The relationships between thermal conductivity and other petrophysical properties have been analysed for a borehole drilled in a Tertiary Flysch sequence. We establish equations that permit us to predict rock thermal conductivity from logging data. A regression analysis of thermal conductivity, bulk density, and sonic velocity yields thermal conductivity with an average accuracy of better than ...

The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences, due to the costs inherent to drilling wells,...

In this paper we compare three different soft computing methods used as the well log data analysis model in petroleum engineering. Due to the diversely behaving nature, namely, that each region has a unique geophysical characteristic it is difficult to build a universal model relating the mathematical behaviour of the measured variables. This is the reason why soft computing techniques may be f...

Subsurface rock strata are often conceptualized in three dimensions as depth surfaces, or horizons. By picking the corresponding reflection event from 3-D seismic data, an approximate “seismic horizon” can be obtained, but in general the inconsistencies in the approximation are nontrivial. Picks from well log data give the depth to the horizon to high accuracy, but only at a few points. To achi...

Well log data analysis plays an important role in petroleum exploration. It is used to identify the potential for oil production at a given source and so forms the basis for the estimation of financial returns and economic benefits. In recent years, many computational intelligence techniques such as backpropagation neural networks (BPNN) and fuzzy systems have been applied to perform the task. ...