BALCH, ROBERT, WILLIAM WEISS, and SHAOCHANG WO, Petroleum Recovery Research Center, New Mexico Tech, Socorro, New Mexico
ABSTRACT: Core Porosity Prediction Using Wire-Line Logs, Case Study: Dagger Draw Field, New Mexico
At Dagger Draw it is difficult to arrive at reliable estimates of total pore volume in the vuggy dolomitic reservoir. Correlating wire-line logs to core porosity using a neural network generated a "ground truth" porosity estimator for the reservoir. Well logs in the field include CAL, DRHO, GR, LLD, ILLS, MSFL, NPHI, PEF, RHOB, and values of DPHI, PHI-L (liquid phase porosity) and PHI-G (gas phase porosity) were calculated. Log values were aligned with the center point of laboratory analyzed core samples in five cored wells. Neural network solutions generated using four of the five wells to blindly predict the fifth well validated results. Each well was excluded and tested in this fashion.
Fuzzy ranking was used to select logs for correlating with core porosity, and also indicated that the problem was complex, and guided neural network design. The seven best logs were used as inputs, and results show that the neural network can describe the complex relationships between the wire-line data and core porosity. Regression relationship were able to predict the excluded wells at up to CC=0.80. These results are a great improvement on the linear equations previously used for estimating porosity at Dagger Draw.
The use of artificial intelligence to generate core porosity data using only wire-line log data can provide data that could only be attained previously using expensive cores. The resulting correlation tool, if applied at hundreds of wells within the field without core data, would provide the basis for geostatistically derived pore volume maps.
AAPG Search and Discovery Article #90915©2000 AAPG Rocky Mountain Section, Albuquerque, New Mexico