Locally linear embedding-based seismic attribute extraction and applications

Abstract

How to extract optimal composite attributes from a variety of conventional seismic attributes to detect reservoir features is a reservoir predication key, which is usually solved by reducing dimensionality. Principle component analysis (PCA) is the most widely-used linear dimensionality reduction method at present. However, the relationships between seismic attributes and reservoir features are non-linear, so seismic attribute dimensionality reduction based on linear transforms can’t solve non-linear problems well, reducing reservoir prediction precision. As a new non-linear learning method, manifold learning supplies a new method for seismic attribute analysis. It can discover the intrinsic features and rules hidden in the data by computing low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. In this paper, we try to extract seismic attributes using locally linear embedding (LLE), realizing inter-horizon attributes dimensionality reduction of 3D seismic data first and discuss the optimization of its key parameters. Combining model analysis and case studies, we compare the dimensionality reduction and clustering effects of LLE and PCA, both of which indicate that LLE can retain the intrinsic structure of the inputs. The composite attributes and clustering results based on LLE better characterize the distribution of sedimentary facies, reservoir, and even reservoir fluids.