Confidence-Constrained Support Vector Regression for Geological Surface Uncertainty Modeling

Guangmin Hu,Ting Chen,Shicheng Yu

doi:10.1109/access.2020.3028932

Guangmin Hu, Ting Chen + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3028932

Copy DOI

Abstract

Reconstruction of complex geological surface is widely used in oil and gas exploration, geological modeling, geological structure analysis, and other fields. It is an important basis for data visualization and visual analysis in these fields. The complexity of geological structures, the inaccuracy and sparsity of seismic interpretation data, and the lack of tectonic morphological information can lead to uncertainty in geological surface reconstruction. The existing geological surface uncertainty characterization and uncertain reconstruction methods have a shortcoming in balancing the interpolation error of high-confidence samples and model structure risk. Based on support vector regression (SVR), a method with confidence constraints for uncertainty characterization and the modeling of geological surfaces is proposed in this article. The proposed method minimizes the structural risk by adding a regularization term representing the model complexity, integrates high-confidence samples, such as drilling data, based on confidence constraints, and utilizes well path points by assigning appropriate inequality constraints to the corresponding prediction points. The results based on a real-world fault data set show that the uncertainty envelopes and fault realizations generated by the proposed method are constrained by well observations and well paths, effectively reducing the uncertainty.

Highlights

In the field of petroleum exploration, geological surfaces are reconstructed based on drilling data, seismic interpretation data, and various constraints representing regional geological knowledge [1]
Due to the characteristics of support vector regression (SVR), the current uncertainty modeling methods based on SVR regard sample points with high confidence as points in insensitive band or outliers; the information associated with those points cannot be effectively utilized
To effectively integrate well data, this article propose a confidence-constrained support vector interval regression (CCSVIR) model that is used to generate envelopes representing the uncertainty of geological surface

Summary

Introduction

In the field of petroleum exploration, geological surfaces are reconstructed based on drilling data, seismic interpretation data, and various constraints representing regional geological knowledge [1]. Such reconstructions are the basis for establishing sequence and reservoir models. This uncertainty makes it extremely difficult to construct real geological surface from seismic interpretation data and well observations [2]. SVR first maps the data x ∈ Rl into feature space H via a nonlinear function φ : Rl → H. To tolerate a small error in fitting the given data, the -insensitive loss function n

Methods

Results

Conclusion