Cased Hole
Solutions: Predicting Open
Hole
Logs Using Artifical Neural Networks
John F. Gegg
EnCana Oil and Gas USA, Denver, CO
Neural Net predicted open hole
triple combo logs (density, neutron, resistivity) have been used successfully in Jonah for reservoir characterization, net pay determination, frac staging, and OGIP calculations. Drilling and
Hole
conditioning problems in the area create poor open
hole
logging conditions, many times resulting in the inability to reach TD (12,000 + feet) with logging tools. In wells where an open
hole
logging suite is obtained, the borehole conditions adversely affect tool measurements because of borehole rugosity and tool sticking issues. These issues have been the motivation to move toward a cased
hole
logging program. The benefits have been lower costs, reduced risks, and improved vertical resolution and logging data accuracy.
The process and interpretation workflow involves training and application wells. A training well has both open hole
logging data and cased
hole
logging data. An application well has only cased
hole
data. An artificial neural network (ANN) model is developed for the training well, across a specific stratigraphic interval or geographic area, using the relationship between the open
hole
triple combo data and the cased
hole
data. Pulsed neutron logs work especially well for this because of the strong correlation between sigma, inelastic and capture count rates to resistivity, bulk density, and porosity respectively. Once developed, the ANN model can be applied to application wells that have only cased
hole
data. The ANN model results are checked against offsetting wells and normalized (if necessary) to insure the most accurate porosity and resistivity data possible. After analysis for frac staging, these data can then be integrated with other wells in the field for net pay and OGIP calculations and reservoir characterization.
AAPG Search and Discovery Article #90092©2009 AAPG Rocky Mountain Section, July 9-11, 2008, Denver, Colorado