Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada

Estimating permeability from well log information in uncored borehole intervals is an important yet difficult task encountered in many earth science disciplines. Most commonly, permeability is estimated from various well log curves using either empirical relationships or some form of multiple linear regression (MLR). More sophisticated, multiple nonlinear regression (MNLR) techniques are not as common because of difficulties associated with choosing an appropriate mathematical model and with analyzing the sensitivity of the chosen model to the various input variables. However, the recent development of a class of nonlinear optimization techniques known as artificial neural networks (ANNs) does much to overcome these difficulties. We use a back‐propagation ANN (BP-ANN) to model the interrelationships between spatial position, six different well logs, and permeability. Data from four wells in the Venture gas field (offshore eastern Canada) are organized into training and supervising data sets for BP-ANN modeling. Data from a fifth well in the same field are retained as an independent data set for testing. When applied to this test data, the trained BP-ANN produces permeability values that compare well with measured values in the cored intervals. Permeability profiles calculated with the trained BP-ANN exhibit numerous low permeability horizons that are correlatable between the wells at Venture. These horizons likely represent important, intra‐reservoir barriers to fluid migration that are significant for future reservoir production plans at Venture. For discussion, we also derive predictive equations using conventional statistical methods (i.e., MLR, and MNLR) with the same data set used for BP-ANN modeling. These examples highlight the efficacy of BP-ANNs as a means of obtaining multivariate, nonlinear models for difficult problems such as permeability estimation.