Intelligent prediction and integral analysis of shale oil and gas sweet spots
Abstract Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Ba...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
KeAi Communications Co., Ltd.
2018
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/93464f75af3a48749f2f3b65c3af23b6 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:93464f75af3a48749f2f3b65c3af23b6 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:93464f75af3a48749f2f3b65c3af23b62021-12-02T12:03:43ZIntelligent prediction and integral analysis of shale oil and gas sweet spots10.1007/s12182-018-0261-y1672-51071995-8226https://doaj.org/article/93464f75af3a48749f2f3b65c3af23b62018-10-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-018-0261-yhttps://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.Ke-Ran QianZhi-Liang HeXi-Wu LiuYe-Quan ChenKeAi Communications Co., Ltd.articleShale reservoirMachine learningSupport vector machineSweet spot predictionScienceQPetrologyQE420-499ENPetroleum Science, Vol 15, Iss 4, Pp 744-755 (2018) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Shale reservoir Machine learning Support vector machine Sweet spot prediction Science Q Petrology QE420-499 |
| spellingShingle |
Shale reservoir Machine learning Support vector machine Sweet spot prediction Science Q Petrology QE420-499 Ke-Ran Qian Zhi-Liang He Xi-Wu Liu Ye-Quan Chen Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| description |
Abstract Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential. |
| format |
article |
| author |
Ke-Ran Qian Zhi-Liang He Xi-Wu Liu Ye-Quan Chen |
| author_facet |
Ke-Ran Qian Zhi-Liang He Xi-Wu Liu Ye-Quan Chen |
| author_sort |
Ke-Ran Qian |
| title |
Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| title_short |
Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| title_full |
Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| title_fullStr |
Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| title_full_unstemmed |
Intelligent prediction and integral analysis of shale oil and gas sweet spots |
| title_sort |
intelligent prediction and integral analysis of shale oil and gas sweet spots |
| publisher |
KeAi Communications Co., Ltd. |
| publishDate |
2018 |
| url |
https://doaj.org/article/93464f75af3a48749f2f3b65c3af23b6 |
| work_keys_str_mv |
AT keranqian intelligentpredictionandintegralanalysisofshaleoilandgassweetspots AT zhilianghe intelligentpredictionandintegralanalysisofshaleoilandgassweetspots AT xiwuliu intelligentpredictionandintegralanalysisofshaleoilandgassweetspots AT yequanchen intelligentpredictionandintegralanalysisofshaleoilandgassweetspots |
| _version_ |
1718394703173386240 |