An integrated deep learning solution for petrophysics, pore pressure, and geomechanics property prediction
| Autor: | Marianne Rauch-Davies, Ehsan Zabihi Naeini, Sam Green, Iestyn Russell-Hughes |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | The Leading Edge. 38:53-59 |
| ISSN: | 1938-3789 1070-485X |
| DOI: | 10.1190/tle38010053.1 |
| Popis: | In unconventional plays, wells are drilled at an unprecedented rate. This, together with technical challenges in terms of complex stratigraphy, multiple play types, variable rock properties, and various elements of pore pressure, geomechanics, fracturing, and diagenesis, calls for more sophisticated, faster, consistent, and wider ranging analytical tools. Given the scale of the work — i.e., the number of wells — performing classical workflows for petrophysics, pore pressure, and geomechanics prediction can be impractical (if not impossible) due to turnaround considerations. Also such workflows might not use any preexisting regional studies efficiently. In principle, a machine learning approach can mitigate these shortcomings. We show that a supervised deep neural network approach can be an alternative innovative tool for petrophysical, pore pressure, and geomechanics analysis enabling the use of all the previously interpreted data to devise solutions that simultaneously integrate wide-ranging wellbore and wireline logs. Beyond that, a similar approach is taken to predict a certain number of attributes solely from seismically derived properties, which allows one to compute volumetric models. The application of such an algorithm is shown on a Permian case study in which the automatic neural-network-based algorithms achieve reasonable accuracy in a fraction of the time. |
| Databáze: | OpenAIRE |
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