A geological log data from a well in Kansas, USA is analyzed using Machine Learning (M.L.) techniques in Python. The data is overviewed, cleaned and analyzed for important patterns and relationships with which we found relationships of logs with each other and correlation of types of formations with the logs. Using this, we can eliminate the use of logs which are correlated or have no relative importance to the type of formation when we have prior geological knowledge of the area. Also, predictions of formation type were made successfully once the data is trained with the M.L. algorithms.
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View Code? Open in Web Editor NEWA geological log data from a well in Kansas, USA is analyzed using Machine Learning (M.L.) techniques in Python. The data is overviewed, cleaned and analyzed for important patterns and relationships with which we found relationships of logs with each other and correlation of types of formations with the logs. Using this, we can eliminate the use of logs which are correlated or have no relative importance to the type of formation when we have prior geological knowledge of the area. Also, predictions of formation type were made successfully once the data is trained with the M.L. algorithms.