Characterization of the Neogene Fluvial Reservoirs in the Qinhuangdao (QHD) 32-6 Field: An Integrated Approach Using Seismic Spectral Deconvolution, Sand-Constrained Seismic Forward Modeling, and Densely-Spaced Well Data

Abstract

The Qinhuangdao (QHD) 32-6 oilfield is located in the mid-west of the Shijiutuo uplift at the center of Bozhong sub-basin of the Bohai Bay Basin, northeast China. The main target in the field, the Neogene Minghuazhen Formation, is deposited as large fluvial systems. The study area is located in the north of the QHD32-6 field. After decades of exploration and production drilling the area is densely penetrated (128 wells with an average spacing close to 500m) and has various seismic data coverage, including a high-resolution 3D seismic volume with a center frequency of 55Hz.

The current study attempts to characterize the distribution, geometry, and architecture of the stacked fluvial channelized reservoirs by integrating spectral-decomposed seismic attribute (SDSA) analysis, sand-constrained seismic forward modeling, and superb well data. The high-resolution 3-D seismic data is decomposed into multiple spectral-decomposed volumes with low, medium, and high center frequencies. These volumes are use to describe the distribution, geometries, and aspect ratios of composite sand bodies (stacked channel forms). High-frequency SDSA highlights thin channel sand bodies; whereas, low-frequency SDSA detects thicker channel sand bodies better. To best utilized the decomposed volumes, the high, medium, and low frequency SDSA are also blended using both RGB color blending and Support Vector Machine (SVM) data fusion. Reservoir sand geometries are well characterized, and the correlation coefficient between sand thickness derived from wireline logs and seismic attributes, for instance, is above 0.8. Within the composite sand bodies, smaller-scale channel forms and their bounding surfaces are characterized using sand-constrained seismic forward modeling.

Overall, the integrated approach characterizes the distribution, geometries, and architecture of different hierarchies of reservoirs effectively. Results are, thus, utilized in well planning to optimize production drilling.

AAPG Datapages/Search and Discovery Article #90323 ©2018 AAPG Annual Convention and Exhibition, Salt Lake City, Utah, May 20-23, 2018