Showing papers by "Akhil Datta-Gupta published in 1996"

Optimal transformations for multiple regression : Application to permeability estimation from well logs

Abstract: Conventional multiple regression for permeability estimation from well logs requires a functional relationship to be presumed. Due to the inexact nature of the relationship between petrophysical variables, it is not always possible to identify the underlying functional form between dependent and independent variables in advance. When large variations in petrological properties are exhibited, parametric regression often fails or leads to unstable and erroneous results, especially for multivariate cases. In this paper we describe a nonparametric approach for estimating optimal transformations of petrophysical data to obtain the maximum correlation between observed variables. The approach does not require a priori assumptions of a functional form and the optimal transformations are derived solely based on the data set. An iterative procedure involving the alternating conditional expectation (ACE) forms the basis of our approach. The power of ACE is illustrated using synthetic as well as field examples. The results clearly demonstrate improved permeability estimation by ACE compared to conventional parametric regression methods.

Utilizing crosswell, single well and pressure transient tests for characterizing fractured gas reservoirs

Abstract: As part of its Department of Energy (DOE)/Industry cooperative program in oil and gas, Berkeley Lab has an ongoing effort in cooperation with Conoco and Amoco to develop equipment, field techniques, and interpretational methods to further the practice of characterizing naturally fractured, heterogeneous reservoirs The focus of the project is an interdisciplinary approach, involving geology, rock physics, geophysics, and reservoir engineering The goal is to combine the various methods into a unified approach for predicting fluid migration

A new approach to seismic data integration during reservoir characterization using optimal non‐parametric transformations

Abstract: We propose a two-stage approach to integrating seismic data into reservoir characterization. First, we use a non-parametric approach to calibrate the seismic and well data through an optimal transformation to obtain the maximal correlation between two data sets. These optimal transformations are totally data-driven and do not assume any a priori functional relationship. Next, cokriging or stochastic cosimulation is carried out in the transformed space to generate conditional realizations of reservoir properties. The proposed approach allows for non-linearity between reservoir properties and seismic attributes and exploits the secondary data to its fullest potential. Furthermore, cokriging or cosimulation is considerably simplified when carried in conjunction with the optimal transformations because of a significant reduction in the variance function calculations particularly when multiple seismic attributes are involved. The proposed approach has been applied to synthetic as well as field examples. The synthetic examples involve reproducing a pre-generated primary data set using sparse primary and multiple dense secondary data sets. A comparison with traditional kriging and cokriging is also presented to illustrate the superiority of our proposed approach. The field example uses 3-D seismic and well log data from a 2 mi{sup 2} area of the Stratton gas field in South Texas --more » a fluvial reservoir system. Using multiple seismic attributes in conjunction with well data, we estimate pore-footage distribution for a selected zone in the middle Frio formation.« less

A New Approach to Seismic Data Integration During Reservoir Characterization Using Optimal non-Parametric Transformations

Abstract: We propose a two-stage approach to integrating seismic data into reservoir characterization. First, we use a non-parametric approach to calibrate the seismic and well data through an optimal transformation to obtain the maximal correlation between two data sets. These optimal transformations are totally data-driven and do not assume any a priori functional relationship. Next, cokriging or stochastic cosimulation is carried out in the transformed space to generate conditional realizations of reservoir properties. The proposed approach allows for non-linearity between reservoir properties and seismic attributes and exploits the secondary data to its fullest potential. Furthermore, cokriging or cosimulation is considerably simplified when carried in conjunction with the optimal transformations because of a significant reduction in the variance function calculations particularly when multiple seismic attributes are involved. The proposed approach has been applied to synthetic as well as field examples. The synthetic examples involve reproducing a pre-generated primary data set using sparse primary and multiple dense secondary data sets. A comparison with traditional kriging and cokriging is also presented to illustrate the superiority of our proposed approach. The field example uses 3-D seismic and well log data from a 2 mi{sup 2} area of the Stratton gas field in South Texas --more » a fluvial reservoir system. Using multiple seismic attributes in conjunction with well data, we estimate pore-footage distribution for a selected zone in the middle Frio formation.« less