An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression
In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental...
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MDPI AG
2023-02-01
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1639 |
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| author | Gang Hui Fei Gu Junqi Gan Erfan Saber Li Liu |
| author_facet | Gang Hui Fei Gu Junqi Gan Erfan Saber Li Liu |
| author_sort | Gang Hui |
| collection | DOAJ |
| description | In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental findings are thoroughly evaluated. The relationship between shale gas production and reservoir parameters is assessed using multiple linear regression (MLR). Three hundred and five core samples from fifteen wells were later examined using the MLR technique to discover the fundamental controlling characteristics of shale potential. Quartz, clay, and calcite were found to comprise the bulk of the Duvernay shale. The average values for the effective porosity and permeability were 3.96% and 137.2 nD, respectively, whereas the average amount of total organic carbon (TOC) was 3.86%. The examined Duvernay shale was predominantly deposited in a gas-generating timeframe. As input parameters, the MLR method calculated the components governing shale productivity, including the production index (PI), gas saturation (S<sub>g</sub>), clay content (V<sub>cl</sub>), effective porosity (F), total organic carbon (TOC), brittleness index (BI), and brittle mineral content (BMC) (BMC). Shale gas output was accurately predicted using the MLR-based prediction model. This research may be extended to other shale reservoirs to aid in the selection of optimal well sites, resulting in the effective development of shale resources. |
| first_indexed | 2024-03-11T08:54:12Z |
| format | Article |
| id | doaj.art-bb9fdf8fed82442aac7aefa66c64af6c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:54:12Z |
| publishDate | 2023-02-01 |
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| series | Energies |
| spelling | doaj.art-bb9fdf8fed82442aac7aefa66c64af6c2023-11-16T20:15:40ZengMDPI AGEnergies1996-10732023-02-01164163910.3390/en16041639An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear RegressionGang Hui0Fei Gu1Junqi Gan2Erfan Saber3Li Liu4State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum Beijing, Beijing 102249, ChinaResearch Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, ChinaResearch Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, ChinaSchool of Mechanical and Mining Engineering, The University of Queensland, Saint Lucia, QLD 4072, AustraliaResearch Institute of Petroleum Exploration and Development, China National Petroleum Corporation, Beijing 721002, ChinaIn the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in the absence of a full, multi-data study of reservoir characterization. To forecast Duvernay shale gas production in the vicinity of Fox Creek, Alberta, the multi-scale experimental findings are thoroughly evaluated. The relationship between shale gas production and reservoir parameters is assessed using multiple linear regression (MLR). Three hundred and five core samples from fifteen wells were later examined using the MLR technique to discover the fundamental controlling characteristics of shale potential. Quartz, clay, and calcite were found to comprise the bulk of the Duvernay shale. The average values for the effective porosity and permeability were 3.96% and 137.2 nD, respectively, whereas the average amount of total organic carbon (TOC) was 3.86%. The examined Duvernay shale was predominantly deposited in a gas-generating timeframe. As input parameters, the MLR method calculated the components governing shale productivity, including the production index (PI), gas saturation (S<sub>g</sub>), clay content (V<sub>cl</sub>), effective porosity (F), total organic carbon (TOC), brittleness index (BI), and brittle mineral content (BMC) (BMC). Shale gas output was accurately predicted using the MLR-based prediction model. This research may be extended to other shale reservoirs to aid in the selection of optimal well sites, resulting in the effective development of shale resources.https://www.mdpi.com/1996-1073/16/4/1639unconventional shale productivitymineralogypetrophysicsgeochemistrygeomechanicsmultiple linear regression |
| spellingShingle | Gang Hui Fei Gu Junqi Gan Erfan Saber Li Liu An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression Energies unconventional shale productivity mineralogy petrophysics geochemistry geomechanics multiple linear regression |
| title | An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression |
| title_full | An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression |
| title_fullStr | An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression |
| title_full_unstemmed | An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression |
| title_short | An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression |
| title_sort | integrated approach to reservoir characterization for evaluating shale productivity of duvernary shale insights from multiple linear regression |
| topic | unconventional shale productivity mineralogy petrophysics geochemistry geomechanics multiple linear regression |
| url | https://www.mdpi.com/1996-1073/16/4/1639 |
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