Prediction of Reservoir Architecture and Porosity Distribution Using Multiple Seismic Attributes and Neural Networks

Abstract

Abstract This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas and net pay and reservoir property distribution in the Zafiro Field, offshore Equatorial Guinea. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional methods of estimating rock properties from seismic data, such as seismic trace inversion, amplitude mapping, and AVO studies. Our procedure is straight forward but does require careful quality control to insure reliable predictions from the seismic data. Network training, test, and validation data sets provide calibration of seismic attributes with well log data, optimize the network parameters, and estimate the performance of the system to predict hidden representative data. Comprehensive statistical methods and interpretation/subjective measures insure that only attributes providing true relationships and a physical basis are used in the prediction of porosity from seismic attributes. The result is a 3-D volume of seismicly derived rock properties for the reservoir 'interval of interest. In effect, we are transforming the seismic trace attributes into seismic-scale petrophysical logs. The advantage of this transformation is the additional interwell information this method provides. The additional reservoir detail allows for optimum placement of horizontal wells and improved field development. Introduction Two study areas are presented. The first is the Pegasus Field located 25 miles south of Midland, Texas in the southwestern portion of what is now the Midland basin. Hydrocarbons are structurally trapped at Pegasus in a northeast to southwest trending faulted anticline, 7 miles long (N-S) by 4 miles wide (E-W). The Devonian reservoir, at a depth of 11,500 feet, is one of six producing intervals that range in depth from 5,500 to 13,000 feet. The upper half of the Devonian is dominantly limestone, with minor chert and dolomite, and has low-porosity pay within an overall nonporous interval. The lower half of the Devonian is dominantly composed of chert, with minor dolomite and limestone, and contains the high porosity reservoir interval. Reservoir fluid type is retrograde gas condensate; i.e., the condensate is produced in conjunction with a wet gas phase when reservoir pressure is maintained at a sufficiently high level. No distinct fluid contacts are encountered in the wells, but the condensate yield (BCPD/MCFGPD) and water saturation increase down the flanks of the structure. The Devonian pay interval at Pegasus field provides a data-rich case study in which core, log, and seismic were interpreted in an integrated fashion to construct a sequence stratigraphic framework, within which seismic attributes and inversion models could be interpreted. The goals of this study were to identify the Devonian sequence stratigraphic framework and the porosity distribution within these siliceous carbonates, to ascertain the seismic signature of the Devonian reservoir interval, and to attempt to quantify, through the use of seismic inversion and multiple seismic attributes, the porosity distribution. If successful, this would allow mapping of individual reservoir units, a better prediction of porous reservoir in new wells, and optimum placement of horizontal wells.