Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

Adel Othman,Mohamed Fathy,Islam A Mohamed

doi:10.1007/s12145-021-00573-x

Abstract

The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

Highlights

The Sequoia field, the case study, is one of the major gas fields in both West Delta Deep Marine (WDDM) and Rosetta concessions (Fig. 1) Samuel et al (2003)
Blind-well tests were performed to the resulted volumes for quality control (QC) and assessment purposes
The second analysis method is the blind well test, in which Sequoia–D5 well was not included in the analysis for QC of estimation products

Summary

Introduction

The Sequoia field, the case study, is one of the major gas fields in both WDDM and Rosetta concessions (Fig. 1) Samuel et al (2003). Different inversion methodologies have been proposed for reservoir properties characterization to predict rock/fluid properties from seismic amplitude data. To overcome this challenge, we implemented one of the Artificial Intelligence (AI) algorithms for seismic reservoir characterization. AI is a modern branch of computer sciences. It has a wide range of applications that cover almost every aspect of our modern lifestyle. Artificial Neural Network (ANN) is one of the most promising algorithms. ANN inversion gained popularity over the last decades because of its ability to establish nonlinear relationships between the input and the target property. At well locations, it “learns” the relationships that link the target log and the seismic attributes.

Methods

Results

Conclusion