix properties can be highly sensitive to kerogen properties. Moreover, the response of many downhole logs to kerogen is similar to their response to fluids. Relevant kerogen properties must be accurately known to separate tool responses to kerogen (in the matrix volume) and fluids (in the pore volume), to arrive at accurate volumetric interpretations. Unfortunately, relevant petrophysical properties of kerogen are poorly known in general and nearly always unknown in the formation of interest.
A robust method of “thermal maturity-adjusted log interpretation” replaces these unknown or assumed kerogen properties with a consistent set of relevant properties specifically optimized for the organic shale of interest, derived from only a single estimate of thermal maturity of the kerogen. The method is founded on the study of more than 50 kerogens spanning eight major oil- and gas-producing sedimentary basins, 300 Ma of depositional age, and thermal maturity from immature to dry gas (vitrinite reflectance, Ro, ranges from 0.5 to 4%). The determined kerogen properties include measured chemical (C, H, N, S, O) composition and skeletal (grain) density, as well as computed nuclear properties of apparent log density, hydrogen index, thermal- and epithermal-neutron porosities, macroscopic thermal-neutron capture cross section, macroscopic fast-neutron elastic scattering cross section, and photoelectric factor. For kerogens relevant to the petroleum industry (i.e., type II kerogen with thermal maturity ranging from early oil to dry gas), it is demonstrated that petrophysical properties are controlled mainly by thermal maturity, with no observable differences between sedimentary basins. As a result, universal curves are established relating kerogen properties to thermal maturity of the kerogen, and the curves apply equally well in all studied shale plays. Sensitivity calculations and field examples demonstrate the importance of using a consistent set of accurate kerogen properties in downhole log analysis. Thermal maturity-adjusted log interpretation provides a robust estimate of these properties, enabling more accurate and confident interpretation of porosity, saturation, and hydrocarbon in place in organic-rich shales.
Spectrograms computed from passive seismic trace data reveal the presence and types of signals that are emitted from fractures in reservoirs. The signals are dominated by resonances, are episodic, and are stimulated in different fractures at different times. Computing the spectrograms over hours and days allows clock times to be identified that can be used for imaging the active fractures in the reservoirs. Studies of passive seismic recorded during hydraulic fracturing suggest that these resonances are related to transmissive fractures and arise from fluid filled fractures. They are present in the trace data during quiet times, during stimulation times, and during reservoir production times and the resonating fractures are mapped for all of these conditions. These resonances appear in at least two forms. One type can be modeled as eigen-vibrations of isolated fractures excited by external forces and present in the data as dispersive resonance in the spectrograms. The second type appears as non-dispersive resonance interpreted as turbulent fluid flow moving in and out of connected fractures. By studying their characteristics, we have been able to identify dispersive and non-dispersive types of resonance in our data. The dispersive type can be excited in fractures that experience fracture dimension changes and also in fractures that are experiencing pressure changes within the fracture but do not have fluid flow. Using a unique data set from the hydraulic stimulation of an Engineered Geothermal System development, we have identified both types and have examples of the resonance starting as dispersive resonance, changing to non-dispersive resonance, and then terminating in microearthquake events.
Processing selected frequency and time windows of these emissions allows the resonance waveforms to be mapped back to their source locations using seismic depth migration. In quiet, pre-stimulation periods, resonances that show dispersion are more common. During stimulation, when fractures have been pressurized, non-dispersive resonance is more common perhaps caused by fluid flow in the fractures. In the transition between these states, previously closed fractures that are intersected by the stimulation will change their resonance character from dispersive type to non-dispersive type. Recordings during reservoir flooding or during production demonstrate a dominance of non-dispersive resonance. In all cases their locations can be mapped using multichannel recording and seismic depth migration methods.
Butler, Shane (University of North Dakota Energy & Environmental Research Center) | Azenkeng, Alexander (University of North Dakota Energy & Environmental Research Center) | Mibeck, Blaise (University of North Dakota Energy & Environmental Research Center) | Kurz, Bethany (University of North Dakota Energy & Environmental Research Center) | Eylands, Kurt (University of North Dakota Energy & Environmental Research Center)
Advanced characterization of the Bakken Formation, an unconventional oil and gas play of the Williston Basin, was performed via newly developed analytical tools of microscopic investigation in concert with standard laboratory methods. Characterization of an unconventional formation to understand the composition and distribution of framework grains, organic matter (OM), clay minerals, and porosity is difficult because of the extremely lithified nature of the lithofacies within the formation and the small grain and particle sizes. In this study, corroborative methods aimed to define micro- and nanoscale fabrics that impact parameters such as maturity, recovery, clay content, micropore networks, and CO2 interactions for either storage or enhanced oil recovery (EOR). Lateral and vertical variations in the rock fabric across multiple wellsites were observed on a micro- to nanometer scale with innovative analytical technologies.
Detailed morphologies and chemical compositions of ion-milled samples were obtained with field emission scanning electron microscopy (FESEM) coupled with energy-dispersive spectroscopy (EDS). Furthermore, a new software suite, Advanced Mineral Identification and Characterization System (AMICS), was used to classify and quantify mineralogy, OM, and porosity from the FESEM images. For validation purposes, x-ray diffraction was used to obtain bulk mineral and clay mineral data and x-ray fluorescence to obtain bulk chemical compositions of the samples. Advanced image analysis was performed on high-resolution FESEM images as another corroborative approach to characterize key features of interest within the lithofacies. Each sample consisted of high-resolution FESEM backscattered electron (BSE) images taken at multiple magnifications to maximize particle morphology in the fine-grained rock of the unconventional reservoir.
The data highlighted trends related to factors that impact CO2 transport and sorption in unconventional reservoirs. Segmented BSE images from the FESEM using program parameters that included texture, gray scale, and other morphological properties made it possible to estimate OM, clays, and porosity for each sample. The compositional analysis, including matrix porosity, OM porosity, and mineralogical composition maps, provided context for the potential of organic-rich and tight rock formations as CO2- based EOR targets or CO2 storage targets.
Advanced image analysis techniques were applied to better understand and quantify factors that could affect CO2 storage in the Bakken Formation, with an ultimate goal of improved method development to estimate CO2 storage potential of unconventional reservoirs. Discernible differences in fabric, mineral, and elemental content in comparable lithofacies across wellsites provided insight into the nature of the Bakken Formation, which could serve as a proxy for other tight rock, organic-rich reservoirs that could be potential targets for both CO2-based EOR and CO2 storage.
Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (University of Oklahoma) | El-Monier, Ilham (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development Co. Ltd. CNPC / China National Oil and Gas Exploration and Development Corporation) | Xiong, Fengyang (The Ohio State University)
The accurate estimation of the elastic properties of the rock is of great importance for designing a successful hydraulic fracturing. Among these properties, Young's modulus and Poisson's ratio essentially control fracture aperture and conductivity. However, the fissile nature of the shale rock largely challenges the mechanical properties measurement using a cylindrical core sample. While the nanoindentation technology can be applied to measure small chips of rock fragment, but reproducible experiments are required to provide an unbiased estimation. Herein, we are proposing a machine learning approach to predict the elastic moduli. We utilized an ensemble of data mining techniques and a database that include both the mineralogy and pore characteristics. Our results indicate that K-Means clustering yields best performance on data classification than all other tested methods while the elastic moduli estimation from Artificial Neural Network (ANN)is most accurate than Support Vector Machine (SVM), Multivariate Linear Regression (MLR) and Multivariate Adaptive Regression Spine (MARS). The dimension reduction became essential when then input datasets are remarkably correlated. The supervised learning techniques with our proposed approach leverage the usability of the lab experiment data and overcome disadvantages of the traditional elastic moduli measurement. It also further lands the far-reaching guide for the fracturing design.
Machine learning have recently revolutionized the oil and gas industry (Alcocer and Rodrigues 2001, Al-Fattah and Startzman 2001, Kohli and Arora 2014, Okpo et al. 2016, Sinha et al. 2016, Tariq et al. 2017, Luo et al. 2018, Nande 2018, Rashidi et al. 2018, Sidaoui et al. 2018, Xu et al. 2019). As a data-rich industry, machine learning finds applications in every corner ranging from production forecast to drilling efficiency (Hegde and Gray 2017, Fulford et al. 2016). Given the significance of geomechanical properties of the rock, the volume of studies has attempted to leverage machine learning techniques. For instance, Li et al. (2018) developed a workflow implementing various machine learning algorithm to accurately provide an alternative to synthesize the sonic logs and geomechanical properties afterwards. In the same time, Hadi and Nygaard (2018) used Artificial Neural Network (ANN) to develop an empirical model to estimate the shear velocity from conventional logs. Another dimension was presented by Jain et al. (2015) where they proposed an approach to integrate both core and log spectroscopy which provided better estimations of the mineralogy.
Gas production from shale formations is growing, especially in the USA. However, the origin of shale gases remains poorly understood. The objective of this study is to interpret the origin of shale gases from around the world using recently revised gas genetic diagrams. We collected a large dataset of gas samples recovered from shale formations around the world and interpreted the origin of shale gases using recently revised gas genetic diagrams. The dataset includes >2000 gas samples from the USA, China, Canada, Saudi Arabia, Australia, Sweden, Poland, Argentina, United Kingdom and France. Both free gases collected at wellheads and desorbed gases from cores are included in the dataset. Shale gas samples come from >34 sedimentary basins and >65 different shale formations (plays) ranging in age from Proterozoic (Kyalla and Velkerri Formations, Australia) to Miocene (Monterey Formation, USA). The original data were presented in >80 publications and reports. We plotted molecular and isotopic properties of shale gases on the revised genetic diagrams and determined the origin of shale gases. Based on the distribution of shale gases within the genetic diagram of δ13C of methane (C1) versus C1/(C2+C3), most shale gases appear to have thermogenic origin. The majority of these thermogenic gases are late-mature (e.g., Marcellus Formation, USA and Wufeng-Longmaxi Formation, China) and mid-mature (associated with oil generation, e.g., Eagle Ford Formation, USA). Importantly, shales may contain early-mature thermogenic gases rarely found in conventional accumulations (e.g., T⊘yen Formation, Sweden and Colorado Formation, Canada). Some shale gases have secondary microbial origin, i.e., they originated from anaerobic biodegradation of oils. For example, gases from New Albany Formation and Antrim Formation (USA) have secondary microbial origin. Relatively few shale gases have primary microbial origin, and they often have some minor admixture of thermogenic gas (e.g., Nicolet Formation, Canada and Alum Formation, Sweden). Two other revised gas genetic plots based on δ2H and δ13C of methane and δ13C of CO2 support and enhance the above interpretation. Although shales that contain secondary microbial gas can be productive (e.g., New Albany Formation, USA), the resource-rich, highly productive and commercially successful shale plays contain thermogenic gas. Plays with late-mature thermogenic gas (e.g., Marcellus Formation, USA and Wufeng-Longmaxi Formation, China) appear to be most productive.
Robust links between unconventional pore-scale properties, organic matter, and production trends remain unclear, despite numerous pore-scale characterization studies from various petro-technical disciplines. Specifically, a clear and/or widely agreed upon understanding of kerogen-bitumen-porosity relationships is currently lacking. This work explores an interdisciplinary petrographic methodology to link organic pore-associations and habit to geochemistry and, ultimately, petrophysics. The method directly collocates (overlays) high resolution mosaic scanning electron microscopy (SEM) images with reflected white and UV/fluorescent light images (organic matter petrography analysis), enabling the identification of various kerogen maceral types and bitumen within the monochromatic SEM images. Mosaic SEM images are leveraged to help ensure the statistical representativeness of the characterized area. The consistent application of this integrated imaging workflow across various rock types, maturity, and basins has enabled foundational insights into specific organic-matter porosity associations and trends.
Understanding unconventional reservoirs requires examining the porosity and permeability hosted within the mudrock-based (clay and silt-sized grains; includes claystones, mudstones, chalks, siltstones, shales, etc.) stratigraphy of the petroleum system, typically characterized by low porosity and low permeability. Organic porosity, specifically, has been studied for less than a decade, and there is currently a lack of clear understanding of organic porosity development in unconventional mudstone reservoirs (Katz and Arango, 2018). Due to the small nature of the pore sizes, scanning electron microscopy (SEM) is one method used to characterize nanoporosity hosted in the mineral matrices and/or organic matter (Loucks and Reed, 2014). However, SEM is limited in the ability to differentiate between different organic macerals, or individual organic matter constituents, found in the examined organic-rich shale/mudstone. Traditional methods for definitive organic matter determination include organic petrographic analyses using standard incident white light and UV microscopy under oil immersion. Organic petrography is limited to lower magnifications, approximately 50x magnification, compared to the high-magnification possible with SEM, allowing for resolutions up to approximately 2.5 nm/pixel and, correspondingly, pore features of around 5-10 nm.
Penghui, Su (PetroChina Research Institute of Petroleum Explorationand and Development) | Zhaohui, Xia (PetroChina Research Institute of Petroleum Explorationand and Development) | Ping, Wang (PetroChina Research Institute of Petroleum Explorationand and Development) | Liangchao, Qu (PetroChina Research Institute of Petroleum Explorationand and Development) | xiangwen, Kong (PetroChina Research Institute of Petroleum Explorationand and Development) | Wenguang, Zhao (PetroChina Research Institute of Petroleum Explorationand and Development)
Interest has spread to potential unconventional shale reservoirs in the last decades, and they have become an increasingly important source of hydrocarbon. Importantly, pore structure of shale has considerable effects on the storage, seepage and output of the fluids in shale reservoirs so that reliable fractal characteristics are essential. To better understand the evolution characteristics of pore structure for a shale gas condensate reservoir and their influence on liquid hydrocarbon occurrences and reservoir physical properties, we conducted high-pressure mercury intrusion tests (HPMIs), field emission scanning electron microscopies (FESEM), total organic carbon (TOC), Rock-Eval pyrolysis and saturation measurements on samples from the Duvernay formation. Furthermore, the fractal theory is applied to calculate the fractal dimension of the capillary pressure curves, and three fractal dimensions D1, D2 and D3 are obtained. The relationships among the characteristics of the Duvernay shale (TOC, organic matter maturity, fluid saturation), the pore structure parameters (permeability, porosity, median pore size), and the fractal dimensions were investigated.
The results show that the fractal dimension D1 ranges from 2.44 to 2.85, D2 ranges from 2.09 to 2.15 and D3 ranges from 2.35 to 2.48. D2 and D3 have a good positive correlation. The pore system studied mainly consists of organic pores and microfractures, with the percentage of micropores being 50.38%. TOC has a positive relationship with porosity and D3 due to the development of organic pores. D3 has a positive correlation with gas saturation. With increased D3, median pore size shows a decreasing trend and an increase in permeability and porosity, demonstrating that D3 has a large effect on pore size distribution and the heterogeneity of pore size. In general, D3 has a better correlation with petrophysical and petrochemical parameters. Fractal theory can be applied to better understand the pore evolution, pore size distribution and fluid storage capacity of shale reservoirs.
The geomechanical properties of reservoirs, which are important for formation stimulation, are often determined from triaxial tests on large-scale samples such as core plugs or blocks. It is difficult to recover large samples from shale formations because they are mechanically unstable and usually break down into pieces. The present study develops a two-scale model that uses drill cuttings to estimate the static elastic properties of shales at the core scale. We first propose a physically representative element to capture the elastic deformation of a solid grain with a known minerology by accounting for the grain size and its elastic properties using the structural-mechanics approach (a small-scale model). We then develop a core-scale model dependent on the volume fractions of the minerals, which are obtained from X-ray diffraction (XRD), for different realizations of the spatial distribution of the solid grains (large-scale model). The sensitivity of the large-scale model to the number of the elements is tested. The proposed model shows promising results for four shale formations (New Albany, Rocky Mountain Siliceous, Lower Bakken, and Barnett) and has major applications for the geomechanical characterization of a formation from drill cuttings.
Kurz, Bethany A. (Energy & Environmental Research Center) | Sorensen, James A. (Energy & Environmental Research Center) | Hawthorne, Steven B. (Energy & Environmental Research Center) | Smith, Steven (Energy & Environmental Research Center) | Sanei, Hamed (Aarhus University) | Ardakani, Omid (Geological Survey of Canada) | Walls, Joel D. (Ingrain - Halliburton) | Jin, Lu (Energy & Environmental Research Center) | Butler, Shane (Energy & Environmental Research Center) | Beddoe, Christopher (Energy & Environmental Research Center) | Mibeck, Blaise (Energy & Environmental Research Center)
Kerogen is functionally defined as the portion of OM in a sample that is insoluble using organic solvents (Tissot and Welte, 1984). Primary kerogen consists of OM deposited in the sedimentary basins from which hydrocarbons form during the catagenesis process in the sedimentary rocks (Vandenbroucke and Largeau, 2007). Depending on the depositional environment of the rock, kerogens can be composed of algae, spores, pollen, and woody or herbaceous material (Tissot and Welte, 1984). Solid bitumen is also an important component of kerogen in organic-rich mudrocks (Sanei and others, 2015). Solid bitumen is generally defined as a secondary kerogen formed because of cracking of retained hydrocarbon (Jacob, 1984; Curiale, 1986, Sanei and others, 2015). Curiale (1986) also identified a preoil bitumen formed from the earlygeneration (immature) products of rich source rocks, probably extruded as very viscous fluids, which migrated minimal distances to fractures. Some components of solid bitumen, depending on their composition, are generally considered extractable using organic solvents, although at later stages of thermal maturity, solid bitumen becomes a carbon-rich, unextractable solid referred to as pyrobitumen (Jacob, 1984; Curiale, 1986; Sanei and others, 2015).
Torres-Parada, Emilio J. (The University of Oklahoma) | Sinha, Saurabh (The University of Oklahoma) | Infante-Paez, Lennon E. (The University of Oklahoma) | Slatt, Roger M. (The University of Oklahoma) | Marfurt, Kurt J. (The University of Oklahoma)
Thermal maturity of Woodford Shale gas and oil plays, Oklahoma, USA, International Journal of Coal Geology, p. 1-13.