Non-parametric regression and neural-network infill drilling recovery models for carbonate reservoirs Academic Article uri icon

abstract

  • This work introduces non-parametric regression and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in West Texas. Development of the oil recovery forecast models helps understand the relative importance of dominant reservoir characteristics and operations variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological settings, and make operations decisions. The variety of applications demands the creation of multiple recovery forecast models. One of the significant constraints for the model development is the limited number of field data that are inexact and often exhibit uncertain relationships. The inexact and uncertain relationship may also encompass a large number of possible independent variables. This situation mandates proper selection of independent variables for the infill drilling recovery model. Non-parametric regression and multivariate principal component analysis are used to identify the dominant and the optimum number of independent variables. The advantage of the non-parametric regression is easy to use and can quickly provide results that reveal the dominant independent variables and relative characteristics of the relationships. The disadvantage is retaining a large variance of forecast results for a particular data set. The insight of interdependency of the variables gained in non-parametric regression and multivariate principal component analysis is employed to develop an effective neural network. The neural network infill drilling recovery model is capable of forecasting the oil recovery with less error variance. This work shows that a multiple use of various modeling techniques may provide a healthy interaction between the different approaches and thereby, a better oil recovery forecast. (C) 2000 Elsevier Science Ltd. All rights reserved.

published proceedings

  • Computers & Geosciences

author list (cited authors)

  • Wu, C. H., Soto, R. D., Valko, P. P., & Bubela, A. M.

publication date

  • January 1, 2000 11:11 AM