Mimicking the process of manual sequence stratigraphy well correlation

Author:

Fang Huijing1,Lou Yihuai2ORCID,Zhang Bo3ORCID,Xu Huaimin4ORCID,Lu Man3

Affiliation:

1. China University of Petroleum (Beijing), College of Geoscience, Beijing 102249, China and The University of Alabama, Department of Geological Science, Tuscaloosa, Alabama 35401, USA..

2. Zhejiang Huadong Construction Engineering CO., LTD, Hangzhou 310000, China and Zhejiang University, MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Hangzhou 310000, China..

3. The University of Alabama, Department of Geological Science, Tuscaloosa, Alabama 35401, USA..

4. China University of Petroleum (Beijing), College of Geoscience, Beijing 102249, China.(corresponding author).

Abstract

Stratigraphic correlation of well logs is based on interactive interpreter-based pattern recognition. A skilled interpreter identifies similar patterns (such as upward fining and coarsening) in user-defined well sections and links them using either a conscious or subconscious stratigraphic model. This manual stratigraphic correlation of numerous wells in mature fields can be time-consuming and error-prone. To expedite the process of stratigraphic correlation, we have performed semiautomatic stratigraphic correlation of wireline logs from multiple wells using improved dynamic time warping (IDTW). IDTW uses semblance, which compares the shape of the well logs, to replace the Euclidean distance in pairwise error computation. The resulting error matrix is compatible with the lateral nonstationary variation of well logs in the same formation. The workflow begins with interpreting stratigraphic well tops on user-defined well sections that is similar to the current process of stratigraphy analysis. The interpreted wells are then treated as reference wells to aid in interpreting well tops for other wells. Necessary manual interventions are incorporated during the semiautomatic stratigraphic correlation process. We applied our method to two experimental fields: a sand-rich reservoir and a mud-rich reservoir. The applications illustrate that our method performs well in aggradational strata and successfully predicts discontinuities with manual interventions.

Funder

China National Science and Technology Major Project

Science Foundation of China University of Petroleum

National Natural Science Foundation of China

China Scholarship Council

Publisher

Society of Exploration Geophysicists

Subject

Geology,Geophysics

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