Geostatistical Prediction of Sand Distribution of Gas Reservoir in Jilin, China

This case study involves sand distribution prediction for the Gujiazhi and Houwujiahu gas fields in Jilin Province, China. Initial data analysis involved correcting and calibrating the well logs. Log data from 74 wells were available from the Gujiazhi field and the Houwujiahu field. This initial process also established 3-D porosity, permeability and clay volume models for each field. The Gujiazhi grid has a cell dimension of 100x100x2 meter, Houwujiahu grid 100x100x1 meter. Geostatistical inversion was conducted for a 3-D seismic volume covering both fields. Synthetic resistivity logs were generated and converted to pseudo-impedance in time, the derived reflectivity was convolved with a wavelet, and the synthetic seismogram correlated with actual seismic traces. This process was allowed to iterate until a satisfactory correlation was reached. One VSP was used for the initial time-depth conversion, and the time-depth relationship for each well was subsequently time-adjusted until the synthetic seismograms matched the actual seismic, allowing a reasonable lateral velocity variation. A common wavelet was extracted for all the wells. With the optimized time-depth control and common wavelet, resistivity inversion was conducted for the 3-D seismic grid. A 3-D reservoir geology model was generated by co-kriging, using the well log data as hard data and the inverted seismic data as soft data. The 3-D reservoir geology model from well logs showed high vertical resolution but poor lateral resolution, while the 3-D seismic-inverted model showed higher lateral resolution but poor vertical resolution. The co-kriging model showed favorable resolution both vertically and laterally. The resulting reservoir geology model provided insights into assessing the distribution of pay sands. Since in this area, resistivity is a good indicator of lithology, the study results demonstrate that geostatistical inversion of seismic data to resistivity is useful in predicting sand distribution, and for optimizing future drilling locations. Introduction The Gujiazhi and Houwujiahu fields are located in Lash County of Jilin Province, China. The Gujiazhi structure is actually part of the broad, gentle Houwujiahu anticline, and are situated in the western end of the Shiou Central structural belt. To date, there are 74 wells in this area, producing 35×10 m per day in Gujiazhi and about 40×10 m per day in Houwujiahu, and supplying gas to cities in the region. The main producing strata are in the fan-delta complexes within the Dengloku formation, where the Chuan-I and Chuan-II members are deltaic depositional sequences. The main pay zones are the delta-front distributary-channel sandstones. The structures in the area are complicated by the many faults, the multiple gas pays are widely dispersed, and individual gas sands are thin. The physical properties of the gas sands vary greatly, while the prominent gas sands have moderate to low permeability, and are tight reservoirs with strong inhomogeneity. Our study involves stratigraphic analysis, sequence identification and correlation, depositional microfacies, detailed 3-D structure interpretation and log interpretation. From this basis, we performed reservoir modeling to describe the reservoirs in detail and the spatial variation of physical properties. The reservoir modeling study was divided into two parts: For the Gujiazhi field, generate 3-D, horizontal and cross-sectional models of porosity, permeability and shale contents for the primary reservoir sands: N-IX, N-X, N-XI, X-I, and X-II, using well log data from 12 wells and core analysis data from 4 wells. For the Houwujiahu field, generate 3-D models of porosity, permeability and shale contents for the Quan-I member, using well log data from 24 wells. This allows better three-dimensional visualization of the reservoirs, and provides enhanced basis for better delineation of the internal distribution of gas pays within the reservoirs section. Reservoir Modeling Of Gujiazhi Field Input data. The input data for the Gujiazhi field are as follows: (1) Data for individual pay zones (Table 1). SPE 84055 Geostatistical Prediction of Sand Distribution of Gas Reservoir in Jilin, China Li-Wei Qiu, SINOPEC, AnPing Yang, Applied Computer Engineering, Sheng-Xiang Long and Zhi-Jiang Kang, SINOPEC Copyright 2003, Society of Petroleum Engineers Inc. This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, U.S.A., 5–8 October 2003. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s).