Abstract:
The research work was carried out at Sanghar district which is situated at Thar platform,
Southern Indus Basin. The research data comprised of seismic lines and well data in SEGY and
LAS Format respectively. The name of the well is ICHHRI-01 which is located at 63km North of
Bobi field in Khairpur District, Sindh Province. The well was drilled by OGDCL to test
hydrocarbon potential of sands of Lower Goru Formation of cretaceous age. The total depth of
the well was 3300 meters. The well was abandoned.
The main aim of work revolves around Reservoir characterization in which two
approaches of predictive reservoir and realistic reservoir characterization is used. This approach
is helpful to get maximum use of the seismic data with less control of wells in an area. The
methodology adopt for this purpose is to test and use original workflow for seismic structural
and sequence stratigraphic interpretation system(SSIS). The regional seismic lines are used to
quickly build a digital chronostratigraphic framework for the Southern Indus Basin (Lower
Cretaceous). The sequence stratigraphic meaning to depositional sequences are than assigned.
Wheeler diagrams are generated after that which is used in different depositional histories
(depositional sequence geometries and stacking patterns within a common stratigraphic
framework). Realistic reservoir methods including attribute analysis, spectral decomposition,
neural networks and fuzzy logic is worn to minimize the uncertainty which help interpreter to
take full use of seismic and geologic dataset. A Neural network is a non-linear statistical data
modeling tool which is able to model complex relationships between inputs and outputs or to
find patterns in data. An artificial neural network is a computational model based on biological
neural networks. It consists of an interconnected group (network) of artificial neurons(nodes) and
processes information using a connectionist computation approach (interconnected networks of
simple units). Similarly, Fuzzy logic has termed as self-filtering technique using logical
expression. This method is proposed as self-filtering technique in interpretation because it will
filter out the result and would combine the number of results by using logical expression. It is
also dependent upon the knowledge of the interpreter and the way to apply the logic for specific
subsurface interpretation. Thus, it termed as self-filtering technique in interpretation
