Thanks Tim. Interesting suggestion. Would you be suggesting appending the dataframes generated in the for loop to something like a pickle file?
Would there be any memory issues holding that file open while all las files are being read and appended?
Similar Posts
Loading Multiple Well Log LAS Files Using Python
ByAndy
Loading Multiple Well Log LAS Files Using Python Appending Multiple LAS Files to a Pandas Dataframe Crossplots of density vs neutron porosity from multiple wells using the Python library matplotlib. Imagae created by the author. Log ASCII Standard (LAS) files are a common Oil & Gas industry format for storing and transferring well log data. The data…
Exploring Well Log Data Using Pandas, Matplotlib, and Seaborn
ByAndy
Exploring Well Log Data Using Pandas, Matplotlib, and Seaborn An example of exploring petrophysical and well log measurements using a number of plots from Seaborn and Matplotlib Photo by Markus Spiske on Unsplash Machine learning and Artificial Intelligence are becoming popular within the geoscience and petrophysics domains. Especially over the past decade. Machine learning is a subdivision…
Visualising Well Data Coverage Using Matplotlib
ByAndy
Visualising Well Data Coverage Using Matplotlib Exploring where data is and where it isn’t Photo by Vilmos Heim on Unsplash Exploratory Data Analysis (EDA) is an integral part of Data Science. The same is true for the petrophysical domain and can often be referred to as the Log QC or data review stage of a project. It…
10 Applications of Machine Learning Within Petrophysics
ByAndy
10 Applications of Machine Learning Within Petrophysics Examples of machine learning to enhance your petrophysical workflow Photo by Dan Meyers on Unsplash Several decades of hydrocarbon exploration have led to the acquisition and storage of large quantities of well related measurements, which have been used to characterise the subsurface geology and its hydrocarbon potential. The potential…
Well Log Data Outlier Detection With Machine Learning and Python
ByAndy
Well Log Data Outlier Detection With Machine Learning and Python Identification of outliers is an essential step in the machine learning workflow Photo by Will Myers on Unsplash Outliers are anomalous points within a dataset. They are points that don’t fit within the normal or expected statistical distribution of the dataset and can occur for a variety of…
Exploratory Data Analysis with Well Log Data
ByAndy
Exploratory Data Analysis with Well Log Data Photo by Lukas Blazek on Unsplash Once data has been collated and sorted through, the next step in the Data Science process is to carry out Exploratory Data Analysis (EDA). This step allows us to identify patterns within the data, understand relationships between the features (well logs) and identify possible…