A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator
Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure t...
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| Autor*in: |
Haneef, Javed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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© King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 48(2022), 7 vom: 08. Nov., Seite 9339-9356 |
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| Übergeordnetes Werk: |
volume:48 ; year:2022 ; number:7 ; day:08 ; month:11 ; pages:9339-9356 |
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| DOI / URN: |
10.1007/s13369-022-07436-7 |
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| Katalog-ID: |
SPR051968495 |
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| 520 | |a Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. | ||
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10.1007/s13369-022-07436-7 doi (DE-627)SPR051968495 (SPR)s13369-022-07436-7-e DE-627 ger DE-627 rakwb eng Haneef, Javed verfasserin (orcid)0000-0001-8901-2267 aut A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 Sheraz, Assad aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 7 vom: 08. Nov., Seite 9339-9356 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:7 day:08 month:11 pages:9339-9356 https://dx.doi.org/10.1007/s13369-022-07436-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 7 08 11 9339-9356 |
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10.1007/s13369-022-07436-7 doi (DE-627)SPR051968495 (SPR)s13369-022-07436-7-e DE-627 ger DE-627 rakwb eng Haneef, Javed verfasserin (orcid)0000-0001-8901-2267 aut A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 Sheraz, Assad aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 7 vom: 08. Nov., Seite 9339-9356 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:7 day:08 month:11 pages:9339-9356 https://dx.doi.org/10.1007/s13369-022-07436-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 7 08 11 9339-9356 |
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10.1007/s13369-022-07436-7 doi (DE-627)SPR051968495 (SPR)s13369-022-07436-7-e DE-627 ger DE-627 rakwb eng Haneef, Javed verfasserin (orcid)0000-0001-8901-2267 aut A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 Sheraz, Assad aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 7 vom: 08. Nov., Seite 9339-9356 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:7 day:08 month:11 pages:9339-9356 https://dx.doi.org/10.1007/s13369-022-07436-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 7 08 11 9339-9356 |
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10.1007/s13369-022-07436-7 doi (DE-627)SPR051968495 (SPR)s13369-022-07436-7-e DE-627 ger DE-627 rakwb eng Haneef, Javed verfasserin (orcid)0000-0001-8901-2267 aut A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 Sheraz, Assad aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 7 vom: 08. Nov., Seite 9339-9356 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:7 day:08 month:11 pages:9339-9356 https://dx.doi.org/10.1007/s13369-022-07436-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 7 08 11 9339-9356 |
| allfieldsSound |
10.1007/s13369-022-07436-7 doi (DE-627)SPR051968495 (SPR)s13369-022-07436-7-e DE-627 ger DE-627 rakwb eng Haneef, Javed verfasserin (orcid)0000-0001-8901-2267 aut A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 Sheraz, Assad aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 48(2022), 7 vom: 08. Nov., Seite 9339-9356 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:48 year:2022 number:7 day:08 month:11 pages:9339-9356 https://dx.doi.org/10.1007/s13369-022-07436-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2022 7 08 11 9339-9356 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051968495</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230621064813.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230621s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13369-022-07436-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051968495</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13369-022-07436-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Haneef, Javed</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8901-2267</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drilling KPI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drilling well complexities</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-productive time KPI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Design well complexity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Well complexity calculator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geological well complexity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sheraz, Assad</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The Arabian journal for science and engineering</subfield><subfield code="d">Berlin : Springer, 2011</subfield><subfield code="g">48(2022), 7 vom: 08. 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Haneef, Javed |
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Haneef, Javed misc Drilling KPI misc Drilling well complexities misc Non-productive time KPI misc Design well complexity misc Well complexity calculator misc Geological well complexity A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator |
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A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator Drilling KPI (dpeaa)DE-He213 Drilling well complexities (dpeaa)DE-He213 Non-productive time KPI (dpeaa)DE-He213 Design well complexity (dpeaa)DE-He213 Well complexity calculator (dpeaa)DE-He213 Geological well complexity (dpeaa)DE-He213 |
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comparative analysis of well key performance indicators (kpis) with well complexities using well complexity calculator |
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A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator |
| abstract |
Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Drilling the oil and gas well is the most important job for finding the hydrocarbon resources below the earth. Many risks/complexities are involved in fulfilling this complex job. Oil and gas well drilling companies are using different types of key performance indicators (KPIs) to measure the performance of the drilling operations, for example, time versus depth, rate of penetration, non-productive time, etc. There is no standardization or benchmarking available for comparing these KPIs. Drilling companies are using different references for these KPIs as per their policies or norms. This research paper presents the application of the well complexity calculator, which is developed on a broader range of parameters to calculate different types of complexities. These complexities can be used as a standardized reference and the KPIs are compared with them. For this purpose, drilled wells camouflaged data obtained from different oil and gas well drilling companies in Pakistan, which were used to measure their complexities by the well complexity calculator. Accordingly, relationships of these complexities are presented against different industry-wide used Drilling KPIs achieved on the wells, like Dry Hole Drilling Days, Feet per Day, Dry Hole Drilling Days per 10 K Feet, Dry Hole Drilling Cost, NPT Percentage, and Non-Productive Time (NPT). It is found that with the increase of well complexities, all the KPIs showed an increase/decrease in specific trends; except NPT Percentage, this trend verifies the authenticity of the well complexity calculator. NPT Percentage did not show any relationship against the well complexities, although it is one of the widely used Drilling KPIs. Besides this, a comparison of one of the Absolute Drilling KPIs “Dry Hole Drilling Cost” and one of the Normalized Drilling KPI “Dry Hole Drilling Cost Per Foot” is also compared against the well complexities, and trends confirm that the use of such normalized KPI alone is only acceptable when design and all the properties of the Wells are similar, which is usually not the case. It is also found that instead of using NPT Percentage or single-factor normalized KPIs, Absolute Drilling KPIs like Non-Productive Time recorded in hours, Dry Hole Drilling Days, and Dry Hole Drilling Cost vs Well Complexities are better tools to compare the performance of the Wells, since the ratio of output is not taken against the single-input parameter like depth but instead against the combination of different types of parameters. Based on Complexities vs the KPI relationship, an improved modular approach is presented to carry out KPI benchmarking and performance evaluation and comparison. © King Fahd University of Petroleum & Minerals 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
A Comparative Analysis of Well Key Performance Indicators (KPIs) with Well Complexities Using Well Complexity Calculator |
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https://dx.doi.org/10.1007/s13369-022-07436-7 |
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| score |
7.399976 |