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Seismic Texture Applied To Well Calibration And Reservoir Property Prediction In The North Central Appalachian Basin
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Book Synopsis Seismic Texture Applied to Well Calibration and Reservoir Property Prediction in the North Central Appalachian Basin by : Connor Gieger
Download or read book Seismic Texture Applied to Well Calibration and Reservoir Property Prediction in the North Central Appalachian Basin written by Connor Gieger and published by . This book was released on 2017 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Prediction of Reservoir Properties of the N-sand, Vermilion Block 50, Gulf of Mexico, from Multivariate Seismic Attributes by : Rasheed Abdelkareem Jaradat
Download or read book Prediction of Reservoir Properties of the N-sand, Vermilion Block 50, Gulf of Mexico, from Multivariate Seismic Attributes written by Rasheed Abdelkareem Jaradat and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The quantitative estimation of reservoir properties directly from seismic data is a major goal of reservoir characterization. Integrated reservoir characterization makes use of different varieties of well and seismic data to construct detailed spatial estimates of petrophysical and fluid reservoir properties. The advantage of data integration is the generation of consistent and accurate reservoir models that can be used for reservoir optimization, management and development. This is particularly valuable in mature field settings where hydrocarbons are known to exist but their exact location, pay, lateral variations and other properties are poorly defined. Recent approaches of reservoir characterization make use of individual seismic attributes to estimate inter-well reservoir properties. However, these attributes share a considerable amount of information among them and can lead to spurious correlations. An alternative approach is to evaluate reservoir properties using multiple seismic attributes. This study reports the results of an investigation of the use of multivariate seismic attributes to predict lateral reservoir properties of gross thickness, net thickness, gross effective porosity, net-to-gross ratio and net reservoir porosity thickness product. This approach uses principal component analysis and principal factor analysis to transform eighteen relatively correlated original seismic attributes into a set of mutually orthogonal or independent PC's and PF's which are designated as multivariate seismic attributes. Data from the N-sand interval of Vermilion Block 50 field, Gulf of Mexico, was used in this study. Multivariate analyses produced eighteen PC's and three PF's grid maps. A collocated cokriging geostaistical technique was used to estimate the spatial distribution of reservoir properties of eighteen wells penetrating the N-sand interval. Reservoir property maps generated by using multivariate seismic attributes yield highly accurate predictions of reservoir properties when compared to predictions produced with original individual seismic attributes. To the contrary of the original seismic attribute results, predicted reservoir properties of the multivariate seismic attributes honor the lateral geological heterogeneities imbedded within seismic data and strongly maintain the proposed geological model of the N-sand interval. Results suggest that multivariate seismic attribute technique can be used to predict various reservoir properties and can be applied to a wide variety of geological and geophysical settings.
Book Synopsis MULTICOMPONENT SEISMIC ANALYSIS AND CALIBRATION TO IMPROVE RECOVERY FROM ALGAL MOUNDS by : Paul La Pointe
Download or read book MULTICOMPONENT SEISMIC ANALYSIS AND CALIBRATION TO IMPROVE RECOVERY FROM ALGAL MOUNDS written by Paul La Pointe and published by . This book was released on 2003 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes the results made in fulfillment of contract DE-FG26-02NT15451, ''Multicomponent Seismic Analysis and Calibration to Improve Recovery from Algal Mounds: Application to the Roadrunner/Towaoc Area of the Paradox Basin, Ute Mountain Ute Reservation, Colorado''. Optimizing development of highly heterogeneous reservoirs where porosity and permeability vary in unpredictable ways due to facies variations can be challenging. An important example of this is in the algal mounds of the Lower and Upper Ismay reservoirs of the Paradox Basin in Utah and Colorado. It is nearly impossible to develop a forward predictive model to delineate regions of better reservoir development, and so enhanced recovery processes must be selected and designed based upon data that can quantitatively or qualitatively distinguish regions of good or bad reservoir permeability and porosity between existing well control. Recent advances in seismic acquisition and processing offer new ways to see smaller features with more confidence, and to characterize the internal structure of reservoirs such as algal mounds. However, these methods have not been tested. This project will acquire cutting edge, three-dimensional, nine-component (3D9C) seismic data and utilize recently-developed processing algorithms, including the mapping of azimuthal velocity changes in amplitude variation with offset, to extract attributes that relate to variations in reservoir permeability and porosity. In order to apply advanced seismic methods a detailed reservoir study is needed to calibrate the seismic data to reservoir permeability, porosity and lithofacies. This will be done by developing a petrological and geological characterization of the mounds from well data; acquiring and processing the 3D9C data; and comparing the two using advanced pattern recognition tools such as neural nets. In addition, should the correlation prove successful, the resulting data will be evaluated from the perspective of selecting alternative enhanced recovery processes, and their possible implementation. The work is being carried out on the Roadrunner/Towaoc Fields of the Ute Mountain Ute Tribe, located in the southwestern corner of Colorado. Although this project is focused on development of existing resources, the calibration established between the reservoir properties and the 3D9C seismic data can also enhance exploration success. During the time period covered by this report, the majority of the project effort has gone into the permitting, planning and design of the 3D seismic survey, and to select a well for the VSP acquisition. The business decision in October, 2002 by WesternGeco, the projects' seismic acquisition contractor, to leave North America, has delayed the acquisition until late summer, 2003. The project has contracted Solid State, a division of Grant Geophysical, to carry out the acquisition. Moreover, the survey has been upgraded to a 3D9C from the originally planned 3D3C survey, which should provide even greater resolution of mounds and internal mound structure.
Book Synopsis Reservoir Property Prediction from Well-logs, VSP and Multicomponent Seismic Data by : Natalia Soubotcheva
Download or read book Reservoir Property Prediction from Well-logs, VSP and Multicomponent Seismic Data written by Natalia Soubotcheva and published by . This book was released on 2006 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quantifying the Permeability Heterogeneity of Sandstone Reservoirs in Boonsville Field, Texas by Integrating Core, Well Log and 3D Seismic Data by : Qian Song
Download or read book Quantifying the Permeability Heterogeneity of Sandstone Reservoirs in Boonsville Field, Texas by Integrating Core, Well Log and 3D Seismic Data written by Qian Song and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasing hydrocarbon reserves by finding new resources in frontier areas and improving recovery in the mature fields, to meet the high energy demands, is very challenging for the oil industry. Reservoir characterization and heterogeneity studies play an important role in better understanding reservoir performance to meet this industry goal. This study was conducted on the Boonsville Bend Conglomerate reservoir system located in the Fort Worth Basin in central-north Texas. The primary reservoir is characterized as highly heterogeneous conglomeratic sandstone. To find more potential and optimize the field exploitation, it's critical to better understand the reservoir connectivity and heterogeneity. The goal of this multidisciplinary study was to quantify the permeability heterogeneity of the target reservoir by integrating core, well log and 3D seismic data. A set of permeability coefficients, variation coefficient, dart coefficient, and contrast coefficient, was defined in this study to quantitatively identify the reservoir heterogeneity levels, which can be used to characterize the intra-bed and inter-bed heterogeneity. Post-stack seismic inversion was conducted to produce the key attribute, acoustic impedance, for the calibration of log properties with seismic. The inverted acoustic impedance was then used to derive the porosity volume in Emerge (the module from Hampson Russell) by means of single and multiple attributes transforms and neural network. Establishment of the correlation between permeability and porosity is critical for the permeability conversion, which was achieved by using the porosity and permeability pairs measured from four cores. Permeability volume was then converted by applying this correlation. Finally, the three heterogeneity coefficients were applied to the permeability volume to quantitatively identify the target reservoir heterogeneity. It proves that the target interval is highly heterogeneous both vertically and laterally. The heterogeneity distribution was obtained, which can help optimize the field exploitation or infill drilling designs. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149473
Book Synopsis Uncertainty Analysis and Reservoir Modeling by : Y. Zee Ma
Download or read book Uncertainty Analysis and Reservoir Modeling written by Y. Zee Ma and published by AAPG. This book was released on 2011-12-20 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Techniques to Integrated Oil and Gas Reservoir Characterization by : Enwenode Onajite
Download or read book Applied Techniques to Integrated Oil and Gas Reservoir Characterization written by Enwenode Onajite and published by Elsevier. This book was released on 2021-04-14 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Techniques to Integrated Oil and Gas Reservoir Characterization: A Problem-Solution Discussion with Experts presents challenging questions encountered by geoscientists in their day-to-day work in the exploration and development of oil and gas fields and provides potential solutions from experts working in the field. Covers Amplitude Versus Offset (AVO), well-to-seismic tie, phase of seismic data, seismic inversion studies, pore pressure prediction, rock physics and exploration geological. The text examines challenges in the industry as well as the solutions and techniques used to overcome those challenges. Over the past several years there has been a growing integration of geophysical, geological, and reservoir engineering, production and petrophysical data to predict and determine reservoir properties. This includes reservoir extent and sand development away from the well bore, as well as in unpenetrated prospects, leading to optimization planning for field development. As such, geoscientists now must learn the technology, processes and challenges involved within their specific functions in order to complete day-to-day activities. Presents a thorough understanding of the requirements and issues of various disciplines in characterizing a wide spectrum of reservoirs Includes real-life problems and challenging questions encountered by geoscientists in their day-to-day work, along with answers from experts working in the field Provides an integrated approach among different disciplines (geology, geophysics, petrophysics, and petroleum engineering)
Book Synopsis Statistical Learning and Inference of Subsurface Properties Under Complex Geological Uncertainty with Seismic Data by : Anshuman Pradhan
Download or read book Statistical Learning and Inference of Subsurface Properties Under Complex Geological Uncertainty with Seismic Data written by Anshuman Pradhan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Attempting to characterize, image or quantify the subsurface using geophysical data for exploration and development of earth resources presents interesting and unique challenges. Subsurface heterogeneities are the result of abstract paleo geological events, exhibiting variability that is spatially complex and existent across multiple scales. This leads to significantly high-dimensional inverse problems under complex geological uncertainty, which are computationally challenging to solve with conventional geophysical and statistical inference methods. In this dissertation, we discuss these challenges within the context of subsurface property estimation from seismic data. We discuss three specific seismic estimation problems and propose methods from statistical learning and inference to tackle these challenges. The first problem we address is that of incorporating constraints from geological history of a basin into seismic estimation of P-wave velocity and pore pressure. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions, and pore pressure through rock-physics models. We account for geologic uncertainty by defining prior probability distributions uncertain basin modeling parameters. We have developed an approximate Bayesian inference framework that uses migration velocity analysis in conjunction with well and drilling data for updating velocity and pore pressure uncertainty. We apply our methodology in 2D to a real field case from the Gulf of Mexico. We demonstrate that our methodology allows for building a geologic and physical model space for velocity and pore-pressure prediction with reduced uncertainty. In the second problem, we investigate the applicability of deep learning models for conditioning reservoir facies models, parameterized by geologically realistic geostatistical models such as training-image based and object-based models, to seismic data. In our proposed approach, end-to-end discriminative learning with convolutional neural networks (CNNs) is employed to directly learn the conditional distribution of model parameters given seismic data. The training dataset for the learning problem is derived by defining and sampling prior distributions on uncertain parameters and using physical forward model simulations. We apply our methodology to a 2D synthetic example and a 3D real case study of seismic facies estimation. Our synthetic experiments indicate that CNNs are able to almost perfectly predict the complex geological features, as encapsulated in the prior model, consistently with seismic data. For real case applications, we propose a methodology of prior falsification for ensuring the consistency of specified subjective prior distributions with real data. We found modeling of additive noise, accounting for modeling imperfections and presence of noise in the data, to be useful in ensuring that a CNN, trained on synthetic simulations, makes reliable predictions on real data. In the final problem, we present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with approximate Bayesian computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin sand reservoir from an offshore delta dataset using pre-stack seismic data.
Book Synopsis Reservoir Characterization Using Seismic Reflectivity and Attributes by : Abdulrahman Mohammad Saleh Al-Moqbel
Download or read book Reservoir Characterization Using Seismic Reflectivity and Attributes written by Abdulrahman Mohammad Saleh Al-Moqbel and published by . This book was released on 2002 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion by : Sarah Bryson Coyle
Download or read book Reservoir Characterization of the Haynesville Shale, Panola County, Texas Using Rock Physics Modeling and Partial Stack Seismic Inversion written by Sarah Bryson Coyle and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the relationship between elastic properties and rock properties of the Haynesville Shale using rock physics modeling, simultaneous seismic inversion, and grid searching. A workflow is developed in which a rock physics model is built and calibrated to well data in the Haynesville Shale and then applied to 3D seismic inversion data to predict porosity and mineralogy away from the borehole locations. The rock physics model describes the relationship between porosity, mineral composition, pore shape, and elastic stiffness using the anisotropic differential effective medium model. The calibrated rock physics model is used to generate a modeling space representing a range of mineral compositions and porosities with a calibrated mean pore shape. The model space is grid searched using objective functions to select a range of models that describe the inverted P-impedance, S-impedance, and density volumes. The selected models provide a range of possible rock properties (porosity and mineral composition) and an estimate of uncertainty. The mineral properties were mapped in three dimensions within the area of interest using this modeling technique and inversion workflow. This map of mineral content and porosity can be interpreted to predict the best areas for hydraulic fracturing.
Book Synopsis Appalachian Basin Low-Permeability Sandstone Reservoir Characterizations by :
Download or read book Appalachian Basin Low-Permeability Sandstone Reservoir Characterizations written by and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A preliminary assessment of Appalachian basin natural gas reservoirs designated as 'tight sands' by the Federal Energy Regulatory Commission (FERC) suggests that greater than 90% of the 'tight sand' resource occurs within two groups of genetically-related units; (1) the Lower Silurian Medina interval, and (2) the Upper Devonian-Lower Mississippian Acadian clastic wedge. These intervals were targeted for detailed study with the goal of producing geologic reservoir characterization data sets compatible with the Tight Gas Analysis System (TGAS: ICF Resources, Inc.) reservoir simulator. The first phase of the study, completed in September, 1991, addressed the Medina reservoirs. The second phase, concerned with the Acadian clastic wedge, was completed in October, 1992. This report is a combined and updated version of the reports submitted in association with those efforts. The Medina interval consists of numerous interfingering fluvial/deltaic sandstones that produce oil and natural gas along an arcuate belt that stretches from eastern Kentucky to western New York. Geophysical well logs from 433 wells were examined in order to determine the geologic characteristics of six separate reservoir-bearing intervals. The Acadian clastic wedge is a thick, highly-lenticular package of interfingering fluvial-deltaic sandstones, siltstones, and shales. Geologic analyses of more than 800 wells resulted in a geologic/engineering characterization of seven separate stratigraphic intervals. For both study areas, well log and other data were analyzed to determine regional reservoir distribution, reservoir thickness, lithology, porosity, water saturation, pressure and temperature. These data were mapped, evaluated, and compiled into various TGAS data sets that reflect estimates of original gas-in-place, remaining reserves, and 'tight' reserves. The maps and data produced represent the first basin-wide geologic characterization for either interval. This report outlines the methods and assumptions used in creating the TGAS data input, and provides basic geologic perspective on the gas-bearing sandstones of the Medina interval and the Acadian clastic wedge.
Book Synopsis An Integrated Seismic and Well Log Analysis for the Estimation of Reservoir Properties by : Muhammad M. Saggaf
Download or read book An Integrated Seismic and Well Log Analysis for the Estimation of Reservoir Properties written by Muhammad M. Saggaf and published by . This book was released on 2000 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Unconventional Reservoir Parameter Estimation by Seismic Inversion and Machine Learning of the Bakken Formation, North Dakota by : Jackson Ray Tomski
Download or read book Unconventional Reservoir Parameter Estimation by Seismic Inversion and Machine Learning of the Bakken Formation, North Dakota written by Jackson Ray Tomski and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research reported in this thesis focuses on the prediction of reservoir parameters and their uncertainties. The thesis comprises two studies. In the first part, I focus on quantitative and seismic interpretation problem, where I describe a workflow for estimation of porosity using the results from pre-stack seismic inversion. The second part focuses on the production problem, where I establish a relationship between completion parameters and production given a production dataset from the Bakken Formation. In the first study, I characterize the unconventional reservoir of the Bakken Formation, specifically within northwest North Dakota using 3D seismic and well log data. I employ seismic inversion followed by application of a Bayesian Neural Network to predict total porosity across the entire seismic volume given an estimated volume of P-impedance. The Bayesian Neural Network utilizes Markov Chain Monte Carlo via Langevin Dynamics in order to sample from the probability distribution and to estimate uncertainity. This method establishes a good correlation between estimated P-impedance from seismic inversion and total porosity from well data. By integrating these techniques, a better understanding of the parameters useful for reservoir characterization is possible given a degree of uncertainity thereby improving oil and gas exploration and risk assessment. In this second study, I make use of a production dataset of the Bakken Formation to identify production patterns in the field to establish a relationship between completion parameters and production. A random forest model is employed alongside the Bayesian Neural Network model to predict production given a set of predictive features found through a series of feature selection methods. I then aim to create various training and testing dataset scenarios through random sampling and clustering. I do this in order to reduce the sampling bias and ensure that the machine learning models are being trained and tested on data coming from similar geological regions with similar production rate values. With the integration of these techniques, a better understanding of the parameters useful for optimizing oil production is possible with a degree of uncertainity when using the Bayesian Neural Network
Book Synopsis Prediction of Reservoir Properties for Geomechanical Analysis Using 3-D Seismic Data and Rock Physics Modeling in the Vaca Muerta Formation, Neuquén Basin, Argentina by : Carlos Convers
Download or read book Prediction of Reservoir Properties for Geomechanical Analysis Using 3-D Seismic Data and Rock Physics Modeling in the Vaca Muerta Formation, Neuquén Basin, Argentina written by Carlos Convers and published by . This book was released on 2017 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pore Pressure Prediction in the Point Pleasant Formation in the Appalachian Basin, in Parts of Ohio, Pennsylvania, and West Virginia, United States of America by : Bennett Trotter
Download or read book Pore Pressure Prediction in the Point Pleasant Formation in the Appalachian Basin, in Parts of Ohio, Pennsylvania, and West Virginia, United States of America written by Bennett Trotter and published by . This book was released on 2018 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Ordovician-aged Point Pleasant Formation is an economically important unconventional oil and gas play in the Appalachian Basin, in particular Ohio, western Pennsylvanian and West Virginia. The Point Pleasant Formation is known to have overpressured pore fluids, that is, pore pressure above hydrostatic conditions. Overpressure is an important rock property to constrain because it exerts a strong control on the mechanical stability of boreholes, the response of the formation to hydraulic fracture stimulation, and volumetric flow rates of produced fluids. However, what is not well known is the spatial distribution, magnitude, and controls on the overpressure within the Point Pleasant Formation. In this study, pore pressure in the Point Pleasant Formation is estimated based on sonic velocity geophysical logs measured in 33 wells as well as mudweight data from 23 wells. From this analysis, a map of overpressure in the Point Pleasant identifies a large area of overpressure centered in southeastern Ohio primarily within the counties of Noble, Monroe, and Washington . This overpressure map may facilitate target selection, safer drilling, and more successful well completions. Areas of significant overpressure have also been linked to enhanced risks of induced seismic events, thus the overpressure map may also indicate areas that have higher probability to trigger induced seismic events during hydraulic fracturing or waste water disposal.
Download or read book Petroleum Abstracts written by and published by . This book was released on 1993 with total page 1752 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Unconventional Reservoir Geomechanics by : Mark D. Zoback
Download or read book Unconventional Reservoir Geomechanics written by Mark D. Zoback and published by Cambridge University Press. This book was released on 2019-05-16 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of the key geologic, geomechanical and engineering principles that govern the development of unconventional oil and gas reservoirs. Covering hydrocarbon-bearing formations, horizontal drilling, reservoir seismology and environmental impacts, this is an invaluable resource for geologists, geophysicists and reservoir engineers.
