Digital Image Analysis for Petrophysical Evaluation

Master's thesis in Petroleum Engineering Economic feasibility of any field development largely depends upon its reservoir storage and flow capacity. Porosity, saturation and permeability are important parameters to determine the type and volume of hydrocarbons in place and to estimate recoverab...

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Main Author: Baig, Mirza Hassan
Format: Master Thesis
Language:English
Published: University of Stavanger, Norway 2018
Subjects:
Online Access:http://hdl.handle.net/11250/2569368
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spelling ftunivstavanger:oai:uis.brage.unit.no:11250/2569368 2023-05-15T15:39:13+02:00 Digital Image Analysis for Petrophysical Evaluation Baig, Mirza Hassan 2018-06-15 application/pdf http://hdl.handle.net/11250/2569368 eng eng University of Stavanger, Norway Masteroppgave/UIS-TN-IEP/2018; http://hdl.handle.net/11250/2569368 petroleumsteknologi petroleum engineering thin section digital image analysis porosity core permeability VDP::Teknologi: 500::Berg‑ og petroleumsfag: 510::Petroleumsteknologi: 512 Master thesis 2018 ftunivstavanger 2021-05-06T20:17:46Z Master's thesis in Petroleum Engineering Economic feasibility of any field development largely depends upon its reservoir storage and flow capacity. Porosity, saturation and permeability are important parameters to determine the type and volume of hydrocarbons in place and to estimate recoverable reserves. They are also key parameters in planning, modelling and development of a reservoir. The porosity and saturation of the reservoir can be determined with reasonable certainty through interpretation of petrophysical logs or through analysis of physical core samples. Permeability evaluation is challenging, because the definition and the scale of this measurement varies across its sources. Well logs provide an empirically derived absolute value, cores provide both scalar (Kair) and vector (Kv, Kh, vertical and horizontal) permeabilities, while the reservoir volume investigated by a well test is quite large as compared to logs or cores. Hence, porosity, saturation and permeability are often compared between its sources, and calibrated as needed. While logs based interpretation is a fast interpretation technique, getting core results can take significant time. Also, not all wells are cored, or sometimes core samples are too small to carry out the analysis, leading to a missing link between core-log integration. To improve some of the inaccuracies and limitations, ‘digital image analysis’ on core or on drill cuttings ‘thin sections’ can be a useful technique in estimating reservoir properties. Digital image analysis can provide porosity, pore size distribution, flow path tortuosity (permeability), irreducible water saturation and mineralogy of the samples. Pore space area and perimeter is also determined and can be used in studying chemical reactions in the pore wall area for improved oil recovery. This research work aims to develop novel automated digital image analysis methods for Petrophysical Evaluation, and thus overcomes some of the limitations with regards to objectivity and repeatability of traditional manual techniques. To analyze porosity of thin section images, a threshold value on pixels intensity histogram is required to separate pores response from the matrix. A set of rules have been developed to remove human subjectivity in selecting this threshold value. Correlations have been applied for permeability and tortuosity evaluation to understand reservoir flow potential. Petrographic thin section samples of reservoir rocks from 7128/6-1 well in the Barents Sea are studied. The thin section images are digitalized and analyzed using MatLab functions. Petrophysical properties, namely porosity, permeability and irreducible water saturation are quantified. In addition, some features of the pore space are quantified, including area, perimeter, major & minor axis of the pore area and orientation of the pores. The results from digital image analysis are compared against results from conventional core analysis to establish validity and limitations of thin section image interpretation technique. Master Thesis Barents Sea University of Stavanger: UiS Brage Barents Sea
institution Open Polar
collection University of Stavanger: UiS Brage
op_collection_id ftunivstavanger
language English
topic petroleumsteknologi
petroleum engineering
thin section
digital image analysis
porosity
core
permeability
VDP::Teknologi: 500::Berg‑ og petroleumsfag: 510::Petroleumsteknologi: 512
spellingShingle petroleumsteknologi
petroleum engineering
thin section
digital image analysis
porosity
core
permeability
VDP::Teknologi: 500::Berg‑ og petroleumsfag: 510::Petroleumsteknologi: 512
Baig, Mirza Hassan
Digital Image Analysis for Petrophysical Evaluation
topic_facet petroleumsteknologi
petroleum engineering
thin section
digital image analysis
porosity
core
permeability
VDP::Teknologi: 500::Berg‑ og petroleumsfag: 510::Petroleumsteknologi: 512
description Master's thesis in Petroleum Engineering Economic feasibility of any field development largely depends upon its reservoir storage and flow capacity. Porosity, saturation and permeability are important parameters to determine the type and volume of hydrocarbons in place and to estimate recoverable reserves. They are also key parameters in planning, modelling and development of a reservoir. The porosity and saturation of the reservoir can be determined with reasonable certainty through interpretation of petrophysical logs or through analysis of physical core samples. Permeability evaluation is challenging, because the definition and the scale of this measurement varies across its sources. Well logs provide an empirically derived absolute value, cores provide both scalar (Kair) and vector (Kv, Kh, vertical and horizontal) permeabilities, while the reservoir volume investigated by a well test is quite large as compared to logs or cores. Hence, porosity, saturation and permeability are often compared between its sources, and calibrated as needed. While logs based interpretation is a fast interpretation technique, getting core results can take significant time. Also, not all wells are cored, or sometimes core samples are too small to carry out the analysis, leading to a missing link between core-log integration. To improve some of the inaccuracies and limitations, ‘digital image analysis’ on core or on drill cuttings ‘thin sections’ can be a useful technique in estimating reservoir properties. Digital image analysis can provide porosity, pore size distribution, flow path tortuosity (permeability), irreducible water saturation and mineralogy of the samples. Pore space area and perimeter is also determined and can be used in studying chemical reactions in the pore wall area for improved oil recovery. This research work aims to develop novel automated digital image analysis methods for Petrophysical Evaluation, and thus overcomes some of the limitations with regards to objectivity and repeatability of traditional manual techniques. To analyze porosity of thin section images, a threshold value on pixels intensity histogram is required to separate pores response from the matrix. A set of rules have been developed to remove human subjectivity in selecting this threshold value. Correlations have been applied for permeability and tortuosity evaluation to understand reservoir flow potential. Petrographic thin section samples of reservoir rocks from 7128/6-1 well in the Barents Sea are studied. The thin section images are digitalized and analyzed using MatLab functions. Petrophysical properties, namely porosity, permeability and irreducible water saturation are quantified. In addition, some features of the pore space are quantified, including area, perimeter, major & minor axis of the pore area and orientation of the pores. The results from digital image analysis are compared against results from conventional core analysis to establish validity and limitations of thin section image interpretation technique.
format Master Thesis
author Baig, Mirza Hassan
author_facet Baig, Mirza Hassan
author_sort Baig, Mirza Hassan
title Digital Image Analysis for Petrophysical Evaluation
title_short Digital Image Analysis for Petrophysical Evaluation
title_full Digital Image Analysis for Petrophysical Evaluation
title_fullStr Digital Image Analysis for Petrophysical Evaluation
title_full_unstemmed Digital Image Analysis for Petrophysical Evaluation
title_sort digital image analysis for petrophysical evaluation
publisher University of Stavanger, Norway
publishDate 2018
url http://hdl.handle.net/11250/2569368
geographic Barents Sea
geographic_facet Barents Sea
genre Barents Sea
genre_facet Barents Sea
op_relation Masteroppgave/UIS-TN-IEP/2018;
http://hdl.handle.net/11250/2569368
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