Probabilistic geothermal resource assessment in Maichen Sag, south China
It is crucial for financial providers, investment groups, resource developers, and exploration companies to rate new geothermal projects in terms of resources and reserves. In general, the existing volumetric method is constrained by limited information when projects are at the early stage of develo...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.972125/full |
| _version_ | 1798000353001603072 |
|---|---|
| author | Mingchuan Wang Fan Yang Ying Zhang Dianwei Zhang Jianyun Feng Jun Luo Yan Zeng |
| author_facet | Mingchuan Wang Fan Yang Ying Zhang Dianwei Zhang Jianyun Feng Jun Luo Yan Zeng |
| author_sort | Mingchuan Wang |
| collection | DOAJ |
| description | It is crucial for financial providers, investment groups, resource developers, and exploration companies to rate new geothermal projects in terms of resources and reserves. In general, the existing volumetric method is constrained by limited information when projects are at the early stage of development. The main objective of this study is to estimate the probabilistic potential thermal energy of the M research area in the Maichen Sag, a geothermal prospect in South China, through stochastic methodologies. The probabilistic assessment methodology provides a way to embody the uncertainty and risk in geothermal projects and to quantify the power potential in a probable range. In this study, proxy numerical models were built by combining the Experimental Design (ED) and Response Surface Methodology (RSM) with the Monte Carlo Simulation technique. An improved workflow for combined ED-RSM that uses two-level Full Factorial and Box–Behnken designs was proposed. For comparative analysis, the typical volumetric technique was also implemented in this study. The ED-RSM results show that the M area has P10, P50, and P90 reserves of 5.7 × 1014 J, 5.3 × 1014 J, and 5 × 1014 J, respectively, and these numbers from the typical volumetric method are 1.5 × 1015 J, 9 × 1014 J, and 5.1 × 1014 J, respectively. In this study, the operability, applicability, and accessibility of ED-RSM in the assessment of geothermal potential and its ability to provide a reliable output are demonstrated. |
| first_indexed | 2024-04-11T11:17:40Z |
| format | Article |
| id | doaj.art-d34e2a69ea364ab7976926365dcdde3f |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-11T11:17:40Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-d34e2a69ea364ab7976926365dcdde3f2022-12-22T04:27:09ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-09-011010.3389/feart.2022.972125972125Probabilistic geothermal resource assessment in Maichen Sag, south ChinaMingchuan WangFan YangYing ZhangDianwei ZhangJianyun FengJun LuoYan ZengIt is crucial for financial providers, investment groups, resource developers, and exploration companies to rate new geothermal projects in terms of resources and reserves. In general, the existing volumetric method is constrained by limited information when projects are at the early stage of development. The main objective of this study is to estimate the probabilistic potential thermal energy of the M research area in the Maichen Sag, a geothermal prospect in South China, through stochastic methodologies. The probabilistic assessment methodology provides a way to embody the uncertainty and risk in geothermal projects and to quantify the power potential in a probable range. In this study, proxy numerical models were built by combining the Experimental Design (ED) and Response Surface Methodology (RSM) with the Monte Carlo Simulation technique. An improved workflow for combined ED-RSM that uses two-level Full Factorial and Box–Behnken designs was proposed. For comparative analysis, the typical volumetric technique was also implemented in this study. The ED-RSM results show that the M area has P10, P50, and P90 reserves of 5.7 × 1014 J, 5.3 × 1014 J, and 5 × 1014 J, respectively, and these numbers from the typical volumetric method are 1.5 × 1015 J, 9 × 1014 J, and 5.1 × 1014 J, respectively. In this study, the operability, applicability, and accessibility of ED-RSM in the assessment of geothermal potential and its ability to provide a reliable output are demonstrated.https://www.frontiersin.org/articles/10.3389/feart.2022.972125/fullsouth Chinamaichen sagprobabilistic geothermal energy assessmentexperimental designresponse surface methodologytemperature field |
| spellingShingle | Mingchuan Wang Fan Yang Ying Zhang Dianwei Zhang Jianyun Feng Jun Luo Yan Zeng Probabilistic geothermal resource assessment in Maichen Sag, south China Frontiers in Earth Science south China maichen sag probabilistic geothermal energy assessment experimental design response surface methodology temperature field |
| title | Probabilistic geothermal resource assessment in Maichen Sag, south China |
| title_full | Probabilistic geothermal resource assessment in Maichen Sag, south China |
| title_fullStr | Probabilistic geothermal resource assessment in Maichen Sag, south China |
| title_full_unstemmed | Probabilistic geothermal resource assessment in Maichen Sag, south China |
| title_short | Probabilistic geothermal resource assessment in Maichen Sag, south China |
| title_sort | probabilistic geothermal resource assessment in maichen sag south china |
| topic | south China maichen sag probabilistic geothermal energy assessment experimental design response surface methodology temperature field |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.972125/full |
| work_keys_str_mv | AT mingchuanwang probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT fanyang probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT yingzhang probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT dianweizhang probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT jianyunfeng probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT junluo probabilisticgeothermalresourceassessmentinmaichensagsouthchina AT yanzeng probabilisticgeothermalresourceassessmentinmaichensagsouthchina |