Machine learning-based seismic spectral attribute analysis to delineate a tight-sand reservoir

(in the Sulige gas field of central Ordos Basin, western China)

Significance 

The Sulige gas field in central Ordos Basin is the largest gas field in China. The first exploratory well produced natural gas from a tight sandstone reservoir, namely the P1h8 reservoir. In the early phase of the project, 2D seismic lines were acquired to determine the gas potential. However, since 2010, 3D seismic surveys have been acquired in the Sulige gas field to better characterize reservoir heterogeneity and to optimize the placement of the expensive horizontal well. At present, horizontal wells yield more gas when compared to their vertical counterparts. Nonetheless, the number of horizontal wells is much smaller than that of vertical wells. To further enhance gas production, more horizontal wells ought to be drilled. Moreover, since massive sands at the braid bars are the potential targets for horizontal wells, it is important to better map and predict the distribution of economically viable tight sandstones throughout the field. In light of this, many high-quality 3D seismic approaches have been presented. For instance, previous reports have explored approaches such as the short time Fourier transform, continuous wavelet transform, and matching pursuit decomposition. Regardless, more is needed to delineate the subsurface depositional facies and reservoir thicknesses.

Considering the linear or nonlinear correlation between seismic spectral attribute (SSAs) and sand thickness in the Sulige gas field, one can postulate that a combination of both multiple linear regression (MLR) and radial basis function neural network (RBFNN) could yield a better optimal regression model. Bearing this in mind, researchers from the National Engineering Laboratory for Offshore Oil Exploration in China: Professor Zhiguo Wang and Professor Jinghuai Gao, in collaboration with Professor Dengliang Gao at the West Virginia University in the United States and Dr. Xiaolan Lei, and Professor Daxing Wang at the CNPC Changqing Oilfield Company (China) exploited the advantages of the combined MLR and RBFNN by applying a machine learning-based spectral attribute analysis to assist 3D seismic interpretation in tight gas reservoirs in the Sulige field, Ordos Basin, western China. Their work is currently published in the research journal, Marine and Petroleum Geology.

In their workflow, they first implemented the seismic spectral decomposition by using the continuous wavelet transform with the generalized Morse wavelets. Second, they extracted SSAs of the target reservoir of interest following which they performed multi-dimensional data analysis using the principal component analysis, thus significantly reducing the computational time and storage space for SSAs analysis and visualization. In addition, through the use of red-green-blue (RGB) blending technique, the team proceeded to make a high-resolution subsurface depositional facies map from the reduced three principal components from the original multi-dimensional SSAs.

The research team reported that validation analysis of the 9 blind test wells revealed that the increased heterogeneity association with seismic facies changed and the lack of training wells could lead to false thickness predictions. Moreover, based on a geological model of the P1h8 reservoir, the correlation between spectral attributes and thickness of sands were found.

In summary, the study used mediocre-quality 3D seismic data, upon which they successfully applied a spectral attribute analysis workflow by using the continuous wavelet transform with generalized Morse wavelets and artificial neural network to improve the delineation of sandstones in the Lower Permian Xiashihezi Formation, Ordos Basin, China. Remarkably, the results obtained illustrated significant variation in reservoir thickness across the field, which can be useful for evaluating reservoir heterogeneity and connectivity. In a statement to Advances in Engineering, the authors highlighted that their machine-aided multi-dimensional SSAs analysis could be useful for play screening in the reconnaissance phase, prospect generation and maturation in the exploration phase, and well placement in the development phase.

Machine learning-based seismic spectral attribute analysis to delineate a tight-sand reservoir in the Sulige gas field of central Ordos Basin, western China - Advances in Engineering

About the author

Zhiguo Wang is currently an Associate Professor with the School of the School of Mathematics and Statistics, Xi’an Jiaotong University. He was a Visiting Scholar (2016-2017) with the Electrical and Computer Engineering, Duke University, USA. He was the recipient of SEG/ExxonMobil SEP Travel Award in 2009, SEG/Chevron SLS Travel Award in 2009, SEG Annual Meeting Special Global Technical Session Travel Grant in 2014, and Science and Technological Advancement Award from Ministry of Education in 2016. He is, and has been, principal investigator in several NSFC projects. He serves as a member for SEG Membership Committee, SEG Emerging Professionals International Committee, SEG Travel Grant Committee and SEG EVOLVE Technical Committee. His research is mainly focused on seismic signal analysis, seismic machine learning, seismic geomorphology and seismic interpretation for oil and gas industry. He is an active member of SEG and AAPG.

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About the author

Professor Dengliang Gao received a PhD (1997) in geology and geophysics from Duke University. He is a Professor of Geology and Geophysics at West Virginia University, was an adjunct professor at University of Houston (2007) and a lecturer at Tongji University (1986-1991) (China). Before joining the faculty at West Virginia University in 2009, he worked at Chevron Energy Technology Company (2008-2009), Marathon Oil Company (1998-2007), and Exxon Production Research Company (1997-1998). He was the recipient of two US patents, and was selected to receive the Robert H. Dott Sr. Memorial Award (2015) from AAPG, the DOE/NETL-RUA Outstanding Research Award (2013) from URS, two Grover E. Murray best (second place) published paper awards (2006) from GCAGS, and Science and Technological Advancement Award (1991) from China’s Education Commission.

He was twice recognized as an outstanding GEOPHYSICS associate editor (2007, 2008) and outstanding GEOPHYSICS peer reviewer (2006) by SEG. He served as INTERPRETATION special section editor (2015, 2017, 2018, 2019, 2020), GEOPHYSICS associate editor (2006–2015), AAPG special publication editor (2009–2012), on the AAPG Publications Committee (2006–2009), and as the Co-chair for the 31th Annual GCSSEPM Foundation Bob F. Perkins Research Conference on seismic attributes (2011). His research interests include seismic texture and seismic structure analysis for subsurface characterization.

About the author

Daxing Wang is currently a Professor-level Senior Engineer with the Exploration and Development Research Institute of PetroChina Changqing Oilfield Company, Xi’an, China. He received the B.S. and M.S. degrees in geophysical exploration from the Southwest Petroleum University, Sichuan, China, in 1983 and 1995, respectively, and the Ph.D. degree in solid geophysics from the Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China, in 2005.

His research interests include reservoir characterization and hydrocarbon detection. Dr. Wang was the recipient of the first prize of science and technology award of Shaanxi province in 2017.

About the author

Jinghuai Gao is currently a distinguished professor of flying talent Professor with the School of Electronic and Information Engineering and the School of Mathematics and Statistics, Xi’an Jiaotong University. He received the M.S. degree in applied geophysics from Chang’an University, Xi’an, China, in 1991, and the Ph.D. degree in electromagnetic field and microwave technology from Xi’an Jiaotong University, Xi’an, in 1997.

From 1997 to 2000, he was a Post-Doctoral Researcher with the Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China. In 1999, he was a Visiting Scientist with the Modeling and Imaging Laboratory, University of California at Santa Cruz, USA. He is also an Associate Director with the National Engineering Laboratory for Offshore Oil Exploration, Xi’an Jiaotong University. He is the principal investigator of the fundamental theory and method for geophysical exploration and development of unconventional oil and gas, which is a major program of the National Natural Science Foundation of China under Grant 41390450. His research interests include seismic wave propagation and imaging theory, seismic reservoir and fluid identification, and seismic inverse problem theory and method.

Dr. Gao was the recipient of the first prize of scientific and technological progress, Ministry of Education in 2016, the Chen Zongqi Geophysical Best Paper Award in 2013, and 37 China patents. He has published more than 170 peer reviewed journal papers and chapters. He serves as an associated editor of IEEE Transaction on Geoscience and Remote Sensing, and an editorial board member of the Chinese Journal of Geophysics. He is an active member of IEEE and SEG.

Reference

Zhiguo Wang, Dengliang Gao, Xiaolan Lei, Daxing Wang, Jinghuai Gao. Machine learning-based seismic spectral attribute analysis to delineate a tight-sand reservoir in the Sulige gas field of central Ordos Basin, western China. Marine and Petroleum Geology, volume 113 (2020) 104136.

Go To Marine and Petroleum Geology

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