Reservoir Characterization of Sawan Field Using 3D Seismic and Well Log Data, Lower Indus Basin, Pakistan

Abstract

Seismic reflection that is an indirect geophysical technique. It is widely used for hydrocarbon prospecting and to characterize physical reservoir parameter. Three dimensional (3D) reservoir modelling, is one of the major tool for the characterization subsurface fluid, lithology and to map the spatial distribution of heterogeneities in reservoir porosities. For the development of reservoir and locating a new drilling prospect, 3D reservoir models are built in by exploration companies. The main aim of this study, is to obtain an improved estimation of reservoir characteristics in Sands of Lower Goru formation of Sawan Gas field, Middle Indus Basin, Pakistan. In order to achieve the target, seismic interpretation, wire-line log analysis, spectral decomposition and seismic inversion algorithms are applied. Sawan field is located in Thar Desert, Sindh with geographical coordinate’s lies at 26°55'34.3452'' to 27°17'14.424'' (North) and 68°32'28.9'' to 69°18' 40.464'' (East). Structural interpretation is carried out through using 3D post stack seismic data of Sawan Gas field. Four horizons have been marked by overlaying the synthetic seismogram on seismic section. Moreover, structural maps such as time and depth map of C-interval Sand have been prepared to understand the subsurface extent and structure of reservoir. Seismic interpretation shows that, Sawan area is effected by extensional regime. Many en-echelon faults oriented from NW to SE direction. These faults discontinue at odds with tertiary unconformity. They make flower-like pattern having minor throw. Seismic attributes extracted at C-Interval Sand that confirmed the structural interpretation and highlighted potential sand bodies for hydrocarbon exploration. Petrophysical analysis that is used for, quantification of important reservoir properties. As petrophysical properties provide information about fluid and lithology of any rock. Petrophysical results confirmed the presence of Gas in C-Interval Sand at Well-01 & 7, with effective porosity ranges from 10-16%, water saturation (Sw) 35-45%, hydrocarbon saturation 65% and volume of shale is 11-17%. Using Sawan well-01 & 07, the reservoir lithology is confirmed through facies analysis i.e. by cross-plotting of different logs such as (Neutron versus Sonic logs) and (Density versus Resistivity Deep laterolog) logs. These are performed acrossreservoir level which shows that main lithology of the reservoir is Sand with interbedded thin Shale sequences. Spectral decomposition is applied within the window of 10-40 Hz that, divides the frequency window into several sub bands such as 11, 13, 16, 19, 23, 28 and 33 Hz. Seismic sections and time slices are prepared along these sub bands and are giving limited subsurface information that is not resolving the main reservoir. At 16 and 19 Hz, we get optimum results and formation at reservoir level but at Sawan well-01, drilled in the area lies in vicinity of high amplitude at dominant frequency band at 19 Hz. At higher frequencies such as, 33 and 40 Hz the reflectors become thinner and continuity of reflectors are not clear at that frequencies. Moreover, in the research mapping, quantification and interpretation of hydrocarbon bearing zones are facilitated by seismic inversion algorithms. Seismic post stack inversions use the physical properties of rock formations and reconstruct the earth model in impedance from that enhance the resolution of seismic data. Two type of inversion method used such as Model based inversion (MBI) and Sparse-spike inversion (SSI) which is to accurately pick low impedance zone with correlation coefficient and error. MBI has C.C of (0.99) with an error of (0.07) at reservoir level. C.C of SSI is (0.99) and error is (0.12). All inversion methods, are resolving the reservoir very well but MBI is better among them in the study area with maximum correlation and minimum error. Porosity of low impedence of C-Interval Sand of Lower Goru formation, is estimated through interpolation technique such as probabilistic neural network (PNN) for spatially distribution. In order to invert porosities over seismic cube inverted impedance is used as an external attribute along with derived porosities at well location.