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Shale Analytics

Author : Shahab D. Mohaghegh
ISBN : 9783319487533
Genre : Science
File Size : 87.21 MB
Format : PDF, ePub
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This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.
Category: Science

Shale Gas

Author : Ali Al-Juboury
ISBN : 9781789236187
Genre : Technology & Engineering
File Size : 33.28 MB
Format : PDF, Docs
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Natural gas, particularly shale gas, is one of the main sustainable energy sources in the current century. It is an abundant energy resource, playing an active role in future energy demand and enabling nations to transition to higher support on renewable energy sources. The book aims to add some contributions and new advances in technologies and prospects on shale gas reserves in selected regions of the world, in terms of new technologies of extraction, new discoveries of promising reserves, synthesis and applications to get high quality of this cleanest consuming non-renewable energy source.
Category: Technology & Engineering

Data Driven Analytics For The Geological Storage Of Co2

Author : Shahab Mohaghegh
ISBN : 9781315280790
Genre : Science
File Size : 86.35 MB
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Data driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of Artificial Intelligence and Machine Learning in data driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of Artificial Intelligence and Machine Learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.
Category: Science

Applied Statistical Modeling And Data Analytics

Author : Srikanta Mishra
ISBN : 9780128032800
Genre : Science
File Size : 86.78 MB
Format : PDF, Kindle
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Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications
Category: Science

Optimization Of Hydraulic Fracture Stages And Sequencing In Unconventional Formations

Author : Ahmed Alzahabi
ISBN : 9781351618229
Genre : Technology & Engineering
File Size : 85.69 MB
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Shale gas and/or oil play identification is subject to many screening processes for characteristics such as porosity, permeability, and brittleness. Evaluating shale gas and/or oil reservoirs and identifying potential sweet spots (portions of the reservoir rock that have high-quality kerogen content and brittle rock) requires taking into consideration multiple rock, reservoir, and geological parameters that govern production. The early determination of sweet spots for well site selection and fracturing in shale reservoirs is a challenge for many operators. With this limitation in mind, Optimization of Hydraulic Fracture Stages and Sequencing in Unconventional Formations develops an approach to improve the industry’s ability to evaluate shale gas and oil plays and is structured to lead the reader from general shale oil and gas characteristics to detailed sweet-spot classifications. The approach uses a new candidate selection and evaluation algorithm and screening criteria based on key geomechanical, petrophysical, and geochemical parameters and indices to obtain results consistent with existing shale plays and gain insights on the best development strategies going forward. The work introduces new criteria that accurately guide the development process in unconventional reservoirs in addition to reducing uncertainty and cost.
Category: Technology & Engineering

Study Of Flow Mechanisms In Shale Using Ct Imaging And Data Analytics

Author : Beibei Wang
ISBN : OCLC:1090342447
Genre :
File Size : 88.45 MB
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Due to the decline of current conventional oil and gas reservoirs, the development of unconventional resources has received great attention in recent years (World Energy Outlook 2012). Shale, formations that are considered as both source rocks and reservoirs, play a significant role in the USA's hydrocarbon production (EIA 2019). Hence, understanding the effective and efficient development of unconventional resources is of crucial importance. Nevertheless, there are still numerous technical challenges related to fluid transport in shale. The nanoporous system of shale formations has relatively low porosity and ultralow permeability that has considerable influence on fluid transport by advection and diffusion (Javadpour et al., 2007). Moreover, cracks and natural fractures are also very common in shale and they play a very important role in production. Natural cracks and fractures contribute directly to storage and permeability, and they can interact with hydraulic fracturing treatments (Gale et al., 2010). The heterogeneous pore and network system together with the significant variation in mineral composition raise challenges for the understanding of fluid transport through shale. Mechanistic understanding of fluid transport in shale reservoirs is crucial for future production forecasts and for better field planning and development. This research work bridges the gap in understanding the storage and transport mechanisms of unconventional resources. Various experimental, simulation and data analysis techniques were applied, as follows. First, simulation of adsorption properties using statistical modelling based on Grand Canonical Monte Carlo (GCMC) techniques for CO2 adsorption in clay systems was performed. Significant CO2 is predicted to adsorb to clay. Results from simulation and experiment are compared to further investigate the adsorption properties of gas shale and to predict the adsorbed phase densities as a function of temperature, pressure, and pore size. It was observed that the simulated CO2 adsorption for the clay is smaller compared to organic matter. This result shows the same trend as the experimental measurement. At 60 bar and 80 °C, the CO2 adsorption in a 2 nm pore in clay is around 2 mmol/cm3; while in the 2 nm pore in the organic matter, the CO2 adsorption is around 13 mmol/cm3. Second, we carried out experiments to probe liquid behaviour in shale samples by X-ray CT imaging. CT scans were taken continuously after injecting water and water tracer into the core. From the change of CT signal of the shale core over time as the water flows through the porous medium, the water flow path is visualized. From CT image analysis, when injecting water into the dry core, a water front was observed to move along the core over time. The CT signal of the entire core increased substantially after breakthrough indicating that water preferably flowed through larger pore space and then transported into the matrix. Third, following on the success of imaging liquid movement in shale, experiments were carried out to visualize and study liquid diffusion in sandstone, carbonate, and two shale samples. The diffusion study is designed to be purely concentration driven with no pressure difference applied to the system. An effective diffusion coefficient was calculated by fitting the experimentally measured concentration profile data and analytical solutions from Fick's law. Then, sample tortuosity was analyzed based on the effective diffusion. The sandstone and carbonate had tortuosities of 1.34 and 1.36, respectively, in agreement with literature. The shale samples had tortuosity in excess of 10 indicating substantial geometrical complexity of shale porous networks. Finally, a data-driven deep learning approach was developed to infer the permeability distribution of shale samples. Through analyzing flow images of the shale sample from CT scans, a convolutional neural network model was trained to calculate the average and local permeability of the sample. Compared to traditional permeability measurement and calculation, this method presents a local 3-D permeability map of the shale and provides valuable information to understand the nature of shale and their production capabilities.

Position Paper

Author : John C. Sharer
ISBN : STANFORD:36105032888369
Genre : Natural gas
File Size : 47.96 MB
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Category: Natural gas

Hart S E And P

Author :
ISBN : STANFORD:36105123887601
Genre : Gas industry
File Size : 89.59 MB
Format : PDF
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Category: Gas industry