Determining Fracture Pressure Gradients from Well Logs

Facies classification from well logs using an inception convolutional network

The idea to use automated algorithms to determine geological facies from well logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent and dramatic increase in research in the field of machine learning makes it a good time to revisit the topic. Following an exercise proposed by Dubois et al. (2007) and Hall (2016) we employ a modern type of deep convolutional network, called...

Reservoir Properties from Well Logs using neural Networks

Development of an Intelligent System to Synthesize Petrophysical Well Logs

Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. There are three main petrophysical...

Automatically tying well logs to seismic data

Seismic data are recorded and commonly interpreted in vertical two-way time; well logs, measured in depth, must be tied to seismic using a time-depth curve. However, well ties contain a large amount of uncertainty due to errors in the generation of synthetic seismograms and manual matching of synthetic seismograms to seismic traces. Using dynamic time warping, a fast algorithm that optimally al...

Simultaneous correlation of multiple well logs

Well log correlation is an important step in geophysical interpretation, but as the number of wells increases, so does the complexity of the correlation process. We propose a new method for automatic and simultaneous well log correlation that provides a globally optimal alignment of all logs, and in addition, is relatively insensitive to large measurement errors common in well logs. First, for ...