Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Solomon Asante-Okyere,Xiangfeng Zhu,Yao Yevenyo Ziggah,Mercy Moses Rulegeya,Chuanbo Shen

doi:10.3390/en11123261

Solomon Asante-Okyere, Xiangfeng Zhu + Show 3 more

Open Access

https://doi.org/10.3390/en11123261

Copy DOI

Abstract

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

Highlights

The focus of oil and gas exploration in complex and challenging fields has intensively increased in recent years [1]
Based on the computational benefits realized in the aforementioned discussion, the main aim of this study is to extend the application of Gaussian process regression (GPR) and assess its viability as a predictive intelligent model for the evaluation of reservoir porosity and permeability from petrophysical well logs
The suitability of GPR technique was evaluated for the prediction of reservoir porosity and permeability

Summary

Introduction

The focus of oil and gas exploration in complex and challenging fields has intensively increased in recent years [1]. These complex hydrocarbon fields exhibit a high degree of heterogeneity and non-uniform spatial distribution of reservoir properties [2,3]. The characterization of such reservoir properties like porosity and permeability cannot be accurately accomplished by developing empirical models from well log parameters since they are inadequate to fully account for the heterogeneous nature of the complex well conditions. This is the major reason why the petroleum industry has embraced the application of computational intelligence techniques, in reservoir characterization, as a result of its Energies 2018, 11, 3261; doi:10.3390/en11123261 www.mdpi.com/journal/energies

Objectives

Results

Conclusion