GEOSCIENCE | download brochure
Enthought has a great deal of experience working with scientists and engineers from the oil and gas industry. Services offered to these clients range from customized on-site training to extensive consultation contracts and tailor-made software. Major clients in this field have included Shell and ConocoPhillips.
Training for Geoscientists
Enthought offers a variety of training courses for geoscientists. Python for Geophysicists is one of our most popular choices. Other focused sessions, such as Scientific Computing with Python, are available to those working in geomechanics, engineering, or modeling. On-site customized courses can also be arranged.
Geoscience Custom Applications
CSEM data calibration This tool was built so that our client, Shell, would no longer have to manually manipulate spreadsheets and Matlab scripts to calibrate data from a putative earth model. Our tool lets the user explore his datasets through tables, maps, and graphs while applying separate calibrations to different subsets of the data. Calibrated data can then be inverted to get an earth model.
Pore pressure prediction CoPEG, an application developed for ConocoPhillips, lets the user calibrate their prediction model to calculate appropriate counter-pressure. Using 1D well log data, the application interpolates the pressure for the depth of the exising well. After adding data from several more wells along with seismic data for a 2D line or 3D cube, CoPEG can predict the pressure at any point in the data space. Data can be viewed simultaneously in one, two or three dimensions.
AVA-based risking Enthought's simulation and risking software, ProAVA, was also developed for ConocoPhillips. The software provides a highly interactive environment for "occasional" investigators to research drilling prospects. However, its extensible framework also allows expert users to define new geologic models and algorithms based on their own theoretical understanding. The properties of each lithographic layer in the model can be generated programatically or from imported well logs. ProAVA then uses Monte Carlo techniques and Bayesian probability theory to estimate the probabilities of finding oil, natural gas, or salt water in the layer of interest.
