Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas
The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Geosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3263/15/1/3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588429117882368 |
|---|---|
| author | Osareni C. Ogiesoba Fritz C. Palacios |
| author_facet | Osareni C. Ogiesoba Fritz C. Palacios |
| author_sort | Osareni C. Ogiesoba |
| collection | DOAJ |
| description | The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having <1% porosity and characterized by PEF values of >4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E. |
| format | Article |
| id | doaj-art-96ff5da9625a457a83c0d0139ce6345e |
| institution | Kabale University |
| issn | 2076-3263 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj-art-96ff5da9625a457a83c0d0139ce6345e2025-01-24T13:34:05ZengMDPI AGGeosciences2076-32632024-12-01151310.3390/geosciences15010003Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West TexasOsareni C. Ogiesoba0Fritz C. Palacios1Bureau of Economic Geology, The University of Texas at Austin, Austin, TX 78713, USABureau of Economic Geology, The University of Texas at Austin, Austin, TX 78713, USAThe photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having <1% porosity and characterized by PEF values of >4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E.https://www.mdpi.com/2076-3263/15/1/3photoelectric factorcarbonatesshalemachine-learningneural networkmultiattributes |
| spellingShingle | Osareni C. Ogiesoba Fritz C. Palacios Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas Geosciences photoelectric factor carbonates shale machine-learning neural network multiattributes |
| title | Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas |
| title_full | Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas |
| title_fullStr | Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas |
| title_full_unstemmed | Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas |
| title_short | Photoelectric Factor Characterization of a Mixed Carbonate and Siliciclastic System Using Machine-Learning Methods: Pennsylvanian Canyon and Strawn Reef Systems, Midland Basin, West Texas |
| title_sort | photoelectric factor characterization of a mixed carbonate and siliciclastic system using machine learning methods pennsylvanian canyon and strawn reef systems midland basin west texas |
| topic | photoelectric factor carbonates shale machine-learning neural network multiattributes |
| url | https://www.mdpi.com/2076-3263/15/1/3 |
| work_keys_str_mv | AT osarenicogiesoba photoelectricfactorcharacterizationofamixedcarbonateandsiliciclasticsystemusingmachinelearningmethodspennsylvaniancanyonandstrawnreefsystemsmidlandbasinwesttexas AT fritzcpalacios photoelectricfactorcharacterizationofamixedcarbonateandsiliciclasticsystemusingmachinelearningmethodspennsylvaniancanyonandstrawnreefsystemsmidlandbasinwesttexas |