Integrated detection and investigation of bad borehole section in the Wolfcamp Formation in the Midland Basin using machine learning, petrophysics, and core characterization
| Autor: | Shuvajit Bhattacharya, William Ambrose, Lucy Tingwei Ko, Brian Casey |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Interpretation. 10:C19-C27 |
| ISSN: | 2324-8866 2324-8858 |
| Popis: | The quality of formation evaluation and reservoir estimates depends a lot on the quality of open hole logs, which is intricately tied to the borehole geometry and stand-offs. Bad borehole sections cause interesting patterns on wireline logs that can result in multiple interpretations. We have conducted an automated identification of bad borehole sections (>50 ft [15 m] thick) in multiple wells in the Wolfcamp Formation in the Midland Basin of the United States and investigated the causalities behind the unstable borehole section. The wells impacted by bad hole conditions appear to be within a particular region of the northern Midland Basin, and the bad hole condition impacts a specific zone in the Wolfcamp D. We consider well logs as multivariate time series (or depth series), where they share interdependencies to a large extent. We use this concept and unsupervised multivariate time series clustering to automatically identify bad borehole sections. Our proposed workflow simultaneously clusters the wireline logs, resulting in a large labeled regional-scale data set that can be further used for log quality assurance, processing, and petrophysical modeling. More importantly, we investigate the reasons behind the borehole washout, integrating sedimentology and geochemical data. Thick borehole washout sections are related to the presence of a significant amount of clay (>73 wt%, a mixture of smectite and illite) in the Wolfcamp D. The results also indicate that the manual editing of wireline logs and even supervised machine learning-based log reconstruction (commonly known as a missing log prediction problem) can lead to erroneous and inconsistent interpretation of subsurface characteristics. Such exercise can be meaningless if derived models are not validated with the ground truth (preserved subsurface rocks). Geologic feature: Clay-rich unstable borehole section Log appearance: Large-to-small caliper response associated with erratic density and neutron porosity logs Features with similar appearance: Gypsum, siderite nodules, and tool failure Formation: Wolfcamp Formation Age: Wolfcampian Location: Northern Midland Basin, Texas, USA Well data: Obtained from the IHS Analysis tools: Unsupervised machine learning, core description, geochemistry, and petrophysical inversion modeling |
| Databáze: | OpenAIRE |
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